Advances in Lipidomics for Cancer Biomarkers Discovery. Int J Mol Sci. 2016;17:E1992. [PMID: 27916803 PMCID: PMC5187792 DOI: 10.3390/ijms17121992]

Cross-Validation of Lipid Structure Assignment Using Orthogonal Ion Activation Modalities on the Same Mass Spectrometer

The onset and progression of cancer is associated with changes in the composition of the lipidome. Therefore, better understanding of the molecular mechanisms of these disease states requires detailed structural characterization of the individual lipids within the complex cellular milieu. Recently, changes in the unsaturation profile of membrane lipids have been observed in cancer cells and tissues, but assigning the position(s) of carbon-carbon double bonds in fatty acyl chains carried by membrane phospholipids, including the resolution of lipid regioisomers, has proven analytically challenging. Conventional tandem mass spectrometry approaches based on collision-induced dissociation of ionized glycerophospholipids do not yield spectra that are indicative of the location(s) of carbon-carbon double bonds. Ozone-induced dissociation (OzID) and ultraviolet photodissociation (UVPD) have emerged as alternative ion activation modalities wherein diagnostic product ions can enable de novo assignment of position(s) of unsaturation based on predictable fragmentation behaviors. Here, for the first time, OzID and UVPD (193 nm) mass spectra are acquired on the same mass spectrometer to evaluate the relative performance of the two modalities for lipid identification and to interrogate the respective fragmentation pathways under comparable conditions. Based on investigations of lipid standards, fragmentation rules for each technique are expanded to increase confidence in structural assignments and exclude potential false positives. Parallel application of both methods to unsaturated phosphatidylcholines extracted from isogenic colorectal cancer cell lines provides high confidence in the assignment of multiple double bond isomers in these samples and cross-validates relative changes in isomer abundance.

Plasma lipidomics profiling in predicting the chemo-immunotherapy response in advanced non-small cell lung cancer

Background

Advanced non-small cell lung cancer (NSCLC) presents significant treatment challenges, with chemo-immunotherapy emerging as a promising approach. This study explores the potential of lipidomic biomarkers to predict responses to chemo-immunotherapy in advanced non-small cell lung cancer (NSCLC) patients.

Methods

A prospective analysis was conducted on 68 NSCLC patients undergoing chemo-immunotherapy, divided into disease control (DC) and progressive disease (PD) groups based on treatment response. Pre-treatment serum samples were subjected to lipidomic profiling using liquid chromatography-mass spectrometry (LC-MS). Key predictive lipids (biomarkers) were identified through projection to latent structures discriminant analysis. A biomarker combined model and a clinical combined model were developed to enhance the prediction accuracy. The predictive performances of the clinical combined model in different histological subtypes were also performed.

Results

Six lipids were identified as the key lipids. The expression levels of PC(16:0/18:2), PC(16:0/18:1), PC(16:0/18:0), CE(20:1), and PC(14:0/18:1) were significantly up-regulated. While the expression level of TAG56:7-FA18:2 was significantly down-regulated. The biomarker combined model demonstrated a receiver operating characteristic (ROC) curve of 0.85 (95% CI: 0.75-0.95) in differentiating the PD from the DC. The clinical combined model exhibited an AUC of 0.87 (95% CI: 0.79-0.96) in differentiating the PD from the DC. The clinical combined model demonstrated good discriminability in DC and PD patients in different histological subtypes with the AUC of 0.78 (95% CI: 0.62-0.96), 0.79 (95% CI: 0.64-0.94), and 0.86 (95% CI: 0.52-1.00) in squamous cell carcinoma, large cell carcinoma, and adenocarcinoma subtype, respectively. Pathway analysis revealed the metabolisms of linoleic acid, alpha-linolenic acid, glycerolipid, arachidonic acid, glycerophospholipid, and steroid were implicated in the chemo-immunotherapy response in advanced NSCLC.

Conclusion

Lipidomic profiling presents a highly accurate method for predicting responses to chemo-immunotherapy in patients with advanced NSCLC, offering a potential avenue for personalized treatment strategies.

Multi Omics Applications in Biological Systems

Traditional methodologies often fall short in addressing the complexity of biological systems. In this regard, system biology omics have brought invaluable tools for conducting comprehensive analysis. Current sequencing capabilities have revolutionized genetics and genomics studies, as well as the characterization of transcriptional profiling and dynamics of several species and sample types. Biological systems experience complex biochemical processes involving thousands of molecules. These processes occur at different levels that can be studied using mass spectrometry-based (MS-based) analysis, enabling high-throughput proteomics, glycoproteomics, glycomics, metabolomics, and lipidomics analysis. Here, we present the most up-to-date techniques utilized in the completion of omics analysis. Additionally, we include some interesting examples of the applicability of multi omics to a variety of biological systems.

Metabolomics Reveals Tyrosine Kinase Inhibitor Resistance-Associated Metabolic Events in Human Metastatic Renal Cancer Cells

The development of resistance to tyrosine kinase inhibitors (TKIs) is a major cause of treatment failure in metastatic renal cell carcinoma (mRCC). A deeper understanding of the metabolic mechanisms associated with TKI resistance is critical for refining therapeutic strategies. In this study, we established resistance to sunitinib and pazopanib by exposing a parental Caki-1 cell line to increasing concentrations of sunitinib and pazopanib. The intracellular and extracellular metabolome of sunitinib- and pazopanib-resistant mRCC cells were investigated using a nuclear magnetic resonance (NMR)-based metabolomics approach. Data analysis included multivariate and univariate methods, as well as pathway and network analyses. Distinct metabolic signatures in sunitinib- and pazopanib-resistant RCC cells were found for the first time in this study. A common metabolic reprogramming pattern was observed in amino acid, glycerophospholipid, and nicotinate and nicotinamide metabolism. Sunitinib-resistant cells exhibited marked alterations in metabolites involved in antioxidant defence mechanisms, while pazopanib-resistant cells showed alterations in metabolites associated with energy pathways. Sunitinib-resistant RCC cells demonstrated an increased ability to proliferate, whereas pazopanib-resistant cells appeared to restructure their energy metabolism and undergo alterations in pathways associated with cell death. These findings provide potential targets for novel therapeutic strategies to overcome TKI resistance in mRCC through metabolic regulation.

Identifying novel proteins for migraine by integrating proteomes from blood and CSF with genome-wide association data

BACKGROUND

Proteome-wide Mendelian randomization studies have been increasingly utilized to identify potential drug targets for diseases. We aimed to identify potential therapeutic targets for migraine and its subtypes through the application of Mendelian randomization and co-localization analysis methods.

METHODS

We utilized cis-protein quantitative trait loci data for 1378 plasma proteins available from two studies with 7213 individuals and 35,559 individuals, respectively. Summary data for migraine and its subtypes were obtained from a genetic study involving up to 1,339,303 individuals. Proteins that passed both the discovery and validation Mendelian randomization analysis, sensitivity analysis, heterogeneity test, and pleiotropy test, were associated with ≥2 outcomes, and received strong support from co-localization analysis (PP.H4.abf ≥0.80) and were classified as tier 1 proteins.

RESULTS

We identified three tier 1 proteins (LRP11, ITIH1, and ADGRF5), whose genes have not been previously identified as causal genes for migraine in genetic studies. LRP11 was significantly associated with the risk of any migraine (OR [odds ratio] = 0.968, 95% CI [confidence interval] = 0.955-0.981, p = 1.27 × 10-6) and significantly/suggestively associated with three migraine subtypes. ITIH1 was significantly associated with the risk of any migraine (OR = 1.044, 95% CI = 1.024-1.065, p = 1.08 × 10-5) and migraine with visual disturbances. ADGRF5 was significantly associated with the risk of any migraine (OR = 0.964, 95% CI = 0.946-0.982, p = 8.74 × 10-5) and suggestively associated with migraine with aura. The effects of LRP11 and ADGRF5 were further replicated using cerebrospinal fluid protein data. Apart from ADGRF5, there was no evidence of potential adverse consequences when modulating the plasma levels. We also identified another four proteins (PLCG1, ARHGAP25, CHGA, and MANBA) with no potential adverse consequences when modulating the plasma levels, and their genes were not reported by previous genetic studies.

CONCLUSIONS

We found compelling evidence for two proteins and suggestive evidence for four proteins that could be promising targets for migraine treatment without significant adverse consequences. The corresponding genes were not reported in previous genetic studies. Future studies are needed to confirm the causal role of these proteins and explore the underlying mechanisms.

Multi-OMICS approaches in cancer biology: New era in cancer therapy

Innovative multi-omics frameworks integrate diverse datasets from the same patients to enhance our understanding of the molecular and clinical aspects of cancers. Advanced omics and multi-view clustering algorithms present unprecedented opportunities for classifying cancers into subtypes, refining survival predictions and treatment outcomes, and unravelling key pathophysiological processes across various molecular layers. However, with the increasing availability of cost-effective high-throughput technologies (HTT) that generate vast amounts of data, analyzing single layers often falls short of establishing causal relations. Integrating multi-omics data spanning genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offers unique prospects to comprehend the underlying biology of complex diseases like cancer. This discussion explores algorithmic frameworks designed to uncover cancer subtypes, disease mechanisms, and methods for identifying pivotal genomic alterations. It also underscores the significance of multi-omics in tumor classifications, diagnostics, and prognostications. Despite its unparalleled advantages, the integration of multi-omics data has been slow to find its way into everyday clinics. A major hurdle is the uneven maturity of different omics approaches and the widening gap between the generation of large datasets and the capacity to process this data. Initiatives promoting the standardization of sample processing and analytical pipelines, as well as multidisciplinary training for experts in data analysis and interpretation, are crucial for translating theoretical findings into practical applications.

Is Lipid Metabolism of Value in Cancer Research and Treatment? Part I- Lipid Metabolism in Cancer

For either healthy or diseased organisms, lipids are key components for cellular membranes; they play important roles in numerous cellular processes including cell growth, proliferation, differentiation, energy storage and signaling. Exercise and disease development are examples of cellular environment alterations which produce changes in these networks. There are indications that alterations in lipid metabolism contribute to the development and progression of a variety of cancers. Measuring such alterations and understanding the pathways involved is critical to fully understand cellular metabolism. The demands for this information have led to the emergence of lipidomics, which enables the large-scale study of lipids using mass spectrometry (MS) techniques. Mass spectrometry has been widely used in lipidomics and allows us to analyze detailed lipid profiles of cancers. In this article, we discuss emerging strategies for lipidomics by mass spectrometry; targeted, as opposed to global, lipid analysis provides an exciting new alternative method. Additionally, we provide an introduction to lipidomics, lipid categories and their major biological functions, along with lipidomics studies by mass spectrometry in cancer samples. Further, we summarize the importance of lipid metabolism in oncology and tumor microenvironment, some of the challenges for lipodomics, and the potential for targeted approaches for screening pharmaceutical candidates to improve the therapeutic efficacy of treatment in cancer patients.

Investigation of the Effects of Labware Contamination on Mass Spectrometry-Based Human Serum Lipidome Analysis

Polypropylene microcentrifuge tubes (MCTs) are increasingly used in lipidome sample preparation. In the absence of a comprehensive study evaluating ramifications of plasticware utilization in mass spectrometry-based lipidomic analyses, we conducted a systematic analysis to elucidate potential negative effects ascribable to labware contamination in serum lipidomics. During serum lipid extractions, tested glassware introduced 24 labware contaminants. In contrast, Eppendorf polypropylene MCTs contributed 485 contaminant features, many of which could be erroneously putatively identified as lipids via their m/z values. Eppendorf MCTs contamination engendered severe ion-suppression of 40 low abundance serum lipids, while generating mild to modest lipid ion-suppression across a multitude of higher abundance coeluting lipids. Less compatible polypropylene MCTs from an alternative manufacturer introduced a staggering 2,949 contaminant m/z values, severely affecting 75 coeluting serum lipids and causing more frequent and pronounced ion-suppression instances. Furthermore, by performing serum extractions with varied initial volumes, it was ascertained that labware-induced lipid ion-suppression is a dynamic phenomenon, contingent on both lipid and labware contaminant concentrations where low-abundance lipids are disproportionately impacted by coelutes of suppressive contaminants. In addition to lipid ion-suppression, the identification and quantification of 7 fatty acid endogenous serum lipids were compromised by the leaching of structurally identical surfactants from MCTs. MCTs artificially introduced 10 additional primary amides extraneous to serum samples. Utmost caution is imperative in interpreting data concerning primary amides and fatty acids when employing plastic labware. Through this investigation, we aspire to elevate awareness regarding the pernicious impact of labware contamination on lipidome analysis.

Lipid Identification of Biomarkers in Esophageal Squamous Cell Carcinoma by Lipidomic Analysis

Lipids participate in many important biological functions through energy storage, membrane structure stabilization, signal transduction, and molecular recognition. Previous studies have shown that patients with esophageal squamous cell carcinoma (ESCC) have abnormal lipid metabolism. However, studies characterizing lipid metabolism in ESCC patients through lipidomics are limited. Plasma lipid profiles of 65 ESCC patients and 42 healthy controls (HC) were characterized by lipidomics-based ultraperformance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). Single-factor and multi-factor statistical analysis were used to screen the differences in blood lipids between groups, and combined with component ratio analysis and receiver operating characteristic (ROC) curve diagnostic efficiency assessment, to reveal the potential mechanisms and biomarkers of ESCC. There were significant differences in lipid profiles between the ESCC and HC groups. Thirty-six differential lipids (11 up-regulated and 25 down-regulated) were selected based on the criteria of p < .05 and fold change > 1.3 or < 0.77. Glycerophospholipids were the major differential lipids, suggesting that these lipid metabolic pathways exhibit a significant imbalance that may contribute to the development of esophageal squamous cell carcinoma. Among them, the seven candidate biomarkers for esophageal squamous cell carcinoma with the highest diagnostic value are three phosphatidylserine (PS), three fatty acids (FA) and one phosphatidylcholine (PC).

Cutaneous squamous cell carcinoma characterized by MALDI mass spectrometry imaging in combination with machine learning

Cutaneous squamous cell carcinoma (SCC) is an increasingly prevalent global health concern. Current diagnostic and surgical methods are reliable, but they require considerable resources and do not provide metabolomic insight. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) enables detailed, spatially resolved metabolomic analysis of tissue samples. Integrated with machine learning, MALDI-MSI could yield detailed information pertaining to the metabolic alterations characteristic for SCC. These insights have the potential to enhance SCC diagnosis and therapy, improving patient outcomes while tackling the growing disease burden. This study employs MALDI-MSI data, labelled according to histology, to train a supervised machine learning model (logistic regression) for the recognition and delineation of SCC. The model, based on data acquired from discrete tumor sections (n = 25) from a mouse model of SCC, achieved a predictive accuracy of 92.3% during cross-validation on the labelled data. A pathologist unacquainted with the dataset and tasked with evaluating the predictive power of the model in the unlabelled regions, agreed with the model prediction for over 99% of the tissue areas. These findings highlight the potential value of integrating MALDI-MSI with machine learning to characterize and delineate SCC, suggesting a promising direction for the advancement of mass spectrometry techniques in the clinical diagnosis of SCC and related keratinocyte carcinomas.

Influence of Dietary Polyunsaturated Fatty Acid Intake on Potential Lipid Metabolite Diagnostic Markers in Renal Cell Carcinoma: A Case-Control Study

Non-invasive diagnostics are crucial for the timely detection of renal cell carcinoma (RCC), significantly improving survival rates. Despite advancements, specific lipid markers for RCC remain unidentified. We aimed to discover and validate potent plasma markers and their association with dietary fats. Using lipid metabolite quantification, machine-learning algorithms, and marker validation, we identified RCC diagnostic markers in studies involving 60 RCC and 167 healthy controls (HC), as well as 27 RCC and 74 HC, by analyzing their correlation with dietary fats. RCC was associated with altered metabolism in amino acids, glycerophospholipids, and glutathione. We validated seven markers (l-tryptophan, various lysophosphatidylcholines [LysoPCs], decanoylcarnitine, and l-glutamic acid), achieving a 96.9% AUC, effectively distinguishing RCC from HC. Decreased decanoylcarnitine, due to reduced carnitine palmitoyltransferase 1 (CPT1) activity, was identified as affecting RCC risk. High intake of polyunsaturated fatty acids (PUFAs) was negatively correlated with LysoPC (18:1) and LysoPC (18:2), influencing RCC risk. We validated seven potential markers for RCC diagnosis, highlighting the influence of high PUFA intake on LysoPC levels and its impact on RCC occurrence via CPT1 downregulation. These insights support the efficient and accurate diagnosis of RCC, thereby facilitating risk mitigation and improving patient outcomes.

Identification of Plasma Lipid Alterations Associated with Melanoma Metastasis

The aim of this study was to apply a state-of-the-art quantitative lipidomic profiling platform to uncover lipid alterations predictive of melanoma progression. Our study included 151 melanoma patients; of these, 83 were without metastasis and 68 with metastases. Plasma samples were analyzed using a targeted Lipidyzer™ platform, covering 13 lipid classes and over 1100 lipid species. Following quality control filters, 802 lipid species were included in the subsequent analyses. Total plasma lipid contents were significantly reduced in patients with metastasis. Specifically, levels of two out of the thirteen lipid classes (free fatty acids (FFAs) and lactosylceramides (LCERs)) were significantly decreased in patients with metastasis. Three lipids (CE(12:0), FFA(24:1), and TAG47:2-FA16:1) were identified as more effective predictors of melanoma metastasis than the well-known markers LDH and S100B. Furthermore, the predictive value substantially improved upon combining the lipid markers. We observed an increase in the cumulative levels of five lysophosphatidylcholines (LPC(16:0); LPC(18:0); LPC(18:1); LPC(18:2); LPC(20:4)), each individually associated with an elevated risk of lymph node metastasis but not cutaneous or distant metastasis. Additionally, seventeen lipid molecules were linked to patient survival, four of which (CE(12:0), CE(14:0), CE(15:0), SM(14:0)) overlapped with the lipid panel predicting metastasis. This study represents the first comprehensive investigation of the plasma lipidome of melanoma patients to date. Our findings suggest that plasma lipid profiles may serve as important biomarkers for predicting clinical outcomes of melanoma patients, including the presence of metastasis, and may also serve as indicators of patient survival.

Perspectives on the success of plasma lipidomics in cardiovascular drug discovery and future challenges

INTRODUCTION

Plasma lipidomics has emerged as a powerful tool in cardiovascular drug discovery by providing insights into disease mechanisms, identifying potential biomarkers for diagnosis and prognosis, and discovering novel targets for drug development. Widespread application of plasma lipidomics is hampered by technological limitations and standardization and requires a collaborative approach to maximize its use in cardiovascular drug discovery.

AREAS COVERED

This review provides an overview of the utility of plasma lipidomics in cardiovascular drug discovery and discusses the challenges and future perspectives of this rapidly evolving field. The authors discuss the role of lipidomics in understanding the molecular mechanisms of CVD, identifying novel biomarkers for diagnosis and prognosis, and discovering new therapeutic targets for drug development. Furthermore, they highlight the challenges faced in data analysis, standardization, and integration with other omics approaches and propose future directions for the field.

EXPERT OPINION

Plasma lipidomics holds great promise for improving the diagnosis, treatment, and prevention of CVD. While challenges remain in standardization and technology, ongoing research and collaboration among scientists and clinicians will undoubtedly help overcome these obstacles. As lipidomics evolves, its impact on cardiovascular drug discovery and clinical practice is expected to grow, ultimately benefiting patients and healthcare systems worldwide.

Identification of dysregulation of sphingolipids in retinoblastoma using liquid chromatography-mass spectrometry

Retinoblastoma (RB) is a rare ocular cancer seen in children that counts for approximately 3% of all childhood cancers. It is found that mutation in RB1, a tumour Suppressor Gene on chromosome 13 as the cause of malignancy. Retinoblastoma protein is the target for ceramide to cause apoptosis. We studied lipidomics of two RB cell lines, one aggressive cell line (NCC-RbC-51) derived from a metastatic site and one non aggressive cell line (WERI-Rb1) in comparison with a control cell line (MIO-M1). Lipid profiles of all the cell lines were studied using high resolution mass spectrometer coupled to high performance liquid chromatography. Data acquired from all the three cell lines in positive mode were analyzed to identify differentially expressed metabolites. Several phospholipids and lysophospholipids were found to be dysregulated. We observed upregulation of hexosyl ceramides, and down regulation of dihydroceramides and higher order sphingoglycolipids hinting at a hindered sphingolipid biosynthesis. The results obtained from liquid chromatography-mass spectrometry are validated by using qPCR and it was observed that genes involved in ceramide biosynthesis pathway are getting down regulated.

Non-targeted metabolomics study for discovery of hepatocellular carcinoma serum diagnostic biomarker

Hepatocellular carcinoma (HCC) is one of the most prevalent malignant cancers worldwide. Due to the asymptomatic features of HCC at early stages, patients are often diagnosed at advanced stages and missed effective treatment. Thus, there is an urgent need to identify sensitive and specific biomarkers for HCC early diagnosis. In the present study, an ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) approach was used to profile serum metabolites from HCC patients, liver cirrhosis (LC) patients, and normal controls (NC). Univariate and multivariate statistical analyses were performed to obtain the metabolomic differences of the three groups and select significantly changed metabolites that can be used as diagnostic biomarkers. In total, 757 differential metabolites were quantified among the three groups, and pathway enrichment analysis of these metabolites indicated that glycerophospholipid metabolism, pentose and glucuronate interconversions, phenylalanine, tyrosine and tryptophan biosynthesis, and linoleic acid metabolism were the most altered pathways involved in HCC development. Receiver operating characteristic (ROC) curve analysis was performed to select and evaluate the diagnostic biomarker performance. Seven metabolites were identified as potential biomarkers that can differentiate HCC from LC and NC, and LC from NC with the good diagnostic performance of area under the curve (AUC) from 0.890 to 0.990. In summary, our findings provide highly effective biomarker candidates to differentiate HCC from LC and NC, LC, and NC, which shed insight into HCC pathological mechanisms and will be helpful in better understanding and managing HCC.

Comparative membrane lipidomics of hepatocellular carcinoma cells reveals diacylglycerol and ceramide as key regulators of Wnt/β-catenin signaling and tumor growth

Hepatocellular carcinoma (HCC) is largely associated with aberrant activation of Wnt/β-catenin signaling. Nevertheless, how membrane lipid composition is altered in HCC cells with abnormal Wnt signaling remains elusive. Here, by exploiting comprehensive lipidome profiling, we unravel the membrane lipid composition of six different HCC cell lines with mutations in components of Wnt/β-catenin signaling, leading to differences in their endogenous signaling activity. Among the differentially regulated lipids are diacylglycerol (DAG) and ceramide, which were downregulated at the membrane of HCC cells after Wnt3a treatment. DAG and ceramide enhanced Wnt/β-catenin signaling by inducing caveolin-mediated endocytosis of the canonical Wnt-receptor complex, while their depletion suppressed the signaling activity along with a reduction of caveolin-mediated endocytosis in SNU475 and HepG2 cells. Moreover, depletion of DAG and ceramide significantly impeded the proliferation, tumor growth, and in vivo migration capacity of SNU475 and HepG2 cells. This study, by pioneering plasma membrane lipidome profiling in HCC cells, exhibits the remarkable potential of lipids to correct dysregulated signaling pathways in cancer and stop abnormal tumor growth.

The Contribution of Lipidomics in Ovarian Cancer Management: A Systematic Review

Lipidomics is a comprehensive study of all lipid components in living cells, serum, plasma, or tissues, with the aim of discovering diagnostic, prognostic, and predictive biomarkers for diseases such as malignant tumors. This systematic review evaluates studies, applying lipidomics to the diagnosis, prognosis, prediction, and differentiation of malignant and benign ovarian tumors. A literature search was performed in PubMed, Science Direct, and SciFinder. Only publications written in English after 2012 were included. Relevant citations were identified from the reference lists of primary included studies and were also included in our list. All studies included referred to the application of lipidomics in serum/plasma samples from human cases of OC, some of which also included tumor tissue samples. In some of the included studies, metabolome analysis was also performed, in which other metabolites were identified in addition to lipids. Qualitative data were assessed, and the risk of bias was determined using the ROBINS-I tool. A total of twenty-nine studies were included, fifteen of which applied non-targeted lipidomics, seven applied targeted lipidomics, and seven were reviews relevant to our objectives. Most studies focused on the potential application of lipidomics in the diagnosis of OC and showed that phospholipids and sphingolipids change most significantly during disease development. In conclusion, this systematic review highlights the potential contribution of lipids as biomarkers in OC management.

Plasma lipids are dysregulated preceding diagnosis of preeclampsia or delivery of a growth restricted infant

BACKGROUND

Lipids serve as multifunctional metabolites that have important implications for the pregnant mother and developing fetus. Abnormalities in lipids have emerged as potential risk factors for pregnancy diseases, such as preeclampsia and fetal growth restriction. The aim of this study was to assess the potential of lipid metabolites for detection of late-onset preeclampsia and fetal growth restriction.

METHODS

We used a case-cohort of 144 maternal plasma samples at 36 weeks' gestation from patients before the diagnosis of late-onset preeclampsia (n = 22), delivery of a fetal growth restricted infant (n = 55, defined as <5th birthweight centile), gestation-matched controls (n = 72). We performed liquid chromatography-tandem mass spectrometry (LC-QQQ) -based targeted lipidomics to identify 421 lipids, and fitted logistic regression models for each lipid, correcting for maternal age, BMI, smoking, and gestational diabetes.

FINDINGS

Phosphatidylinositol 32:1 (AUC = 0.81) and cholesterol ester 17:1 (AUC = 0.71) best predicted the risk of developing preeclampsia or delivering a fetal growth restricted infant, respectively. Five times repeated five-fold cross validation demonstrated the lipids alone did not out-perform existing protein biomarkers, soluble tyrosine kinase-1 (sFlt-1) and placental growth factor (PlGF) for the prediction of preeclampsia or fetal growth restriction. However, lipids combined with sFlt-1 and PlGF measurements improved disease prediction.

INTERPRETATION

This study successfully identified 421 lipids in maternal plasma collected at 36 weeks' gestation from participants who later developed preeclampsia or delivered a fetal growth restricted infant. Our results suggest the predictive capacity of lipid measurements for gestational disorders holds the potential to improve non-invasive assessment of maternal and fetal health.

FUNDING

This study was funded by a grant from National Health and Medical Research Council.

Analytical Techniques for Single-Cell Biochemical Assays of Lipids

Lipids are essential cellular components forming membranes, serving as energy reserves, and acting as chemical messengers. Dysfunction in lipid metabolism and signaling is associated with a wide range of diseases including cancer and autoimmunity. Heterogeneity in cell behavior including lipid signaling is increasingly recognized as a driver of disease and drug resistance. This diversity in cellular responses as well as the roles of lipids in health and disease drive the need to quantify lipids within single cells. Single-cell lipid assays are challenging due to the small size of cells (∼1 pL) and the large numbers of lipid species present at concentrations spanning orders of magnitude. A growing number of methodologies enable assay of large numbers of lipid analytes, perform high-resolution spatial measurements, or permit highly sensitive lipid assays in single cells. Covered in this review are mass spectrometry, Raman imaging, and fluorescence-based assays including microscopy and microseparations.

Effect of Trastuzumab on the thermodynamic behavior and roughness of fluid membrane using unsaturated phospholipid/cholesterol mixed monolayer model

The microenvironment near the receptor on biological membrane plays an important role in regulating drug-receptor interaction, and the interaction between drugs and lipids on membrane can also affect the microenvironment of membrane, which may affect drugs' efficacy or cause the drug resistance. Trastuzumab (Tmab) is a monoclonal antibody, used to treat early breast cancer associated with the overexpression of Human Epidermal growth factor Receptor 2 (HER2). But its effectiveness is limited due to its tendency to make tumor cells resistant to the drug. In this work, the monolayer mixed by unsaturated phospholipids (DOPC, DOPE and DOPS) and cholesterol were used as a model to simulate the fluid membrane region on biological membrane. The phospholipid/cholesterol mixed monolayers in molar ratio 7:3 and 1:1, were respectively used to simulate the one layer of simplified normal cell membrane and tumor cell membrane. The influence of this drug on the phase behavior, elastic modulus, intermolecular force, relaxation and the surface roughness of the unsaturated phospholipid/cholesterol monolayer was investigated. The results show that at 30 mN/m the increase or decrease of the elastic modulus and surface roughness of the mixed monolayer caused by Tamb depends on the type of phospholipid, but the intensity of the effect depends on the content of cholesterol, and the intensity of influence is more significant at the presence of 50% cholesterol. However, the effect of Tmab on the ordering of the DOPC/cholesterol or DOPS/cholesterol mixed monolayer is stronger when the content of cholesterol is 30%, but it was stronger for the DOPE/cholesterol mixed monolayer when the content of cholesterol is 50%. This study is helpful to understand the effects of anticancer drugs on microenvironment of cell membrane, and it has a certain reference value for the design of drug delivery system and drug target identification.

Lipidome Analysis of Oropharyngeal Tumor Tissues Using Nanosecond Infrared Laser (NIRL) Tissue Sampling and Subsequent Mass Spectrometry

Ultrashort pulse infrared lasers can simultaneously sample and homogenize biological tissue using desorption by impulsive vibrational excitation (DIVE). With growing attention on alterations in lipid metabolism in malignant disease, mass spectrometry (MS)-based lipidomic analysis has become an emerging topic in cancer research. In this pilot study, we investigated the feasibility of tissue sampling with a nanosecond infrared laser (NIRL) for the subsequent lipidomic analysis of oropharyngeal tissues, and its potential to discriminate oropharyngeal squamous cell carcinoma (OPSCC) from non-tumorous oropharyngeal tissue. Eleven fresh frozen oropharyngeal tissue samples were ablated. The produced aerosols were collected by a glass fiber filter, and the lipidomes were analyzed with mass spectrometry. Data was evaluated by principal component analysis and Welch's t-tests. Lipid profiles comprised 13 lipid classes and up to 755 lipid species. We found significant inter- and intrapatient alterations in lipid profiles for tumor and non-tumor samples (p-value < 0.05, two-fold difference). Thus, NIRL tissue sampling with consecutive MS lipidomic analysis is a feasible and promising approach for the differentiation of OPSCC and non-tumorous oropharyngeal tissue and may provide new insights into lipid composition alterations in OPSCC.

Transcriptome sequencing and metabolite analysis reveal the single and combined effects of microplastics and di-(2-ethylhexyl) phthalate on Peneaus vannamei

Due to the rising usage of plastics, plastic debris are present throughout marine ecosystems and detrimentally affects marine biota. Additionally, plastics likely result in elusive toxicity effects due to addition of plasticizers. The aim of the present study was to reveal the potential effects and mechanism of microplastics (MPs), di-(2-ethylhexyl) phthalate (DEHP) and copollution of MPs and DEHP (MPs-DEHP) on Peneaus vannamei (P. vannamei) juveniles regarding oxidative stress, transcriptomics and metabolomics. MPs, DEHP and MPs-DEHP significantly induced the activities of superoxide dismutase (SOD) and catalase (CAT); MPs and DEHP have an antagonistic effect for malondialdehyde (MDA); suggesting that disorders of the antioxidant defence systems. 13, 133 and 58 differentially expressed genes and 21, 82 and 39 differentially expressed metabolites were responsible for the distinction of MPs, DEHP and MPs-DEHP groups, respectively. The combination of transcriptomic and metabolomic analyses showed that MPs, DEHP and MPs-DEHP exposure disturbed amino acid and lipid metabolism, and further induced inflammatory responses and dysfunction of purine metabolism. Furthermore, the presence of MPs might alleviate the biotoxicity of DEHP in P. vannamei. These findings provide new insights into the single and combined toxicological effects of MPs and additives for marine biota.

Constructing metabolism-protein interaction relationship to identify glioma prognosis using deep learning

Glioma is heterogeneous disease that requires classification into subtypes with similar clinical phenotypes, prognosis or treatment responses. Metabolic-protein interaction (MPI) can provide meaningful insights into cancer heterogeneity. Moreover, the potential of lipids and lactate for identifying prognostic subtypes of glioma remains relatively unexplored. Therefore, we proposed a method to construct an MPI relationship matrix (MPIRM) based on a triple-layer network (Tri-MPN) combined with mRNA expression, and processed the MPIRM by deep learning to identify glioma prognostic subtypes. These Subtypes with significant differences in prognosis were detected in glioma (p-value < 2e-16, 95% CI). These subtypes had a strong correlation in immune infiltration, mutational signatures and pathway signatures. This study demonstrated the effectiveness of node interaction from MPI networks in understanding the heterogeneity of glioma prognosis.

Analytical Toolbox to Unlock the Diversity of Oxidized Lipids

ConspectusLipids are diverse class of small biomolecules represented by a large variety of chemical structures. In addition to the classical biosynthetic routes, lipids can undergo numerous modifications via introduction of small chemical moieties forming hydroxyl, phospho, and nitro derivatives, among others. Such modifications change the physicochemical properties of a parent lipid and usually result in new functionalities either by mediating signaling events or by changing the biophysical properties of lipid membranes. Over the last decades, a large body of evidence indicated the involvement of lipid modifications in a variety of physiological and pathological events. For instance, lipid (per)oxidation for a long time was considered as a hallmark of oxidative stress and related proinflammatory signaling. Recently, however, with the burst in the development of the redox biology field, oxidative modifications of lipids are also recognized as a part of regulatory and adaptive events that are highly specific for particular cell types, tissues, and conditions.The initial diversity of lipid species and the variety of possible lipid modifications result in an extremely large chemical space of the epilipidome, the subset of the natural lipidome formed by enzymatic and non-enzymatic lipid modifications occurring in biological systems. Together with their low natural abundance, structural annotation of modified lipids represents a major analytical challenge limiting the discovery of their natural variety and functions. Furthermore, the number of available chemically characterized standards representing various modified lipid species remains limited, making analytical and functional studies very challenging. Over the past decade we have developed and implemented numerous analytical methods to study lipid modifications and applied them in the context of different biological conditions. In this Account, we outline the development and evolution of modern mass-spectrometry-based techniques for the structural elucidation of modified/oxidized lipids and corresponding applications. Research of our group is mostly focused on redox biology, and thus, our primary interest was always the analysis of lipid modifications introduced by redox disbalance, including lipid peroxidation (LPO), oxygenation, nitration, and glycation. To this end, we developed an array of analytical solutions to measure carbonyls derived from LPO, oxidized and nitrated fatty acid derivatives, and oxidized and glycated complex lipids. We will briefly describe the main analytical challenges along with corresponding solutions developed by our group toward deciphering the complexity of natural epilipdomes, starting from in vitro-oxidized lipid mixtures, artificial membranes, and lipid droplets, to illustrate the diversity of lipid modifications in the context of metabolic diseases and ferroptotic cell death.

An integrative pan-cancer analysis illustrating the key role of LRP11 in cervical cancer

BACKGROUND

Low density lipoprotein receptor-related protein 11 (LRP11) was involved in the progression of several tumors. However, its role in cervical cancer still remains uncertain.

METHODS

The original tumor data was downloaded from the Cancer Genome Atlas and genotype-tissue expression databases. The expression of LRP11 in normal tissues, tumor tissues and adjacent tissues were evaluated. In addition, we also explored the genetic alteration, prognostic value, and gene function of LRP11. We deeply assessed the interaction between LRP11 and tumor immunity at the pan-cancer level. Finally, research on the association between LRP11 and the resistance of anti-tumor drugs was carried out.

RESULTS

LRP11 was highly expressed and played a risk prognostic factor in cervical cancer and a variety of tumors. Enrichment analysis revealed that LRP11 was involved in multiple tumor malignant pathways. Our research also pointed out the unique role between LRP11 and tumor immune microenvironment. The tumor immune microenvironment of patients with high expression of LRP11 are lack of most immune cells, indicating a immune desert tumor microenvironment. The final drug resistant analysis suggested that patients with high expression of LRP11 may be related to the resistance of many anti-tumor drugs.

CONCLUSION

LRP11 was a potential oncogene and prognostic marker in cervical cancer and pan-cancer. Patients with high LRP11 expression may have immune desert tumor microenvironment.

PFAS-induced lipidomic dysregulations and their associations with developmental toxicity in zebrafish embryos

Per- and polyfluoroalkyl substances (PFASs) are persistent environmental contaminants, posing developmental toxicity to fish and human. PFAS-induced lipid metabolism disorders were demonstrated using the zebrafish (Danio rerio) embryo model, but the detailed changes of lipid compositions and the influence of these changes on the biological development are still unclear. Herein, lipidomics analysis was performed to reveal the dysregulations of lipid metabolism in zebrafish embryos exposed to perfluorooctanoic acid (PFOA) or perfluorooctane sulfonate (PFOS) through microinjection. Various abnormal phenotypes were observed, including heart bleeding, pericardium edema, spinal curvature and increased heart rate at 72 h after fertilization, especially in the PFOS exposure groups. Lipidomic profiling found downregulated phosphatidylethanolamines in the PFAS-exposed embryos, especially those containing a docosahexaenoyl (DHA) chain, indicating an excessive oxidative damage to the embryos. Glycerolipids were mainly upregulated in the PFOA groups but downregulated in the PFOS groups. These aberrations may reflect oxidative stress, energy metabolism malfunction and proinflammatory signals induced by PFASs. However, supplement of DHA may not be effective in recovering the lipidomic dysregulations and protecting from the developmental toxicity induced by PFASs, showing the complexity of the toxicological mechanisms. This work has revealed the associations between the abnormal phenotypes and dysregulations of lipid metabolism in zebrafish embryos induced by PFASs from the aspect of lipidomics, and discovered the underlying molecular mechanisms of the developmental toxicity of PFASs.

Serum lipidomic profiling by UHPLC-MS/MS may be able to detect early-stage endometrial cancer

Nowadays, screening for endometrial cancer (EC) primarily relies on clinical symptoms and imaging, which makes it difficult to detect early-stage disease. Here, we conducted a widely targeted lipidomic analysis of 38 human serum samples in a discovery set and 40 human serum samples in a validation set to profile the dysregulated lipid species and establish lipid biomarkers for early-stage EC. This comprehensive lipidomic determination of 616 serum lipids indicated significant differences between early-stage EC patients and healthy controls. Three phases of lipid biomarker investigation (discovery, validation, and determination of the lipid biomarker panel) were performed, which revealed the upregulation of some sphingolipid, glycerophospholipid, and glycerolipids and downregulation of some carnitine. Consistently, the perturbation of sphingolipid and glycerophospholipid metabolism was also observed from pathway enrichment analysis. Moreover, a lipid biomarker panel, including ursodeoxycholic acid, PC(O-14:0_20:4), and Cer(d18:1/18:0), was established. This panel was assessed as an effective diagnostic model to distinguish early-stage EC patients from healthy controls and atypical endometrial hyperplasia patients within the area under the receiver operating characteristic curve (AUC) reaching 0.903 and 0.928, respectively. In particular, the comparison results of the diagnostic efficacy indicated that the lipid biomarker panel was superior to clinically established indicators for EC diagnosis, including HE4, CA125, CA153, and CA199, suggesting that it could be used as an excellent supplementary method for the diagnosis of early-stage EC. In conclusion, we established a novel and non-invasive lipid biomarker for early-stage EC detection and these findings may provide new insight into the pathological mechanisms of EC.

Lipid network and moiety analysis for revealing enzymatic dysregulation and mechanistic alterations from lipidomics data

Lipidomics is of growing importance for clinical and biomedical research due to many associations between lipid metabolism and diseases. The discovery of these associations is facilitated by improved lipid identification and quantification. Sophisticated computational methods are advantageous for interpreting such large-scale data for understanding metabolic processes and their underlying (patho)mechanisms. To generate hypothesis about these mechanisms, the combination of metabolic networks and graph algorithms is a powerful option to pinpoint molecular disease drivers and their interactions. Here we present lipid network explorer (LINEX$^2$), a lipid network analysis framework that fuels biological interpretation of alterations in lipid compositions. By integrating lipid-metabolic reactions from public databases, we generate dataset-specific lipid interaction networks. To aid interpretation of these networks, we present an enrichment graph algorithm that infers changes in enzymatic activity in the context of their multispecificity from lipidomics data. Our inference method successfully recovered the MBOAT7 enzyme from knock-out data. Furthermore, we mechanistically interpret lipidomic alterations of adipocytes in obesity by leveraging network enrichment and lipid moieties. We address the general lack of lipidomics data mining options to elucidate potential disease mechanisms and make lipidomics more clinically relevant.

Correlations between serum lipid and Ki-67 levels in different breast cancer molecular subcategories

Breast cancer has the highest incidence rate among all cancer types worldwide, seriously threatening women's health. The present retrospective study explored differences in serum lipid contents in different breast cancer (BC) subcategories and their correlation with Ki-67 expression levels in patients with invasive BC with the aim of identifying novel diagnostic and prognostic indicators for personalized BC treatment. The study included 170 patients diagnosed with BC who were diagnosed with invasive BC by postoperative pathological examination. Data on patient age, body mass index and menopausal status were collected, in addition to estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2 (HER2) and antigen Ki-67 expression levels and pathological tumor type. Preoperative circulating lipid levels, specifically the levels of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG) and apolipoproteins A1 (ApoA1) and B (ApoB) were also obtained. Molecular subcategories of BC were grouped based on their immunohistochemistry. Differences in serum lipid levels between the groups were assessed, and correlations between serum lipid and Ki-67 expression levels were explored. While TC, LDL-C, HDL-C and ApoA1 levels differed significantly among molecular subcategories. TG and ApoB levels did not. Circulating TC and LDL-C levels were considerably higher in patients with triple-negative BC (TNBC) and HER2-positive [hormone receptor (HR)-negative] BC than in those with luminal A and B (HER2-negative) BC. Serum HDL-C levels were significantly diminished in the TNBC and HER2-positive (HR-negative) groups compared with the luminal A and B (HER2-negative) groups. ApoA1 levels were significantly reduced in cases of TNBC and HER2-positive (HR-negative) BC compared with luminal A and B BC. Ki-67 expression levels were positively correlated with circulating TC and LDL-C levels and inversely correlated with circulating HDL-C and ApoA1 levels but exhibited no correlation with serum ApoB and TG levels. The results indicate that elevated TC and LDL-C levels and diminished HDL-C and ApoA1 levels were high-risk factors in patients with TNBC and HER2-positive (HR-negative) BC, but not patients with luminal subcategories of BC. Abnormal serum lipid levels were correlated with Ki-67 expression levels, with elevated circulating TC and LDL-C levels and reduced circulating HDL-C and ApoA1 levels indicating a poor prognosis in patients with BC.

A software-assisted untargeted liquid chromatography-mass spectrometry method for lipidomic profiling of human plasma samples

INTRODUCTION

In this paper, an analytical pipeline designed for untargeted lipidomic profiling in human plasma is proposed. The analytical pipeline was developed for case-control studies nested in prospective cohorts.

METHODS

The procedure consisted of isopropanol protein precipitation followed by reverse phase liquid chromatography coupled to high resolution mass spectrometry and software-assisted data processing. The compounds are putatively annotated by matching experimental mass spectrometry data with spectral library data using LipidSearch software. The lipid profile of a pool of plasma samples from 10 healthy volunteers was detected in both positive and negative polarity modes. The impact of the chosen polarity on the number and quality of the lipid identification has been evaluated.

RESULTS

More than 1000 lipids from 12 different classes were detected, 1150 in positive mode and 273 in negative mode. Nearly half of them were unambiguously identified by the software in positive mode, and about one-third in negative mode. The method repeatability was assessed on the plasma pool samples by means of variance components analysis. The intra- and inter-assay precision was measured for 10 lipids chosen among the most abundant found within the different lipid classes. The intra-assay coefficients of variation ranged from 2.56% to 4.56% while intra- and inter-day coefficients of variance never exceeded the 15% benchmark adopted. The lipidomic profiles of the 10 healthy volunteers were also investigated.

DISCUSSION

This method detects a wide range of lipids and reports their degree of identification. It is particularly fit and well-designed for large case-control epidemiologic studies.

Study on potential markers for diagnosis of renal cell carcinoma by serum untargeted metabolomics based on UPLC-MS/MS

Objective: Renal cell carcinoma (RCC) is the most common malignancy of the kidney. However, there is no reliable biomarker with high sensitivity and specificity for diagnosis and differential diagnosis. This study aims to analyze serum metabolite profile of patients with RCC and screen for potential diagnostic biomarkers. Methods: Forty-five healthy controls (HC), 40 patients with benign kidney tumor (BKT) and 46 patients with RCC were enrolled in this study. Serum metabolites were detected by ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), and then subjected to multivariate statistical analysis, metabolic pathway analysis and diagnostic performance evaluation. Results: The changes of glycerophospholipid metabolism, phosphatidylinositol signaling system, glycerolipid metabolism, d-glutamine and d-glutamate metabolism, galactose metabolism, and folate biosynthesis were observed in RCC group. Two hundred and forty differential metabolites were screened between RCC and HC groups, and 64 differential metabolites were screened between RCC and BKT groups. Among them, 4 differential metabolites, including 3-β-D-Galactosyl-sn-glycerol, 7,8-Dihydroneopterin, lysophosphatidylcholine (LPC) 19:2, and γ-Aminobutyryl-lysine (an amino acid metabolite), were of high clinical value not only in the diagnosis of RCC (RCC group vs. HC group; AUC = 0.990, 0.916, 0.909, and 0.962; Sensitivity = 97.73%, 97.73%, 93.18%, and 86.36%; Specificity = 100.00%, 73.33%, 80.00%, and 95.56%), but also in the differential diagnosis of benign and malignant kidney tumors (RCC group vs. BKT group; AUC = 0.989, 0.941, 0.845 and 0.981; Sensitivity = 93.33%, 93.33%, 77.27% and 93.33%; Specificity = 100.00%, 84.21%, 78.38% and 92.11%). Conclusion: The occurrence of RCC may involve changes in multiple metabolic pathways. The 3-β-D-Galactosyl-sn-glycerol, 7,8-Dihydroneopterin, LPC 19:2 and γ-Aminobutyryl-lysine may be potential biomarkers for the diagnosis or differential diagnosis of RCC.

High Throughput LC-MS Platform for Large Scale Screening of Bioactive Polar Lipids in Human Plasma and Serum

Lipids play a key role in many biological processes, and their accurate measurement is critical to unraveling the biology of diseases and human health. A high throughput HILIC-based (LC-MS) method for the semiquantitative screening of over 2000 lipids, based on over 4000 MRM transitions, was devised to produce an accessible and robust lipidomic screen for phospholipids in human plasma/serum. This methodology integrates many of the advantages of global lipid analysis with those of targeted approaches. Having used the method as an initial "wide class" screen, it can then be easily adapted for a more targeted analysis and quantification of key, dysregulated lipids. Robustness was assessed using 1550 continuous injections of plasma extracts onto a single column and via the evaluation of columns from 5 different batches of stationary phase. Initial screens in positive (239 lipids, 431 MRM transitions) and negative electrospray ionization (ESI) mode (232 lipids, 446 MRM transitions) were assessed for reproducibility, sensitivity, and dynamic range using analysis times of 8 min. The total number of lipids monitored using these screening methods was 433 with an overlap of 38 lipids in both modes. A polarity switching method for accurate quantification, using the same LC conditions, was assessed for intra- and interday reproducibility, accuracy, dynamic range, stability, carryover, dilution integrity, and matrix interferences and found to be acceptable. This polarity switching method was then applied to lipids important in the stratification of human prostate cancer samples.

The important role of glycerophospholipid metabolism in the protective effects of polyphenol-enriched Tartary buckwheat extract against alcoholic liver disease

Alcoholic liver disease (ALD) is a mounting public health problem with significant medical, economic and social burdens. Tartary buckwheat (F. tataricum (L.) Gaertn, bitter buckwheat) is a kind of healthy and nutritious food, which has been demonstrated to protect against ALD, but the underlying mechanism has not been fully studied. Herein, we aimed to elucidate the beneficial effects of Tartary buckwheat extract (mainly composed of polyphenols including rutin, quercetin, kaempferol and kaempferol-3-O-rutinoside) in terms of lipid metabolism with the aid of lipidomic analysis. In our study, we employed C57BL/6J mice and a Lieber-DeCarli alcohol liquid diet to construct an ALD model and found that Tartary buckwheat extract was able to prevent ALD-induced histopathological lesions, liver injury and abnormal plasma lipid levels. These beneficial effects might be attributed to the regulation of energy metabolism-related genes (SIRT1, LKB1 and AMPK), lipid synthesis-related genes (ACC, SREBP1c and HMGR) and lipid oxidation-related genes (PPARα, CPT1 and CPT2). In addition, lipidomic profiling and KEGG pathway analysis showed that glycerophospholipid metabolism contributed the most to elucidating the regulatory mechanism of Tartary buckwheat extract. In specific, chronic ethanol intake reduced the level of phosphatidylcholines (PC) and increased the level of phosphatidylethanolamines (PE) in the liver, resulting in a decrease in the PC/PE ratio, which could be all significantly restored by Tartary buckwheat extract intervention, indicating that the Tartary buckwheat extract might regulate PC/PE homeostasis to exert its lipid-lowering effect. Overall, we demonstrated that Tartary buckwheat extract could prevent ALD by modulating hepatic glycerophospholipid metabolism, providing the theoretical basis for its further exploitation as a medical plant or nutritional food.

Potential Biomarkers and Signaling Pathways Associated with the Pathogenesis of Primary Ameloblastoma: A Systems Biology Approach

Objective

Ameloblastoma is a benign odontogenic tumor that may lead to ameloblastic carcinoma. This study aimed to determine potential signaling pathways and biological processes, critical genes and their regulating transcription factors (TFs), and miRNAs, as well as protein kinases involved in the etiology of primary ameloblastoma.

Methods

The dataset GSE132472 was obtained from the GEO database, and multivariate statistical analyses were applied to identify differentially expressed genes (DEGs) in primary ameloblastoma tissues compared to the corresponding normal gingiva samples. A protein-protein interaction (PPI) map was built using the STRING database. The Cytoscape software identified significant modules and the hub genes within the PPI network. Gene Ontology annotation and signaling pathway analyses were executed by employing the DAVID and Reactome databases, respectively. Significant TFs and miRNAs acting on the hub genes were identified using the iRegulon plugin and MiRWalk 2.0 database, respectively. A protein kinase enrichment analysis was conducted using the online Kinase Enrichment Analysis 2 (KEA2) web server. The approved drugs acting on the hub genes were also found.

Results

A total of 1,629 genes were differentially expressed in primary ameloblastoma (P value <0.01 and |Log2FC| > 1). HRAS, CDK1, MAPK3, ERBB2, COL1A1, CYCS, and BRCA1 demonstrated high degree and betweenness centralities in the PPI network. E2F4 was the most significant TF acting on the hub genes. BTK was the protein kinase significantly enriched by the TFs. Cholesterol biosynthesis was considerably involved in primary ameloblastoma.

Conclusions

This study provides an intuition into the potential mechanisms involved in the etiology of ameloblastoma.

An acquired phosphatidylinositol 4-phosphate transport initiates T-cell deterioration and leukemogenesis

Lipid remodeling is crucial for malignant cell transformation and tumorigenesis, but the precise molecular processes involved and direct evidences for these in vivo remain elusive. Here, we report that oxysterol-binding protein (OSBP)-related protein 4 L (ORP4L) is expressed in adult T-cell leukemia (ATL) cells but not normal T-cells. In ORP4L knock-in T-cells, ORP4L dimerizes with OSBP to control the shuttling of OSBP between the Golgi apparatus and the plasma membrane (PM) as an exchanger of phosphatidylinositol 4-phosphate [PI(4)P]/cholesterol. The PI(4)P arriving at the PM via this transport machinery replenishes phosphatidylinositol 4,5-bisphosphate [PI(4,5)P₂] and phosphatidylinositol (3,4,5) trisphosphate [PI(3,4,5)P₃] biosynthesis, thus contributing to PI3K/AKT hyperactivation and T-cell deterioration in vitro and in vivo. Disruption of ORP4L and OSBP dimerization disables PI(4)P transport and T-cell leukemogenesis. In summary, we identify a non-vesicular lipid transport machinery between Golgi and PM maintaining the oncogenic signaling competence initiating T-cell deterioration and leukemogenesis.

The oxysterol-binding protein-related protein 4 (ORP4L) is expressed in T-cell acute lymphoblastic leukemia and is required for leukemogenesis. Here the authors show that ORP4L orchestrates the transport of the phospholipid PI(4)P from Golgi to the plasma membrane, contributing to PI3K/AKT hyperactivation and T-cell leukemogenesis.

Reprogrammed Lipid Metabolism and the Lipid-Associated Hallmarks of Colorectal Cancer

Simple Summary

Colorectal cancer (CRC) is the third-most diagnosed cancer and the second-leading cause of cancer-related deaths worldwide. Limitations in early and accurate diagnosis of CRC gives rise to poor patient survival. Advancements in analytical techniques have improved our understanding of the cellular and metabolic changes occurring in CRC and potentiate avenues for improved diagnostic and therapeutic strategies. Lipids are metabolites with important biological functions; however, their role in CRC is poorly understood. Here, we provide an in-depth review of the recent literature concerning lipid alterations in CRC and propose eight lipid metabolism-associated hallmarks of CRC.

Abstract

Lipids have diverse structures, with multifarious regulatory functions in membrane homeostasis and bioenergetic metabolism, in mediating functional protein–lipid and protein–protein interactions, as in cell signalling and proliferation. An increasing body of evidence supports the notion that aberrant lipid metabolism involving remodelling of cellular membrane structure and changes in energy homeostasis and signalling within cancer-associated pathways play a pivotal role in the onset, progression, and maintenance of colorectal cancer (CRC) and their tumorigenic properties. Recent advances in analytical lipidome analysis technologies have enabled the comprehensive identification and structural characterization of lipids and, consequently, our understanding of the role they play in tumour progression. However, despite progress in our understanding of cancer cell metabolism and lipidomics, the key lipid-associated changes in CRC have yet not been explicitly associated with the well-established ‘hallmarks of cancer’ defined by Hanahan and Weinberg. In this review, we summarize recent findings that highlight the role of reprogrammed lipid metabolism in CRC and use this growing body of evidence to propose eight lipid metabolism-associated hallmarks of colorectal cancer, and to emphasize their importance and linkages to the established cancer hallmarks.

Surface cholesterol-enriched domains specifically promote invasion of breast cancer cell lines by controlling invadopodia and extracellular matrix degradation

Tumor cells exhibit altered cholesterol content. However, cholesterol structural subcellular distribution and implication in cancer cell invasion are poorly understood mainly due to difficulties to investigate cholesterol both quantitatively and qualitatively and to compare isogenic cell models. Here, using the MCF10A cell line series (non-tumorigenic MCF10A, pre-malignant MCF10AT and malignant MCF10CAIa cells) as a model of breast cancer progression and the highly invasive MDA-MB-231 cell line which exhibits the common TP53 mutation, we investigated if cholesterol contributes to cancer cell invasion, whether the effects are specific to cancer cells and the underlying mechanism. We found that partial membrane cholesterol depletion specifically and reversibly decreased invasion of the malignant cell lines. Those cells exhibited dorsal surface cholesterol-enriched submicrometric domains and narrow ER-plasma membrane and ER-intracellular organelles contact sites. Dorsal cholesterol-enriched domains can be endocytosed and reach the cell ventral face where they were involved in invadopodia formation and extracellular matrix degradation. In contrast, non-malignant cells showed low cell invasion, low surface cholesterol exposure and cholesterol-dependent focal adhesions. The differential cholesterol distribution and role in breast cancer cell invasion provide new clues for the understanding of the molecular events underlying cellular mechanisms in breast cancer.

Lipidomic biomarkers: Potential mediators of associations between urinary bisphenol A exposure and colorectal cancer

Previous research reported associations between bisphenol A (BPA) exposure and some malignant tumor incidences, yet the underlying mechanism remains largely uncertain. This investigation was aimed to explore the association of BPA exposure burden with colorectal cancer (CRC) and the role of tumor tissue lipid metabolism in the associations between BPA and CRC using lipidomic approach. Urinary BPA levels in CRC cases were significantly higher than those in controls (P value < 0.05). BPA was positively correlated with all three serum CRC biomarkers, with an estimated odds ratio (OR) of 4.45 (95% confidence interval (95% CI): [1.31, 15.14]) between the highest and lowest tertiles of exposure. Lipidomic screening of tumor samples suggested significant perturbation in the glycerophospholipid metabolism pathway, of which phosphatidylcholine (PC 34:0), phosphatidylcholine (PC 37:1), phosphatidylethanolamine (PE 34:2), triacylglycerol (TG 56:4) demonstrated mediation contribution of 21.9%, 18.7%, 18.4% and 27.39%, respectively, in the association between BPA exposure and CRC. Our work provides novel findings that cancer tissue metabolites may be playing vital mediating roles in the associations between BPA exposure burden and CRC risk. These findings contribute to better understanding of the etiology of CRC induced by environmental stressors.

The Clinical Value of Lipid Abnormalities in Early Stage Cervical Cancer

Background

To describe the characteristics of plasma lipid proliferation in cervical cancer and further evaluate the prognostic significance of lipid levels in cervical cancer.

Methods

We retrospectively reviewed 1713 patients with cervical cancer in our hospital. The preoperative plasma lipid profile, including cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL), and low-density lipoprotein cholesterol (LDL), of 1713 cervical cancer patients was compared with that of 10,397 healthy women. Then, we evaluated the impact of lipids on overall survival (OS) and recurrence-free survival (RFS) in cervical cancer using univariate and multivariate Cox models.

Results

While plasma TC, TG, and LDL were significantly higher, HDL was lower in patients with cervical cancer than in healthy women. TG was identified as an independent predictor for RFS and OS among patients with cervical cancer. Further stratified by age, patients with higher TGs showed a significantly worse RFS and OS than those with lower TGs among patients ≥50 years old but not among those <50 years old.

Conclusion

Cervical cancer was associated with a disordered lipid profile. Hypertriglyceridemia was an independent poor prognostic indicator for cervical cancer, especially for elderly patients. Strengthening lipid management may be beneficial for improving postoperative OS and RFS in patients with cervical cancer.

Unravelling Prostate Cancer Heterogeneity Using Spatial Approaches to Lipidomics and Transcriptomics

Simple Summary

Prostate cancer is a heterogenous disease in terms of disease aggressiveness and therapy response, leading to dilemmas in treatment decisions. This heterogeneity reflects the multifocal nature of prostate cancer and its diversity in cellular and molecular composition, necessitating spatial molecular approaches. Here in view of the emerging importance of rewired lipid metabolism as a source of biomarkers and therapeutic targets for prostate cancer, we highlight recent advancements in technologies that enable the spatial mapping of lipids and related metabolic pathways associated with prostate cancer development and progression. We also evaluate their potential for future implementation in treatment decision-making in the clinical management of prostate cancer.

Abstract

Due to advances in the detection and management of prostate cancer over the past 20 years, most cases of localised disease are now potentially curable by surgery or radiotherapy, or amenable to active surveillance without treatment. However, this has given rise to a new dilemma for disease management; the inability to distinguish indolent from lethal, aggressive forms of prostate cancer, leading to substantial overtreatment of some patients and delayed intervention for others. Driving this uncertainty is the critical deficit of novel targets for systemic therapy and of validated biomarkers that can inform treatment decision-making and to select and monitor therapy. In part, this lack of progress reflects the inherent challenge of undertaking target and biomarker discovery in clinical prostate tumours, which are cellularly heterogeneous and multifocal, necessitating the use of spatial analytical approaches. In this review, the principles of mass spectrometry-based lipid imaging and complementary gene-based spatial omics technologies, their application to prostate cancer and recent advancements in these technologies are considered. We put in perspective studies that describe spatially-resolved lipid maps and metabolic genes that are associated with prostate tumours compared to benign tissue and increased risk of disease progression, with the aim of evaluating the future implementation of spatial lipidomics and complementary transcriptomics for prognostication, target identification and treatment decision-making for prostate cancer.

Lipid Composition of the Cell Membrane Outer Leaflet Regulates Endocytosis of Nanomaterials through Alterations in Scavenger Receptor Activity

Understanding the principles that guide the uptake of engineered nanomaterials (ENMs) by cells is of interest in biomedical and occupational health research. While evidence has started to accumulate on the role of membrane proteins in ENM uptake, the role of membrane lipid chemistry in regulating ENM endocytosis has remained largely unexplored. Here, we have addressed this issue by altering the plasma membrane lipid composition directly in live cells using a methyl-α-cyclodextrin (MαCD)-catalyzed lipid exchange method. Our observations, in an alveolar epithelial cell line and using silica nanoparticles, reveal that the lipid composition of the plasma membrane outer leaflet plays a significant role in ENM endocytosis and the intracellular fate of ENMs, by affecting nonspecific ENM diffusion into the cell, changing membrane fluidity, and altering the activity of scavenger receptors (SRs) involved in active endocytosis. These results have implications for understanding ENM uptake in different subsets of cells, depending on cell membrane lipid composition.

It Takes More than Two to Tango: Complex, Hierarchal, and Membrane-Modulated Interactions in the Regulation of Receptor Tyrosine Kinases

The search for an understanding of how cell fate and motility are regulated is not a purely scientific undertaking, but it can also lead to rationally designed therapies against cancer. The discovery of tyrosine kinases about half a century ago, the subsequent characterization of certain transmembrane receptors harboring tyrosine kinase activity, and their connection to the development of human cancer ushered in a new age with the hope of finding a treatment for malignant diseases in the foreseeable future. However, painstaking efforts were required to uncover the principles of how these receptors with intrinsic tyrosine kinase activity are regulated. Developments in molecular and structural biology and biophysical approaches paved the way towards better understanding of these pathways. Discoveries in the past twenty years first resulted in the formulation of textbook dogmas, such as dimerization-driven receptor association, which were followed by fine-tuning the model. In this review, the role of molecular interactions taking place during the activation of receptor tyrosine kinases, with special attention to the epidermal growth factor receptor family, will be discussed. The fact that these receptors are anchored in the membrane provides ample opportunities for modulatory lipid-protein interactions that will be considered in detail in the second part of the manuscript. Although qualitative and quantitative alterations in lipids in cancer are not sufficient in their own right to drive the malignant transformation, they both contribute to tumor formation and also provide ways to treat cancer. The review will be concluded with a summary of these medical aspects of lipid-protein interactions.

Metabolic and lipidomic characterization of radioresistant MDA-MB-231 human breast cancer cells to investigate potential therapeutic targets

To provide preliminary insights into metabolic and lipidomic characteristics in radioresistant triple-negative breast cancer (TNBC) cells and suggest potential therapeutic targets, we performed a comprehensive metabolic and lipidomic profiling of radioresistant MDA-MB-231 (MDA-MB-231/RR) TNBC cells and their parental cells using gas chromatography-mass spectrometry and nano electrospray ionization-mass spectrometry, followed by multivariate statistical analysis. Buthionine sulfoximine (BSO) and radiation were co-treated to radioresistant TNBC cells. The level of glutathione (GSH) was significantly increased, and the levels of GSH synthesis-related metabolites, such as cysteine, glycine, and glutamine were also increased in MDA-MB-231/RR cells. In contrast, the level of lactic acid was significantly reduced. In addition, reactive oxygen species (ROS) level was decreased in MDA-MB-231/RR cells. In the lipidomic profiles of MDA-MB-231/RR cells, the levels of phosphatidylcholine (PC) and phosphatidylethanolamine (PE) were significantly increased, whereas those of most of the phosphatidylinositol species were significantly decreased. BSO sensitized MDA-MB-231/RR cells to radiotherapy, which resulted in decreased GSH level and increased ROS level and apoptosis. Radioresistant TNBC cells showed distinct metabolic and lipidomic characteristics compared to their parental cells. We suggested activated GSH, PC, and PE biosynthesis pathways as potential targets for treating radioresistant TNBC cells. Particularly, enhanced radiosensitivity was achieved by inhibition of GSH biosynthesis in MDA-MB-231/RR cells.

Applications of spatially resolved omics in the field of endocrine tumors

Endocrine tumors derive from endocrine cells with high heterogeneity in function, structure and embryology, and are characteristic of a marked diversity and tissue heterogeneity. There are still challenges in analyzing the molecular alternations within the heterogeneous microenvironment for endocrine tumors. Recently, several proteomic, lipidomic and metabolomic platforms have been applied to the analysis of endocrine tumors to explore the cellular and molecular mechanisms of tumor genesis, progression and metastasis. In this review, we provide a comprehensive overview of spatially resolved proteomics, lipidomics and metabolomics guided by mass spectrometry imaging and spatially resolved microproteomics directed by microextraction and tandem mass spectrometry. In this regard, we will discuss different mass spectrometry imaging techniques, including secondary ion mass spectrometry, matrix-assisted laser desorption/ionization and desorption electrospray ionization. Additionally, we will highlight microextraction approaches such as laser capture microdissection and liquid microjunction extraction. With these methods, proteins can be extracted precisely from specific regions of the endocrine tumor. Finally, we compare applications of proteomic, lipidomic and metabolomic platforms in the field of endocrine tumors and outline their potentials in elucidating cellular and molecular processes involved in endocrine tumors.

A Study of the Lipidomic Profiles of the CAL-27 and HOK Cell Lines Using EMS Spectra

Objective

The aim of this study was to explore the lipidomic profiles of the CAL-27 human tongue cancer cell line and the human oral keratinocyte (HOK) cell line.

Methods

The lipidomic differences between the CAL-27 and the HOK cell lines were investigated using non-targeted high-performance liquid chromatography–mass spectrometry lipidomic analysis. The resulting data were then further mined via bioinformatics analysis technology and metabolic pathway analysis was conducted in order to map the most affected metabolites and pathways in the two cell lines.

Results

A total of 711 lipids were identified, including 403 glycerophospholipids (GPs), 147 glycerolipids, and 161 sphingolipids. Comparison of the enhanced MS (EMS) spectra of the two cell lines in positive and negative ionization modes showed the lipid compositions of HOK and CAL-27 cells to be similar. The expressions of most GP species in CAL-27 cells showed an increasing trend as compared with HOK, whereas a significant increase in phosphatidylcholine was observed (p < 0.05). Significant differences in the lipid composition between CAL-27 and HOK cells were shown as a heatmap. Through principal component analysis and orthogonal partial least squares discriminant analysis, noticeably clear separation trends and satisfactory clustering trends between groups of HOK and CAL-27 cells were identified. The numbers of specific lipid metabolites that could distinguish CAL-27 from HOK in positive and negative modes were 100 and 248, respectively. GP metabolism was the most significantly altered lipid metabolic pathway, with 4 metabolites differentially expressed in 39 hit products.

Conclusion

This study demonstrated the potential of using untargeted mass spectra and bioinformatics analysis to describe the lipid profiles of HOK and CAL-27 cells.

A reversed phase ultra-high-performance liquid chromatography-data independent mass spectrometry method for the rapid identification of mycobacterial lipids

A rapid reversed-phase ultra-high-performance liquid chromatography-high resolution mass spectrometry based mycobacterial lipidomics approach is described. This method enables the separation of various lipid classes including lipids specific to mycobacterial, such as methoxy mycolic acid and α-mycolic acid. Lipid separation occurs during a relatively short runtime of 14 min on a charged surface hybrid C₁₈ column. A high-resolution quadrupole-time of flight mass spectrometer and a data independent acquisition mode allowed for the simultaneous acquisition of the full scan and collision induced dissociation fragmentation. The proposed method provides lipid detection results equivalent to or better than existing methods, but with a faster throughput and an overall higher sensitivity. The reversed-phase ultra-high-performance liquid chromatography-high resolution mass spectrometry method was shown to obtain structural information for lipids extracted from Mycobacterium smegmatis, but the method is applicable to the analysis of lipids from various bacterial and mammalian cell lines.

Noninvasive urine metabolomics of prostate cancer and its therapeutic approaches: a current scenario and future perspective

INTRODUCTION

The sensitive, specific, fast, robust and noninvasive biomarkers for the evaluation of prostate cancer (PC) remain elusive in medical research. However, efforts are in full sway to investigate and resolve these puzzles for clinical practice. Advances in modern analytical techniques, sample processing, and the emergence of multiple omics approaches have created a great hope for the development of better detection modalities for PC. The objective of the present review is to provide a concise overview of the PC metabolomics-based potential discriminating molecules in urine samples using nuclear magnetic resonance spectroscopy and mass spectrometry.

AREA COVERED

A literature search was executed to find the studies reporting the noninvasive urine-based biomarkers for the diagnosis and prognosis of underlying disease. Most studies have extensivelyreported PC discriminating molecules with their respective controls. Additionally, pathophysiology and the treatment paradigm of PC are summarized and related to the insights underpinning the therapeutic intervention of PC.

EXPERT OPINION

With multi-centric, global, comprehensive omics approaches via either a non- or least-invasive bio-matrix may open new avenues of research for PC biomarker discovery, backed by a molecular mechanistic outline.

Lipids in Pathophysiology and Development of the Membrane Lipid Therapy: New Bioactive Lipids

Membranes are mainly composed of a lipid bilayer and proteins, constituting a checkpoint for the entry and passage of signals and other molecules. Their composition can be modulated by diet, pathophysiological processes, and nutritional/pharmaceutical interventions. In addition to their use as an energy source, lipids have important structural and functional roles, e.g., fatty acyl moieties in phospholipids have distinct impacts on human health depending on their saturation, carbon length, and isometry. These and other membrane lipids have quite specific effects on the lipid bilayer structure, which regulates the interaction with signaling proteins. Alterations to lipids have been associated with important diseases, and, consequently, normalization of these alterations or regulatory interventions that control membrane lipid composition have therapeutic potential. This approach, termed membrane lipid therapy or membrane lipid replacement, has emerged as a novel technology platform for nutraceutical interventions and drug discovery. Several clinical trials and therapeutic products have validated this technology based on the understanding of membrane structure and function. The present review analyzes the molecular basis of this innovative approach, describing how membrane lipid composition and structure affects protein-lipid interactions, cell signaling, disease, and therapy (e.g., fatigue and cardiovascular, neurodegenerative, tumor, infectious diseases).

Comprehensive Metabolomics and Lipidomics Profiling of Prostate Cancer Tissue Reveals Metabolic Dysregulations Associated with Disease Development

Prostate cancer (PCa) is a global health problem that affects millions of men every year. In the past decade, metabolomics and related subareas, such as lipidomics, have demonstrated an enormous potential to identify novel mechanisms underlying PCa development and progression, providing a good basis for the development of new and more effective therapies and diagnostics. In this study, a multiplatform metabolomics and lipidomics approach, combining untargeted mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based techniques, was applied to PCa tissues to investigate dysregulations associated with PCa development, in a cohort of 40 patients submitted to radical prostatectomy for PCa. Results revealed significant alterations in the levels of 26 metabolites and 21 phospholipid species in PCa tissue compared with adjacent nonmalignant tissue, suggesting dysregulation in 13 metabolic pathways associated with PCa development. The most affected metabolic pathways were amino acid metabolism, nicotinate and nicotinamide metabolism, purine metabolism, and glycerophospholipid metabolism. A clear interconnection between metabolites and phospholipid species participating in these pathways was observed through correlation analysis. Overall, these dysregulations may reflect the reprogramming of metabolic responses to produce high levels of cellular building blocks required for rapid PCa cell proliferation.

Changes in Plasma Phospholipid Metabolism Are Associated with Clinical Manifestations of Systemic Sclerosis

Systemic sclerosis (SSc) is an autoimmune disease with fibrosis of the skin and/or internal organs, causing a decrease in quality of life and survival. There is no causative therapy, and the pathophysiology of the SSc remains unclear. Studies showed that lipid metabolism was relevant for autoimmune diseases, but little is known about the role of lipids in SSc. In the present study, we sought to explore the phospholipid profile of SSc by using the lipidomics approach. We also aimed to analyze lipidomics results for different clinical manifestations of SSc. Experiments were performed using high-performance liquid chromatography coupled to mass spectrometry for the lipidomic profiling of plasma samples from patients with SSc. Our study showed, for the first time, significant changes in the level of phospholipids such as plasmalogens and sphingomyelins from the plasma of SSc patients as compared to controls. Phosphatidylcholine plasmalogens species and sphingomyelins were significantly increased in SSc patients as compared to controls. Our results also demonstrated a significant association of changes in the metabolism of phospholipids (phosphatidylcholine and phosphatidylethanolamine plasmalogens species and sphingomyelins) with different clinical manifestations of SSc. Further lipidomic studies might lead to the detection of lipids as new biomarkers or therapeutic targets of SSc.