Comparative Metabolomic and Lipidomic Analysis of Phenotype Stratified Prostate Cells

NEFL is overexpressed and it modulates invasion and migration in neuroendocrine-like PC3-ML2 prostate cancer cells

Prostate cancer clinical outcomes are varied, from non-aggressive asymptomatic to lethal aggressive neuroendocrine forms which represent a critical challenge in the management of the disease. The neurofilament light ( NEFL ) is proposed to be a tumor suppressor gene. Studies have shown that expression of the gene is decreased in various cancers. We have used quantitative RT-PCR, immunoblotting, methylation specific PCR, siRNA knockdown followed by migration/invasion assays to determine associations between NEFL expression and disease phenotype in a panel of prostate cells. We demonstrate that NEFL is overexpressed and it modulates invasion and migration in PC3-ML2 prostate cancers cells which have an aggressive neuroendocrine-like phenotype.

Role of Lipids and Lipid Metabolism in Prostate Cancer Progression and the Tumor’s Immune Environment

Modulation of lipid metabolism during cancer development and progression is one of the hallmarks of cancer in solid tumors; its importance in prostate cancer (PCa) has been demonstrated in numerous studies. Lipid metabolism is known to interact with androgen receptor signaling, an established driver of PCa progression and castration resistance. Similarly, immune cell infiltration into prostate tissue has been linked with the development and progression of PCa as well as with disturbances in lipid metabolism. Immuno-oncological drugs inhibit immune checkpoints to activate immune cells’ abilities to recognize and destroy cancer cells. These drugs have proved to be successful in treating some solid tumors, but in PCa their efficacy has been poor, with only a small minority of patients demonstrating a treatment response. In this review, we first describe the importance of lipid metabolism in PCa. Second, we collate current information on how modulation of lipid metabolism of cancer cells and the surrounding immune cells may impact the tumor’s immune responses which, in part, may explain the unimpressive results of immune-oncological treatments in PCa.

Site-Specific Intact N-Linked Glycopeptide Characterization of Prostate-Specific Membrane Antigen from Metastatic Prostate Cancer Cells

The composition of N-linked glycans that are conjugated to the prostate-specific membrane antigen (PSMA) and their functional significance in prostate cancer progression have not been fully characterized. PSMA was isolated from two metastatic prostate cancer cell lines, LNCaP and MDAPCa2b, which have different tissue tropism and localization. Isolated PSMA was trypsin-digested, and intact glycopeptides were subjected to LC-HCD-EThcD-MS/MS analysis on a Tribrid Orbitrap Fusion Lumos mass spectrometer. Differential qualitative and quantitative analysis of site-specific N-glycopeptides was performed using Byonic and Byologic software. Comparative quantitative analysis demonstrates that multiple glycopeptides at asparagine residues 51, 76, 121, 195, 336, 459, 476, and 638 were in significantly different abundance in the two cell lines (p < 0.05). Biochemical analysis using endoglycosidase treatment and lectin capture confirm the MS and site occupancy data. The data demonstrate the effectiveness of the strategy for comprehensive analysis of PSMA glycopeptides. This approach will form the basis of ongoing experiments to identify site-specific glycan changes in PSMA isolated from disease-stratified clinical samples to uncover targets that may be associated with disease progression and metastatic phenotypes.

From Omics to Multi-Omics Approaches for In-Depth Analysis of the Molecular Mechanisms of Prostate Cancer

Cancer arises following alterations at different cellular levels, including genetic and epigenetic modifications, transcription and translation dysregulation, as well as metabolic variations. High-throughput omics technologies that allow one to identify and quantify processes involved in these changes are now available and have been instrumental in generating a wealth of steadily increasing data from patient tumors, liquid biopsies, and from tumor models. Extensive investigation and integration of these data have led to new biological insights into the origin and development of multiple cancer types and helped to unravel the molecular networks underlying this complex pathology. The comprehensive and quantitative analysis of a molecule class in a biological sample is named omics and large-scale omics studies addressing different prostate cancer stages have been performed in recent years. Prostate tumors represent the second leading cancer type and a prevalent cause of cancer death in men worldwide. It is a very heterogenous disease so that evaluating inter- and intra-tumor differences will be essential for a precise insight into disease development and plasticity, but also for the development of personalized therapies. There is ample evidence for the key role of the androgen receptor, a steroid hormone-activated transcription factor, in driving early and late stages of the disease, and this led to the development and approval of drugs addressing diverse targets along this pathway. Early genomic and transcriptomic studies have allowed one to determine the genes involved in prostate cancer and regulated by androgen signaling or other tumor-relevant signaling pathways. More recently, they have been supplemented by epigenomic, cistromic, proteomic and metabolomic analyses, thus, increasing our knowledge on the intricate mechanisms involved, the various levels of regulation and their interplay. The comprehensive investigation of these omics approaches and their integration into multi-omics analyses have led to a much deeper understanding of the molecular pathways involved in prostate cancer progression, and in response and resistance to therapies. This brings the hope that novel vulnerabilities will be identified, that existing therapies will be more beneficial by targeting the patient population likely to respond best, and that bespoke treatments with increased efficacy will be available soon.

Shotgun Lipidomics for Differential Diagnosis of HPV-Associated Cervix Transformation

A dramatic increase in cervical diseases associated with human papillomaviruses (HPV) in women of reproductive age has been observed over the past decades. An accurate differential diagnosis of the severity of cervical intraepithelial neoplasia and the choice of the optimal treatment requires the search for effective biomarkers with high diagnostic and prognostic value. The objective of this study was to introduce a method for rapid shotgun lipidomics to differentiate stages of HPV-associated cervix epithelium transformation. Tissue samples from 110 HPV-positive women with cervicitis (n = 30), low-grade squamous intraepithelial lesions (LSIL) (n = 30), high-grade squamous intraepithelial lesions (HSIL) (n = 30), and cervical cancers (n = 20) were obtained. The cervical epithelial tissue lipidome at different stages of cervix neoplastic transformation was studied by a shotgun label-free approach. It is based on electrospray ionization mass spectrometry (ESI-MS) data of a tissue extract. Lipidomic data were processed by the orthogonal projections to latent structures discriminant analysis (OPLS-DA) to build statistical models, differentiating stages of cervix transformation. Significant differences in the lipid profile between the lesion and surrounding tissues were revealed in chronic cervicitis, LSIL, HSIL, and cervical cancer. The lipids specific for HPV-induced cervical transformation mainly belong to glycerophospholipids: phosphatidylcholines, and phosphatidylethanolamines. The developed diagnostic OPLS-DA models were based on 23 marker lipids. More than 90% of these marker lipids positively correlated with the degree of cervix transformation. The algorithm was developed for the management of patients with HPV-associated diseases of the cervix, based on the panel of 23 lipids as a result. ESI-MS analysis of a lipid extract by direct injection through a loop, takes about 25 min (including preparation of the lipid extract), which is significantly less than the time required for the HPV test (several hours for hybrid capture and about an hour for PCR). This makes lipid mass spectrometric analysis a promising method for express diagnostics of HPV-associated neoplastic diseases of the cervix.

Lipidomic profiling of clinical prostate cancer reveals targetable alterations in membrane lipid composition

Dysregulated lipid metabolism is a prominent feature of prostate cancer that is driven by androgen receptor (AR) signaling. Here we used quantitative mass spectrometry to define the "lipidome" in prostate tumors with matched benign tissues (n=21), independent unmatched tissues (n=47), and primary prostate explants cultured with the clinical AR antagonist enzalutamide (n=43). Significant differences in lipid composition were detected and spatially visualized in tumors compared to matched benign samples. Notably, tumors featured higher proportions of monounsaturated lipids overall and elongated fatty acid chains in phosphatidylinositol and phosphatidylserine lipids. Significant associations between lipid profile and malignancy were validated in unmatched samples, and phospholipid composition was characteristically altered in patient tissues that responded to AR inhibition. Importantly, targeting tumor-related lipid features via inhibition of acetyl-CoA carboxylase 1 significantly reduced cellular proliferation and induced apoptosis in tissue explants. This first characterization of the prostate cancer lipidome in clinical tissues reveals enhanced fatty acid synthesis, elongation, and desaturation as tumor-defining features, with potential for therapeutic targeting.

Choline Kinase: An Unexpected Journey for a Precision Medicine Strategy in Human Diseases

Choline kinase (ChoK) is a cytosolic enzyme that catalyzes the phosphorylation of choline to form phosphorylcholine (PCho) in the presence of ATP and magnesium. ChoK is required for the synthesis of key membrane phospholipids and is involved in malignant transformation in a large variety of human tumours. Active compounds against ChoK have been identified and proposed as antitumor agents. The ChoK inhibitory and antiproliferative activities of symmetrical bispyridinium and bisquinolinium compounds have been defined using quantitative structure-activity relationships (QSARs) and structural parameters. The design strategy followed in the development of the most active molecules is presented. The selective anticancer activity of these structures is also described. One promising anticancer compound has even entered clinical trials. Recently, ChoKα inhibitors have also been proposed as a novel therapeutic approach against parasites, rheumatoid arthritis, inflammatory processes, and pathogenic bacteria. The evidence for ChoKα as a novel drug target for approaches in precision medicine is discussed.

Identification of lipidomic profiles associated with drug-resistant prostate cancer cells

BACKGROUND

The association of circulating lipids with clinical outcomes of drug-resistant castration-resistant prostate cancer (DR-CRPC) is not fully understood. While it is known that increases in select lipids correlate to decreased survival, neither the mechanisms mediating these alterations nor the correlation of resistance to drug treatments is well characterized.

METHODS

This gap-in-knowledge was addressed using in vitro models of non-cancerous, hormone-sensitive, CRPC and drug-resistant cell lines combined with quantitative LC-ESI-Orbitrap-MS (LC-ESI-MS/MS) lipidomic analysis and subsequent analysis such as Metaboanalyst and Lipid Pathway Enrichment Analysis (LIPEA).

RESULTS

Several lipid regulatory pathways were identified that are associated with Docetaxel resistance in prostate cancer (PCa). These included those controlling glycerophospholipid metabolism, sphingolipid signaling and ferroptosis. In total, 7460 features were identified as being dysregulated between the cell lines studied, and 21 lipid species were significantly altered in drug-resistant cell lines as compared to nonresistant cell lines. Docetaxel resistance cells (PC3-Rx and DU145-DR) had higher levels of phosphatidylcholine (PC), oxidized lipid species, phosphatidylethanolamine (PE), and sphingomyelin (SM) as compared to parent control cells (PC-3 and DU-145). Alterations were also identified in the levels of phosphatidic acid (PA) and diacylglyceride (DAG), whose levels are regulated by Lipin (LPIN), a phosphatidic acid phosphatase that converts PA to DAG. Data derived from cBioPortal demonstrated a population of PCa patients expressing mutations aligning with amplification of LPIN1, LPIN2 and LPIN3 genes. Lipin amplification in these genes correlated to decreased survival in these patients. Lipin-1 mRNA expression also showed a similar trend in PCa patient data. Lipin-1, but not Lipin-2 or - 3, was detected in several prostate cancer cells, and was increased in 22RV1 and PC-3 cell lines. The increased expression of Lipin-1 in these cells correlated with the level of PA.

CONCLUSION

These data identify lipids whose levels may correlate to Docetaxel sensitivity and progression of PCa. The data also suggest a correlation between the expression of Lipin-1 in cells and patients with regards to prostate cancer cell aggressiveness and patient survivability. Ultimately, these data may be useful for identifying markers of lethal and/or metastatic prostate cancer.

Lipids and cancer: Emerging roles in pathogenesis, diagnosis and therapeutic intervention

With the advent of effective tools to study lipids, including mass spectrometry-based lipidomics, lipids are emerging as central players in cancer biology. Lipids function as essential building blocks for membranes, serve as fuel to drive energy-demanding processes and play a key role as signaling molecules and as regulators of numerous cellular functions. Not unexpectedly, cancer cells, as well as other cell types in the tumor microenvironment, exploit various ways to acquire lipids and extensively rewire their metabolism as part of a plastic and context-dependent metabolic reprogramming that is driven by both oncogenic and environmental cues. The resulting changes in the fate and composition of lipids help cancer cells to thrive in a changing microenvironment by supporting key oncogenic functions and cancer hallmarks, including cellular energetics, promoting feedforward oncogenic signaling, resisting oxidative and other stresses, regulating intercellular communication and immune responses. Supported by the close connection between altered lipid metabolism and the pathogenic process, specific lipid profiles are emerging as unique disease biomarkers, with diagnostic, prognostic and predictive potential. Multiple preclinical studies illustrate the translational promise of exploiting lipid metabolism in cancer, and critically, have shown context dependent actionable vulnerabilities that can be rationally targeted, particularly in combinatorial approaches. Moreover, lipids themselves can be used as membrane disrupting agents or as key components of nanocarriers of various therapeutics. With a number of preclinical compounds and strategies that are approaching clinical trials, we are at the doorstep of exploiting a hitherto underappreciated hallmark of cancer and promising target in the oncologist's strategy to combat cancer.

Circulatory and prostatic tissue lipidomic profiles shifts after high-dose atorvastatin use in men with prostate cancer

Prostate cancer patients using cholesterol-lowering statins have 30% lower risk of prostate cancer death compared to non-users. The effect is attributed to the inhibition of the mevalonate pathway in prostate cancer cells. Moreover, statin use causes lipoprotein metabolism changes in the serum. Statin effect on serum or intraprostatic lipidome profiles in prostate cancer patients has not been explored. We studied changes in the serum metabolomic and prostatic tissue lipidome after high-dose 80 mg atorvastatin intervention to expose biological mechanisms causing the observed survival benefit. Our randomized, double-blind, placebo-controlled clinical trial consisted of 103 Finnish men with prostate cancer. We observed clear difference in post-intervention serum lipoprotein lipid profiles between the study arms (median classification error 11.7%). The atorvastatin effect on intraprostatic lipid profile was not as clear (median classification error 44.7%), although slightly differing lipid profiles by treatment arm was observed, which became more pronounced in men who used atorvastatin above the median of 27 days (statin group median classification error 27.2%). Atorvastatin lowers lipids important for adaptation for hypoxic microenvironment in the prostate suggesting that prostate cancer cell survival benefit associated with statin use might be mediated by both, local and systemic, lipidomic/metabolomic profile changes.

Comprehensive metabolomics analysis of prostate cancer tissue in relation to tumor aggressiveness and TMPRSS2-ERG fusion status

Background

Prostate cancer (PC) can display very heterogeneous phenotypes ranging from indolent asymptomatic to aggressive lethal forms. Understanding how these PC subtypes vary in their striving for energy and anabolic molecules is of fundamental importance for developing more effective therapies and diagnostics. Here, we carried out an extensive analysis of prostate tissue samples to reveal metabolic alterations during PC development and disease progression and furthermore between TMPRSS2-ERG rearrangement-positive and -negative PC subclasses.

Methods

Comprehensive metabolomics analysis of prostate tissue samples was performed by non-destructive high-resolution magic angle spinning nuclear magnetic resonance (¹H HR MAS NMR). Subsequently, samples underwent moderate extraction, leaving tissue morphology intact for histopathological characterization. Metabolites in tissue extracts were identified by ¹H/³¹P NMR and liquid chromatography-mass spectrometry (LC-MS). These metabolomics profiles were analyzed by chemometric tools and the outcome was further validated using proteomic data from a separate sample cohort.

Results

The obtained metabolite patterns significantly differed between PC and benign tissue and between samples with high and low Gleason score (GS). Five key metabolites (phosphocholine, glutamate, hypoxanthine, arginine and α-glucose) were identified, who were sufficient to differentiate between cancer and benign tissue and between high to low GS. In ERG-positive PC, the analysis revealed several acylcarnitines among the increased metabolites together with decreased levels of proteins involved in β-oxidation; indicating decreased acyl-CoAs oxidation in ERG-positive tumors. The ERG-positive group also showed increased levels of metabolites and proteins involved in purine catabolism; a potential sign of increased DNA damage and oxidative stress.

Conclusions

Our comprehensive metabolomic analysis strongly indicates that ERG-positive PC and ERG-negative PC should be considered as different subtypes of PC; a fact requiring different, sub-type specific treatment strategies for affected patients.

Improving diagnosis of genitourinary cancers: Biomarker discovery strategies through mass spectrometry-based metabolomics

The genitourinary oncology field needs integration of results from basic science, epidemiological studies, clinical and translational research to improve the current methods for diagnosis. MS-based metabolomics can be transformative for disease diagnosis and contribute to global health parity. Metabolite panels are promising to translate metabolomic findings into the clinics, changing the current diagnosis paradigm based on single biomarker analysis. This review article describes capabilities of the MS-based oncometabolomics field for improving kidney, prostate, and bladder cancer detection, early diagnosis, risk stratification, and outcome. Published works are critically discussed based on the study design; type and number of samples analyzed; data quality assessment through quality assurance and quality control practices; data analysis workflows; confidence levels reported for identified metabolites; validation attempts; the overlap of discriminant metabolites for the different genitourinary cancers; and the translation capability of findings into clinical settings. Ongoing challenges are discussed, and future directions are delineated.

Lipidomics unveils the complexity of the lipidome in metabolic diseases

Dysregulation of lipid metabolism is responsible for pathologies of human diseases including metabolic diseases. Recent advances in lipidomics analysis allow for the targeted and untargeted identification of lipid species and for their quantification in normal and diseased conditions. Herein, this review provides a brief introduction to lipidomics, highlights its application to characterize the lipidome at the cellular and physiological levels under different biological conditions, and discusses the potential for the use of lipidomics in the discovery of biomarkers.

Prostate cancer diagnosis and characterization with mass spectrometry imaging

Background

Prostate cancer (PCa), the most common cancer and second leading cause of cancer death in American men, presents the clinical challenge of distinguishing between indolent and aggressive tumors for proper treatment. PCa presents significant alterations in metabolic pathways that can potentially be measured using techniques like mass spectrometry (MS) or mass spectrometry imaging (MSI) and used to characterize PCa aggressiveness. MS quantifies metabolomic, proteomic, and lipidomic profiles of biological systems that can be further visualized for their spatial distributions through MSI.

Methods

PubMed was queried for all publications relating to MS and MSI in human prostate cancer from April 2007 to April 2017. With the goal of reviewing the utility of MSI in diagnosis and prognostication of human PCa, MSI articles that reported investigations of PCa-specific metabolites or metabolites indicating PCa aggressiveness were selected for inclusion. Articles were included that covered MS and MSI principles, limitations, and applications in PCa.

Results

We identified nine key studies on MSI in intact human prostate tissue specimens that determined metabolites which could either differentiate between benign and malignant prostate tissue or indicate prostate cancer aggressiveness. These MSI-detected biomarkers show promise in reliably identifying PCa and determining disease aggressiveness.

Conclusions

MSI represents an innovative technique with the ability to interrogate cancer biomarkers in relation to tissue pathologies and investigate tumor aggressiveness. We propose MSI as a powerful adjuvant histopathology imaging tool for prostate tissue evaluations, where clinical translation of this ex vivo technique could make possible the use of MSI for personalized medicine in diagnosis and prognosis of prostate cancer. Moreover, the knowledge provided from this technique can majorly contribute to the understanding of molecular pathogenesis of PCa and other malignant diseases.

Metabolic Perturbation and Potential Markers in Patients with Esophageal Cancer

Clinical diagnosis of esophageal cancer (EC) at early stage is rather difficult. This study aimed to profile the molecules in serum and tissue and identify potential biomarkers in patients with EC. A total of 64 volunteers were recruited, and 83 samples (24 EC serum samples, 21 serum controls, 19 paired EC tissues, and corresponding tumor-adjacent tissues) were analyzed. The gas chromatography time-of-flight mass spectrometry (GC/TOF-MS) was employed, and principal component analysis was used to reveal the discriminatory metabolites and identify the candidate markers of EC. A total of 41 in serum and 36 identified compounds in tissues were relevant to the malignant prognosis. A marked metabolic reprogramming of EC was observed, including enhanced anaerobic glycolysis and glutaminolysis, inhibited tricarboxylic acid (TCA) cycle, and altered lipid metabolism and amino acid turnover. Based on the potential markers of glucose, glutamic acid, lactic acid, and cholesterol, the receiver operating characteristic (ROC) curves indicated good diagnosis and prognosis of EC. EC patients showed distinct reprogrammed metabolism involved in glycolysis, TCA cycle, glutaminolysis, and fatty acid metabolism. The pivotal molecules in the metabolic pathways were suggested as the potential markers to facilitate the early diagnosis of human EC.

Seminal plasma enables selection and monitoring of active surveillance candidates using nuclear magnetic resonance-based metabolomics: A preliminary investigation

Background

Diagnosis and monitoring of localized prostate cancer requires discovery and validation of noninvasive biomarkers. Nuclear magnetic resonance (NMR)-based metabolomics of seminal plasma reportedly improves diagnostic accuracy, but requires validation in a high-risk clinical cohort.

Materials and methods

Seminal plasma samples of 151 men being investigated for prostate cancer were analyzed with ¹H-NMR spectroscopy. After adjustment for buffer (add-to-subtract) and endogenous enzyme influence on metabolites, metabolite profiling was performed with multivariate statistical analysis (principal components analysis, partial least squares) and targeted quantitation.

Results

Seminal plasma metabolites best predicted low- and intermediate-risk prostate cancer with differences observed between these groups and benign samples. Lipids/lipoproteins dominated spectra of high grade samples with less metabolite contributions. Overall prostate cancer prediction using previously described metabolites was not validated.

Conclusion

Metabolomics of seminal plasma in vitro may assist urologists with diagnosis and monitoring of either low or intermediate grade prostate cancer. Less clinical benefit may be observed for high-risk patients. Further investigation in active surveillance cohorts, and/or in combination with in vivo magnetic resonance spectroscopic imaging may further optimize localized prostate cancer outcomes.

Lipid quantification by Raman microspectroscopy as a potential biomarker in prostate cancer

Metastatic castration-resistant prostate cancer (mCRPC) remains incurable and is one of the leading causes of cancer-related death among American men. Therefore, detection of prostate cancer (PCa) at early stages may reduce PCa-related mortality in men. We show that lipid quantification by vibrational Raman Microspectroscopy and Biomolecular Component Analysis may serve as a potential biomarker in PCa. Transcript levels of lipogenic genes including sterol regulatory element-binding protein-1 (SREBP-1) and its downstream effector fatty acid synthase (FASN), and rate-limiting enzyme acetyl CoA carboxylase (ACACA) were upregulated corresponding to both Gleason score and pathologic T stage in the PRAD TCGA cohort. Increased lipid accumulation in late-stage transgenic adenocarcinoma of mouse prostate (TRAMP) tumors compared to early-stage TRAMP and normal prostate tissues were observed. FASN along with other lipogenesis enzymes, and SREBP-1 proteins were upregulated in TRAMP tumors compared to wild-type prostatic tissues. Genetic alterations of key lipogenic genes predicted the overall patient survival using TCGA PRAD cohort. Correlation between lipid accumulation and tumor stage provides quantitative marker for PCa diagnosis. Thus, Raman spectroscopy-based lipid quantification could be a sensitive and reliable tool for PCa diagnosis and staging.

Androgen control of lipid metabolism in prostate cancer: novel insights and future applications

One of the most typical hallmarks of prostate cancer cells is their exquisite dependence on androgens, which is the basis of the widely applied androgen deprivation therapy. Among the variety of key cellular processes and functions that are regulated by androgens, lipid metabolism stands out by its complex regulation and its many intricate links with cancer cell biology. Here, we review our current knowledge on the links between androgens and lipid metabolism in prostate cancer, and highlight recent developments and insights into the links between key oncogenic stimuli and altered lipid synthesis and/or uptake that may hold significant potential for biomarker development and provide new vulnerabilities for therapeutic intervention.

Hexadecenoic Fatty Acid Isomers in Human Blood Lipids and Their Relevance for the Interpretation of Lipidomic Profiles

Monounsaturated fatty acids (MUFA) are emerging health biomarkers, and in particular the ratio between palmitoleic acid (9cis-16:1) and palmitic acid (16:0) affords the delta-9 desaturase index that is increased in obesity. Recently, other positional and geometrical MUFA isomers belonging to the hexadecenoic family (C16 MUFA) were found in circulating lipids, such as sapienic acid (6cis-16:1), palmitelaidic acid (9trans-16:1) and 6trans-16:1. In this work we report: i) the identification of sapienic acid as component of human erythrocyte membrane phospholipids with significant increase in morbidly obese patients (n = 50) compared with age-matched lean controls (n = 50); and ii) the first comparison of erythrocyte membrane phospholipids (PL) and plasma cholesteryl esters (CE) in morbidly obese patients highlighting that some of their fatty acid levels have opposite trends: increases of both palmitic and sapienic acids with the decrease of linoleic acid (9cis,12cis-18:2, omega-6) in red blood cell (RBC) membrane PL were reversed in plasma CE, whereas the increase of palmitoleic acid was similar in both lipid species. Consequentially, desaturase enzymatic indexes gave different results, depending on the lipid class used for the fatty acid content. The fatty acid profile of morbidly obese subjects also showed significant increases of stearic acid (C18:0) and C20 omega-6, as well as decreases of oleic acid (9cis-18:1) and docosahexaenoic acid (C22:6 omega-3) as compared with lean healthy controls. Trans monounsaturated and polyunsaturated fatty acids were also measured and found significantly increased in both lipid classes of morbidly obese subjects. These results highlight the C16 MUFA isomers as emerging metabolic marker provided that the assignment of the double bond position and geometry is correctly performed, thus identifying the corresponding lipidomic pathway. Since RBC membrane PL and plasma CE have different fatty acid trends, caution must also be used in the choice of lipid species for the interpretation of lipidomic profiles.