Characterization and annotation of oxidized glycerophosphocholines for non-targeted metabolomics with LC-QTOF-MS data

First insight about the ability of specific glycerophospholipids to discriminate non-small cell lung cancer subtypes

Introduction: Discrimination between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) subtypes in non-small cell lung cancer (NSCLC) patients is a significant challenge in oncology. Lipidomics analysis provides a promising approach for this differentiation. Methods: In an accompanying paper, we explored oxPCs levels in a cohort of 200 NSCLC patients. In this research, we utilized liquid chromatography coupled with mass spectrometry (LC-MS) to analyze the lipidomics profile of matching tissue and plasma samples from 25 NSCLC patients, comprising 11 ADC and 14 SCC cases. This study builds upon our previous findings, which highlighted the elevation of oxidised phosphatidylcholines (oxPCs) in NSCLC patients. Results: We identified eight lipid biomarkers that effectively differentiate between ADC and SCC subtypes using an untargeted approach. Notably, we observed a significant increase in plasma LPA 20:4, LPA 18:1, and LPA 18:2 levels in the ADC group compared to the SCC group. Conversely, tumour PC 16:0/18:2, PC 16:0/4:0; CHO, and plasma PC 16:0/18:2; OH, PC 18:0/20:4; OH, PC 16:0/20:4; OOH levels were significantly higher in the ADC group. Discussion: Our study is the first to report that plasma LPA levels can distinguish between ADC and SCC patients in NSCLC, suggesting a potential role for LPAs in NSCLC subtyping. This finding warrants further investigation into the mechanisms underlying these differences and their clinical implications.

Exploration of oxidized phosphocholine profile in non-small-cell lung cancer

Introduction: Lung cancer is one of the most frequently studied types of cancer and represents the most common and lethal neoplasm. Our previous research on non-small cell lung cancer (NSCLC) has revealed deep lipid profile reprogramming and redox status disruption in cancer patients. Lung cell membranes are rich in phospholipids that are susceptible to oxidation, leading to the formation of bioactive oxidized phosphatidylcholines (oxPCs). Persistent and elevated levels of oxPCs have been shown to induce chronic inflammation, leading to detrimental effects. However, recent reports suggest that certain oxPCs possess anti-inflammatory, pro-survival, and endothelial barrier-protective properties. Thus, we aimed to measure the levels of oxPCs in NSCLC patients and investigate their potential role in lung cancer. Methods: To explore the oxPCs profiles in lung cancer, we performed in-depth, multi-level metabolomic analyses of nearly 350 plasma and lung tissue samples from 200 patients with NSCLC, including adenocarcinoma (ADC) and squamous cell carcinoma (SCC), the two most prevalent NSCLC subtypes and COPD patients as a control group. First, we performed oxPC profiling of plasma samples. Second, we analyzed tumor and non-cancerous lung tissues collected during the surgical removal of NSCLC tumors. Because of tumor tissue heterogeneity, subsequent analyses covered the surrounding healthy tissue and peripheral and central tumors. To assess whether the observed phenotypic changes in the patients were associated with measured oxPC levels, metabolomics data were augmented with data from medical records. Results: We observed a predominance of long-chain oxPCs in plasma samples and of short-chain oxPCs in tissue samples from patients with NSCLC. The highest concentration of oxPCs was observed in the central tumor region. ADC patients showed higher levels of oxPCs compared to the control group, than patients with SCC. Conclusion: The detrimental effects associated with the accumulation of short-chain oxPCs suggest that these molecules may have greater therapeutic utility than diagnostic value, especially given that elevated oxPC levels are a hallmark of multiple types of cancer.

Utilization of LC-MS/MS and Drift Tube Ion Mobility for Characterizing Intact Oxidized Arachidonate-Containing Glycerophosphatidylethanolamine

Lipid peroxidation is a key component in the pathogenesis of numerous disease states, where the oxidative damage of lipids frequently leads to membrane dysfunction and subsequent cellular death. Glycerophosphoethanolamine (PE) is the second most abundant phospholipid found in cellular membranes and, when oxidized, has been identified as an executor of ferroptotic cell death. PE commonly exists in the plasmalogen form, where the presence of the vinyl ether bond and its enrichment in polyunsaturated fatty acids make it especially susceptible to oxidative degradation. This results in a multitude of oxidized products complicating identification and often requiring several analytical techniques for interpretation. In the present study, we outline an analytical approach for the structural characterization of intact oxidized products of arachidonate-containing diacyl and plasmalogen PE. Intact oxidized PE structures, including structural and positional isomers, were identified using complementary liquid chromatography techniques, drift tube ion mobility, and high-resolution tandem mass spectrometry. This work establishes a comprehensive method for the analysis of intact lipid peroxidation products and provides an important pathway to investigate how lipid peroxidation initially impacts glycerophospholipids and their role in redox biology.

Contribution of allergy in the acquisition of uncontrolled severe asthma

Asthma is a multifactorial, heterogeneous disease that has a challenging management. It can be divided in non-allergic and allergic (usually associated with house dust mites (HDM) sensitization). There are several treatments options for asthma (corticosteroids, bronchodilators, antileukotrienes, anticholinergics,…); however, there is a subset of patients that do not respond to any of the treatments, who can display either a T2 or a non-T2 phenotype. A deeper understanding of the differential mechanisms underlying each phenotype will help to decipher the contribution of allergy to the acquisition of this uncontrolled severe phenotype. Here, we aim to elucidate the biological pathways associated to allergy in the uncontrolled severe asthmatic phenotype. To do so, twenty-three severe uncontrolled asthmatic patients both with and without HDM-allergy were recruited from Hospital Universitario de Gran Canaria Dr. Negrin. A metabolomic fingerprint was obtained through liquid chromatography coupled to mass spectrometry, and identified metabolites were associated with their pathways. 9/23 patients had uncontrolled HDM-allergic asthma (UCA), whereas 14 had uncontrolled, non-allergic asthma (UCNA). 7/14 (50%) of the UCNA patients had Aspirin Exacerbated Respiratory Disease. There were no significant differences regarding gender or body mass index; but there were significant differences in age and onset age, which were higher in UCNA patients; and in total IgE, which was higher in UCA. The metabolic fingerprint revealed that 103 features were significantly different between UCNA and UCA (p < 0.05), with 97 being increased in UCA and 6 being decreased. We identified lysophosphocholines (LPC) 18:2, 18:3 and 20:4 (increased in UCA patients); and deoxycholic acid and palmitoleoylcarnitine (decreased in UCA). These metabolites were related with a higher activation of phospholipase A2 (PLA2) and other phospholipid metabolism pathways. Our results show that allergy induces the activation of specific inflammatory pathways, such as the PLA2 pathway, which supports its role in the development of an uncontrolled asthma phenotype. There are also clinical differences, such as higher levels of IgE and earlier onset ages for the allergic asthmatic group, as expected. These results provide evidences to better understand the contribution of allergy to the establishment of a severe uncontrolled phenotype.

Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma

A discovery-based lipid profiling study of serum samples from a cohort that included patients with clear cell renal cell carcinoma (ccRCC) stages I, II, III, and IV (n = 112) and controls (n = 52) was performed using ultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry and machine learning techniques. Multivariate models based on support vector machines and the LASSO variable selection method yielded two discriminant lipid panels for ccRCC detection and early diagnosis. A 16-lipid panel allowed discriminating ccRCC patients from controls with 95.7% accuracy in a training set under cross-validation and 77.1% accuracy in an independent test set. A second model trained to discriminate early (I and II) from late (III and IV) stage ccRCC yielded a panel of 26 compounds that classified stage I patients from an independent test set with 82.1% accuracy. Thirteen species, including cholic acid, undecylenic acid, lauric acid, LPC(16:0/0:0), and PC(18:2/18:2), identified with level 1 exhibited significantly lower levels in samples from ccRCC patients compared to controls. Moreover, 3α-hydroxy-5α-androstan-17-one 3-sulfate, cis-5-dodecenoic acid, arachidonic acid, cis-13-docosenoic acid, PI(16:0/18:1), PC(16:0/18:2), and PC(O-16:0/20:4) contributed to discriminate early from late ccRCC stage patients. The results are auspicious for early ccRCC diagnosis after validation of the panels in larger and different cohorts.

Oxidized lipids in the metabolic profiling of neuroendocrine tumors - Analytical challenges and biological implications

Untargeted metabolomics can be a great tool for exploring new scientific areas; however, wrong metabolite annotation questions the credibility and puts the success of the entire research at risk. Therefore, an effort should be made to improve the quality and robustness of the annotation despite of the challenges, especially when final identification with standards is not possible. Through non-targeted analysis of human plasma samples, from a large cancer cohort study using RP-LC-ESI-QTOF-MS/MS, we have resolved MS/MS annotation through spectral matching, directed to hydroxyeicosatetraenoic acids (HETEs) and, MS/MS structural elucidation for newly annotated oxidized lyso-phosphatidylcholines (oxLPCs). The annotation of unknowns is supported with structural information from fragmentation spectra as well as the fragmentation mechanisms involved, necessarily including data from both polarity modes and different collision energies. In this work, we present evidences that various oxidation products show significant differences between cancer patients and control individuals and we establish a workflow to help identify such modifications. We report here the upregulation of HETEs and oxLPCs in patients with neuroendocrine tumors (NETs). To our knowledge, this is the first attempt to determine HETEs in NETs and one of very few studies where oxLPCs are annotated. The obtained results provide an important insight regarding lipid oxidation in NETs, although their physiological functions still have to be established and require further research.

Framework as a Service, FaaS: Personalized Prebiotic Development for Infants with the Elements of Time and Parametric Modelling of in vitro Fermentation

We proposed a framework with parametric modeling to obtain biological relevant parameters from the total probiotic growth pattern and metabolite production curves. The lag phase, maximum increase rate, and maximum capacity were obtained via a 205-h exploratory In vitro fermentation of a library of 13 structural-characterized prebiotic candidates against an exclusively breastfed infant fecal inoculum. We also conducted 16S rRNA amplicon sequencing of the infant fecal inoculum. Moreover, we introduce a robust composite metabolite-based indicator that reflects the eubiosis/dysbiosis of microbiota to complement the conventional microbial markers. In terms of short-chain fatty acid, we discovered that polymeric beta-glucans from barley demonstrated potential as prebiotic candidates, while alpha-glucans as glycogen showed the least dissolved ammonia production. In terms of total probiotic, beta-glucans from oat and mushroom sclerotia of Pleurotus tuber-regium showed comparable sustainability when compared to alpha-glucans after 48 h. Being classical prebiotic, galacto-oligosaccharides gave the second-highest metabolite-based indicator, followed by lactose. While limited improvement could be made to lactose and oligosaccharides, polymeric beta-glucans from barley avails more capacity for novel prebiotic development, such as structural modification. We anticipate that more similar parallel screening with the element of time and parametric modeling will provide more novel insights.

Integrated strategy for accurately screening biomarkers based on metabolomics coupled with network pharmacology

Screening diagnostic biomarkers can be challenging due to the complexity of traditional Chinese medicine (TCM) and ambiguous pharmacological mechanisms. In this study, we reported an integrated strategy for accurately screening diagnostic biomarkers based on metabolomics coupled with network pharmacology. First, a feasible pharmacological model was established through systems pharmacology and based on metabolomics-based techniques to explore diagnostic biomarkers. While the components satisfying the q-value < 0.05, fold change (FC) ≥ 1.2 or FC ≤ 0.8, coefficient of variance (CV) ≤ 30%(QC) and the variable importance in the project (VIP) value > 1 are considered to be diagnostic biomarkers. Second, the ingredients were retained only when oral bioavailability (OB), Caco-2 permeability, drug half-life, TPSA and drug likeness (DL) satisfied the criteria (OB ≥ 40%; Caco-2 ≥ -0.4; HL ≥ 4 h; TPSA˂140; DL ≥ 0.18) suggested by the TCMSP database. Moreover, ingredients that exhibit extensive biological activity in TCM are also retained. Third, the effect targets of TCM were screened using the TCMSP database, Swiss Target Prediction and STICH online software. Disease targets were gathered from the therapeutic target database (TTD), PharmGkb and TCMSP database. Hub genes were screened by potential protein-protein interaction (PPI) network pharmacology analysis. Finally, a metabolic network pathway is established between the diagnostic biomarker and the hub gene. In the network analysis of metabolic pathways, most of the genes involved in this pathway are the second-step-obtained hub genes, which can explain the accuracy of the identified biomarkers. The proposed integrated strategy was successfully applied to explore the mechanism of action of Pulsatilla decoction (PD) in the treatment of acute ulcerative colitis (UC). Based on this integrated strategy, 23 potential biomarkers of acute UC treated with PD were identified. In conclusion, the integrated strategy provides novel insights into network pharmacology and metabolomics as effective tools to illuminate the mechanism of action of TCM.

Computational solutions in redox lipidomics - Current strategies and future perspectives

The high chemical diversity of lipids allows them to perform multiple biological functions ranging from serving as structural building blocks of biological membranes to regulation of metabolism and signal transduction. In addition to the native lipidome, lipid species derived from enzymatic and non-enzymatic modifications (the epilipidome) make the overall picture even more complex, as their functions are still largely unknown. Oxidized lipids represent the fraction of epilipidome which has attracted high scientific attention due to their apparent involvement in the onset and development of numerous human disorders. Development of high-throughput analytical methods such as liquid chromatography coupled on-line to mass spectrometry provides the possibility to address epilipidome diversity in complex biological samples. However, the main bottleneck of redox lipidomics, the branch of lipidomics dealing with the characterization of oxidized lipids, remains the lack of optimal computational tools for robust, accurate and specific identification of already discovered and yet unknown modified lipids. Here we discuss the main principles of high-throughput identification of lipids and their modified forms and review the main software tools currently available in redox lipidomics. Different levels of confidence for software assisted identification of redox lipidome are defined and necessary steps toward optimal computational solutions are proposed.

Cultivar differentiation of Citri Reticulatae Pericarpium by a combination of hierarchical three-step filtering metabolomics analysis, DNA barcoding and electronic nose

The traditional Chinese medicine Citri Reticulatae Pericarpium (CRP) was mainly originated from the dried pericarp of Citrus reticulata 'Chachi' (Crc), Citrus reticulata 'Dahongpao' (Crd), Citrus reticulata 'Unshiu' (Cru) and Citrus reticulata 'Tangerina' (Crt) in China. Since these four cultivars have great similarities in morphology, reliable methods to differentiate CRP cultivars have rarely been reported. To discriminate the differences of these CRP cultivars, herein an efficient and reliable method by combining metabolomics, DNA barcoding and electronic nose was first established. The hierarchical three-step filtering metabolomics analysis based on liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) indicated that 9 species-specific chemical markers including 6 flavanone glycosides and 3 polymethoxyflavones could be considered as marker metabolites for discrimination of the geoherb Crc from other cultivars. A total of 19 single nucleotide polymorphism (SNP) sites were found in nuclear internal transcribed spacer 2 (ITS2) of CRP, and three stable SNP sites (33, 128 and 174) in the ITS2 region can distinguish the four CRP cultivars. The electronic nose coupled with chemometrics could also be used to effectively distinguish Crc from other CRP cultivars. Therefore, our results indicated that the integrated method will be an effective strategy for discrimination of similar herbal medicines.

CEU Mass Mediator 3.0: A Metabolite Annotation Tool

CEU Mass Mediator (CMM, http://ceumass.eps.uspceu.es ) is an online tool that has evolved from a simple interface to query different metabolomic databases (CMM 1.0) to a tool that unifies the compounds from these databases and, using an expert system with knowledge about the experimental setup and the compounds properties, filters and scores the query results (CMM 2.0). Since this last major revision, CMM has continued to grow, expanding the knowledge base of its expert system and including new services to support researchers in the metabolite annotation and identification process. The information from external databases has been refreshed, and an in-house library with oxidized lipids not present in other sources has been added. This has increased the number of experimental metabolites up 332,665 and the number of predicted metabolites to 681,198. Furthermore, new taxonomy and ontology metadata have been included. CMM has expanded its functionalities with a service for the annotation of oxidized glycerophosphocholines, a service for spectral comparison from MS² data, and a spectral quality-assessment service to determine the reliability of a spectrum for compound identification purposes. To facilitate the collaboration and integration of CMM with external tools and metabolomic platforms, a RESTful API has been created, and it has already been integrated into the HMDB (Human Metabolome Database). This paper will present the novel functionalities incorporated into version 3.0 of CMM.