LIPID MAPS: update to databases and tools for the lipidomics community

Perfluoropentane-based oxygen-loaded nanodroplets reduce microglial activation through metabolic reprogramming

JOURNAL/nrgr/04.03/01300535-202504000-00032/figure1/v/2024-07-06T104127Z/r/image-tiff Microglia, the primary immune cells within the brain, have gained recognition as a promising therapeutic target for managing neurodegenerative diseases within the central nervous system, including Parkinson's disease. Nanoscale perfluorocarbon droplets have been reported to not only possess a high oxygen-carrying capacity, but also exhibit remarkable anti-inflammatory properties. However, the role of perfluoropentane in microglia-mediated central inflammatory reactions remains poorly understood. In this study, we developed perfluoropentane-based oxygen-loaded nanodroplets (PFP-OLNDs) and found that pretreatment with these droplets suppressed the lipopolysaccharide-induced activation of M1-type microglia in vitro and in vivo, and suppressed microglial activation in a mouse model of Parkinson's disease. Microglial suppression led to a reduction in the inflammatory response, oxidative stress, and cell migration capacity in vitro. Consequently, the neurotoxic effects were mitigated, which alleviated neuronal degeneration. Additionally, ultrahigh-performance liquid chromatography-tandem mass spectrometry showed that the anti-inflammatory effects of PFP-OLNDs mainly resulted from the modulation of microglial metabolic reprogramming. We further showed that PFP-OLNDs regulated microglial metabolic reprogramming through the AKT-mTOR-HIF-1α pathway. Collectively, our findings suggest that the novel PFP-OLNDs constructed in this study alleviate microglia-mediated central inflammatory reactions through metabolic reprogramming.

Comprehensive lipidomic insights of differentially accumulating lipids in large pigmented rice sprout collection and the changes in the starch composition upon germination

Germination represents a vital bioprocess characterized by numerous biochemical transformations that significantly influence the nutritional characteristics of rice. The mobilization of starch and lipids during germination plays a pivotal role in altering the dietary profile of rice, thus potentially addressing the nutritional requirements of populations heavily reliant on rice as a staple food. To explore this potential, a comprehensive analysis encompassing lipidomics and starch composition was conducted on a diverse collection of pigmented rice sprouts. High-resolution mass spectrometry unveiled substantial shifts in the lipidome of pigmented rice sprouts, showcasing a notable enrichment in carotenoids and unsaturated triglycerides, with potential human health benefits. Notably, purple rice sprouts exhibited heightened levels of alpha- and beta-carotene. Analysis of starch composition revealed slight changes in amylose and amylopectin content; however, a consistent increase in digestible carbohydrates was observed across all rice varieties. Germination also led to a reduction in resistant starch content, with purple rice sprouts demonstrating a pronounced two-fold decrease (p < 0.05). These changes were corroborated by a 1.33% decrease in gelatinization enthalpy and a 0.40% reduction in the melting of the amylose-lipid complex. Furthermore, pasting property analysis indicated a substantial 42% decrease in the complexation index post-germination. We posit that the insights garnered from this study hold significant promise for the development of novel products enriched with health-promoting lipids and characterized by unique flour properties.

Machine learning methods for compound annotation in non-targeted mass spectrometry-A brief overview of fingerprinting, in silico fragmentation and de novo methods

Non-targeted screenings (NTS) are essential tools in different fields, such as forensics, health and environmental sciences. NTSs often employ mass spectrometry (MS) methods due to their high throughput and sensitivity in comparison to, for example, nuclear magnetic resonance-based methods. As the identification of mass spectral signals, called annotation, is labour intensive, it has been used for developing supporting tools based on machine learning (ML). However, both the diversity of mass spectral signals and the sheer quantity of different ML tools developed for compound annotation present a challenge for researchers in maintaining a comprehensive overview of the field. In this work, we illustrate which ML-based methods are available for compound annotation in non-targeted MS experiments and provide a nuanced comparison of the ML models used in MS data analysis, unravelling their unique features and performance metrics. Through this overview we support researchers to judiciously apply these tools in their daily research. This review also offers a detailed exploration of methods and datasets to show gaps in current methods, and promising target areas, offering a starting point for developers intending to improve existing methodologies.

Selective Enrichment via TiO2 Magnetic Nanoparticles Enables Deep Profiling of Circulating Neutral Glycosphingolipids

Circulating neutral glycosphingolipids (neutral GSLs (nGSLs)) are a unique subset of nGSLs that detach from organs or cell membranes and enter the bloodstream. Altered molecular distribution of circulating nGSL is increasingly associated with diseases. However, profiling of circulating nGSLs presents a lasting challenge due to their low abundances and structural complexity. Although TiO2 magnetic nanoparticles (TiO2 MNPs) were effective for the enrichment of nGSLs in brain tissue, the protocol showed limited selectivity for circulating nGSLs because their abundances were 100-times lower in human plasma than in brain tissue. In this work, we optimized the key parameters of selective enrichment by TiO2 MNPs and achieved 1:10,000 selectivity for nGSLs over interfering phospholipids, while maintaining ∼70% recovery for different subclasses of nGSLs. By integrating TiO2 MNP-based selective enrichment with reversed-phase liquid chromatography mass spectrometry and charge-tagging Paternò-Büchi derivatization, we achieved deep profiling of over 300 structures of nGSLs and sulfatides across 5 orders of magnitude in relative abundances, a significant leap regarding lipid coverage. We also depicted the structural atlas of nGSLs with defined headgroup, long-chain base, N-acyl chain, the location of desaturation, and 2-hydroxylation. Such information provides a valuable resource for lipidomic studies concerning the roles of circulating nGSLs in health and diseases.

De Novo Synthesis of Perdeuterated Phosphoinositide by Installing a Non-native Phospholipid Biopathway in E. coli

Phosphatidylinositol (PI) and its phosphorylated derivatives are of paramount importance in cellular functions and diseases. Understanding their diverse roles is, however, challenged by difficulties in synthesis and labeling techniques. In this proof-of-concept study, we demonstrate that PI can be straightforwardly de novo-synthesized and deuterium (2H)-labeled in Escherichia coli by genomic insertion of PI synthase from Trypanosoma brucei under constitutive synthetic promoter proD. Insertion into loci atpi-gidB and ybb revealed PI accumulation of 41% and 34% (mol/mol), respectively, when cultivated with glycerol as the sole carbon source. Growth of the atpi-gidB-PIS strain in deuterium-labeled (2H) substrates D2O, D8-glycerol, and D6-myo-inositol achieved PI deuteration of 90%, PE deuteration of 95%, and total fatty acids|fatty acid (FA) deuteration of 97%. This study offers an alternative convenient route to chemical and enzymatic labeling synthesis of PI; more excitingly, this work also, in principle, opens a door for tailoring the FA profile of deuterated PI/PE for task-specific application by repurposing FA biosynthesis pathways.

Mobile Phase Contaminants Affect Neutral Lipid Analysis in LC-MS-Based Lipidomics Studies

Lipidomics is a well-established field, enabled by modern liquid chromatography mass spectrometry (LC-MS) technology, rapidly generating large amounts of data. Lipid extracts derived from biological samples are complex, and most spectral features in LC-MS lipidomics data sets remain unidentified. In-depth analyses of commercial triacylglycerol, diacylglycerol, and cholesterol ester standards revealed the expected ammoniated and sodiated ions as well as five additional unidentified higher mass peaks with relatively high intensities. The identities and origin of these unknown peaks were investigated by modifying the chromatographic mobile-phase components and LC-MS source parameters. Tandem MS (MS/MS) of each unknown adduct peak yielded no lipid structural information, producing only an intense ion of the adducted species. The unknown adducts were identified as low-mass contaminants originating from methanol and isopropanol in the mobile phase. Each contaminant was determined to be an alkylated amine species using their monoisotopic masses to calculate molecular formulas. Analysis of bovine liver extract identified 33 neutral lipids with an additional 73 alkyl amine adducts. Analysis of LC-MS-grade methanol and isopropanol from different vendors revealed substantial alkylated amine contamination in one out of three different brands that were tested. Substituting solvents for ones with lower levels of alkyl amine contamination increased lipid annotations by 36.5% or 27.4%, depending on the vendor, and resulted in >2.5-fold increases in peak area for neutral lipid species without affecting polar lipid analysis. These findings demonstrate the importance of solvent selection and disclosure for lipidomics protocols and highlight some of the major challenges when comparing data between experiments.

Alterations of Ceramides, Acylcarnitines, GlyceroLPLs, and Amines in NSCLC Tissues

Abnormal lipid metabolism plays an important role in cancer development. In this study, nontargeted lipidomic study on 230 tissue specimens from 79 nonsmall cell lung cancer (NSCLC) patients was conducted using ultraperformance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS). Downregulation of sphingosine and medium-long-chain ceramides and short-medium-chain acylcarnitine, upregulation of long-chain acylcarnitine C20:0, and enhanced histamine methylation were revealed in NSCLC tissues. Compared with paired noncancerous tissues, adenocarcinoma (AC) tissues had significantly decreased levels of sphingosine, medium-long-chain ceramides (Cer d18:1/12:0 and Cer d16:1/14:0, Cer d18:0/16:0, Cer d18:1/16:0, Cer d18:2/16:0, Cer d18:2/18:0), short-medium-chain (C2-C16) acylcarnitines, LPC 20:0 and LPC 22:1, and significantly increased levels of the long-chain acylcarnitine C20:0, LPC 16:0, LPC P-16:0, LPC 20:1, LPC 20:2, glyceroPC, LPE 16:0, and LPE 18:2. In squamous cell carcinoma (SCC) tissues, sphingosine, Cer d18:2/16:0 and Cer d18:2/18:0, and short-medium-chain acylcarnitines had significantly lower levels, while long-chain acylcarnitines (C20:0, and C22:0 or C22:0 M), LPC 20:1, LPC 20:2, and N1,N12-diacetylspermine had significantly higher levels compared to controls. In AC and SCC tissues, the levels of LPG 18:0, LPG 18:1, and LPS 18:1 were significantly decreased, while the levels of ceramide-1-phosphate (C1P) d18:0/3:0 or LPE P-16:0, N1-acetylspermidine, and 1-methylhistamine were significantly increased than controls. Furthermore, an orthogonal partial least-squares-discriminant analysis (OPLS-DA) model based on a 4-lipid panel was established, showing good discrimination ability between cancerous and noncancerous tissues.

An Untargeted Lipidomics Workflow Incorporating High-Resolution Demultiplexing (HRdm) Drift Tube Ion Mobility-Mass Spectrometry

Global discovery lipidomics can provide comprehensive chemical information toward understanding the intricacies of metabolic lipid disorders such as dyslipidemia; however, the isomeric complexity of lipid species remains an analytical challenge. Orthogonal separation strategies, such as ion mobility (IM), can be inserted into liquid chromatography-mass spectrometry (LC-MS) untargeted lipidomic workflows for additional isomer separation and high-confidence annotation, and the emergence of high-resolution ion mobility (HRIM) strategies provides marked improvements to the resolving power (Rp > 200) that can differentiate small structural differences characteristic of isomers. One such HRIM strategy, high-resolution demultiplexing (HRdm), utilizes multiplexed drift tube ion mobility spectrometry (DTIMS) with post-acquisition algorithmic deconvolution to access high IM resolutions while retaining the measurement precision inherent to the drift tube technique; however, HRdm has yet to be utilized in untargeted studies. In this manuscript, a proof-of-concept study using ATP10D dysfunctional murine models was investigated to demonstrate the utility of HRdm-incorporated untargeted lipidomic analysis pipelines. Total lipid features were found to increase by 2.5-fold with HRdm compared to demultiplexed DTIMS as a consequence of more isomeric lipids being resolved. An example lipid, PC 36:5, was found to be significantly higher in dysfunctional ATP10D mice with two resolved peaks observed by HRdm that were absent in both the functional ATP10D mice and the standard demultiplexed DTIMS acquisition mode. The benefits of utilizing HRdm for discerning isomeric lipids in untargeted workflows have the potential to enhance our analytical understanding of lipids related to disease complexity and biologically relevant studies.

Discrimination of beef composition and sensory quality by using rapid Evaporative Ionisation Mass Spectrometry (REIMS)

Herein, we investigated the potential of REIMS analysis for classifying muscle composition and meat sensory quality. The study utilized 116 samples from 29 crossbred Angus × Salers, across three muscle types. Prediction models were developed combining REIMS fingerprints and meat quality metrics. Varying efficacy was observed across REIMS discriminations - muscle type (71 %), marbling level (32 %), untrained consumer evaluated tenderness (36 %), flavor liking (99 %) and juiciness (99 %). Notably, REIMS demonstrated the ability to classify 116 beef across four Meat Standards Australia grades with an overall accuracy of 37 %. Specifically, "premium" beef could be differentiated from "unsatisfactory", "good everyday" and "better than everyday" grades with accuracies of 99 %, 84 %, and 62 %, respectively. Limited efficacy was observed however, in classifying trained panel evaluated sensory quality and fatty acid composition. Additionally, key predictive features were tentatively identified from the REIMS fingerprints primarily comprised of molecular ions present in lipids, phospholipids, and amino acids.

Interorganelle phospholipid communication, a house not so divided

The presence of membrane-bound organelles with specific functions is one of the main hallmarks of eukaryotic cells. Organelle membranes are composed of specific lipids that govern their function and interorganelle communication. Discoveries in cell biology using imaging and omic technologies have revealed the mechanisms that drive membrane remodeling, organelle contact sites, and metabolite exchange. The interplay between multiple organelles and their interdependence is emerging as the next frontier for discovery using 3D reconstruction of volume electron microscopy (vEM) datasets. We discuss recent findings on the links between organelles that underlie common functions and cellular pathways. Specifically, we focus on the metabolism of ether glycerophospholipids that mediate organelle dynamics and their communication with each other, and the new imaging techniques that are powering these discoveries.

Tick salivary proteome and lipidome with low glycan content correlate with allergic type reactions in the zebrafish model

Ticks, as hematophagous ectoparasites, can manipulate host immune and metabolic processes, causing tick-borne allergies such as α-Gal syndrome (AGS). Glycolipids with bound galactose-alpha-1-3-galactose (α-Gal) are potential allergenic molecules associated with AGS. Nevertheless, proteins and lipids lacking α-Gal modifications may contribute to tick salivary allergies and be linked to AGS. In this study, we characterized the effect of deglycosylated tick salivary proteins without lipids on treated zebrafish fed with dog food formulated with mammalian (beef, lamb, pork) meat by quantitative proteomics analysis of intestinal samples. The characterization and functional annotations of tick salivary lipids with low representation of glycolipids was conducted using a lipidomics approach. Results showed a significant effect of treatment with saliva and saliva deglycosylated protein fraction on zebrafish abnormal or no feeding (p < 0.005). Treatment with this fraction affected multiple metabolic pathways, defense responses to pathogens and protein metabolism, which correlated with abnormal or no feeding. Lipidomics analysis identified 23 lipid classes with low representation of glycolipids (0.70% of identified lipids). The lipid class with highest representation was phosphatidylcholine (PC; 26.66%) and for glycolipids it corresponded to diacylglycerol (DG; 0.48%). Qualitative analysis of PC antibodies revealed that individuals bitten by ticks were more likely to produce PC-IgG antibodies (p < 0.001). DG levels were significantly higher in tick salivary glands (p < 0.05) compared with tick saliva and salivary fractions. The α-Gal content was higher in tick saliva than in deglycosylated saliva and lipid fractions. These results support a possible role for tick salivary proteins and lipids without α-Gal modifications in AGS.

Lipidome of Acinetobacter baumannii antibiotic persister cells

Persister cells constitute a bacterial subpopulation able to survive to high concentrations of antibiotics. This phenotype is temporary and reversible, and thus could be involved in the recurrence of infections and emergence of antibiotic resistance. To better understand how persister cells survive to such high antibiotic concentration, we examined changes in their lipid composition. We thus compared the lipidome of Acinetobacter baumannii ATCC 19606T persister cells formed under ciprofloxacin treatment with the lipidome of control cells grown without antibiotic. Using matrix assisted laser desorption ionisation-Fourier transform ion cyclotron resonance mass spectrometry, we observed a higher abundance of short chains and secondary chains without hydroxylation for lipid A in persister cells. Using liquid chromatography-tandem mass spectrometry, we found that persister cells produced particular phosphatidylglycerols, as LPAGPE and PAGPE, but also lipids with particular acyl chains containing additional hydroxyl group or uncommon di-unsaturation on C18 and C16 acyl chains. In order to determine the impact of these multiple lipidome modifications on membrane fluidity, fluorescence anisotropy assays were performed. They showed an increase of rigidity for the membrane of persister cells, inducing likely a decrease membrane permeability to protect cells during dormancy. Finally, we highlighted that A. baumannii persister cells also produced particular wax esters, composed of two fatty acids and a fatty diol. These uncommon storage lipids are key metabolites allowing a rapid bacterial regrow when antibiotic pressure disappears. These overall changes in persister lipidome may constitute new therapeutic targets to combat these particular dormant cells.

Mixed alkyl/aryl phosphonates identify metabolic serine hydrolases as antimalarial targets

Malaria, caused by Plasmodium falciparum, remains a significant health burden. One major barrier for developing antimalarial drugs is the ability of the parasite to rapidly generate resistance. We previously demonstrated that salinipostin A (SalA), a natural product, potently kills parasites by inhibiting multiple lipid metabolizing serine hydrolases, a mechanism that results in a low propensity for resistance. Given the difficulty of employing natural products as therapeutic agents, we synthesized a small library of lipidic mixed alkyl/aryl phosphonates as bioisosteres of SalA. Two constitutional isomers exhibited divergent antiparasitic potencies that enabled the identification of therapeutically relevant targets. The active compound kills parasites through a mechanism that is distinct from both SalA and the pan-lipase inhibitor orlistat and shows synergistic killing with orlistat. Our compound induces only weak resistance, attributable to mutations in a single protein involved in multidrug resistance. These data suggest that mixed alkyl/aryl phosphonates are promising, synthetically tractable antimalarials.

Identifying and Filling the Chemobiological Gaps of Gut Microbial Metabolites

Human gut microbial metabolites are currently undergoing much research due to their involvement in multiple biological processes that are important for health, including immunity, metabolism, nutrition, and the nervous system. Metabolites exert their effect through interaction with host and bacterial proteins, suggesting the use of "metabolite-mimetic" molecules as drugs and nutraceutics. In the present work, we retrieve and analyze the full set of published interactions of these compounds with human and microbiome-relevant proteins and find patterns in their structure, chemical class, target class, and biological origins. In addition, we use virtual screening to expand (more than 4-fold) the interactions, validate them with retrospective analyses, and use bioinformatic tools to prioritize them based on biological relevance. In this way, we fill many of the chemobiological gaps observed in the published data. By providing these interactions, we expect to speed up the full clarification of the chemobiological space of these compounds by suggesting many reliable predictions for fast, focused experimental testing.

Differences in Prediagnostic Serum Metabolomic and Lipidomic Profiles Between Cirrhosis Patients with and without Incident Hepatocellular Carcinoma

Background

Early detection of hepatocellular carcinoma (HCC) is crucial for improving patient outcomes, but we lack robust clinical biomarkers. This study aimed to identify a metabolite and/or lipid panel for early HCC detection.

Methods

We developed a high-resolution liquid chromatography mass spectrometry (LC-MS)-based profiling platform and evaluated differences in the global metabolome and lipidome between 28 pre-diagnostic serum samples from patients with cirrhosis who subsequently developed HCC (cases) and 30 samples from patients with cirrhosis and no HCC (controls). We linked differentially expressed metabolites and lipids to their associated genes, proteins, and transcriptomic signatures in publicly available datasets. We used machine learning models to identify a minimal panel to distinguish between cases and controls.

Results

Among cases compared with controls, 124 metabolites and 246 lipids were upregulated, while 208 metabolites and 73 lipids were downregulated. The top upregulated metabolites were glycoursodeoxycholic acid, 5-methyltetrahydrofolic acid, octanoyl-coenzyme A, and glycocholic acid. Elevated lipids comprised glycerol lipids, cardiolipin, and phosphatidylethanolamine, whereas suppressed lipids included oxidized phosphatidylcholine and lysophospholipids. There was an overlap between differentially expressed metabolites and lipids and previously published transcriptomic signatures, illustrating an association with liver disease severity. A panel of 12 metabolites that distinguished between cases and controls with an area under the receiver operating curve of 0.98 for the support vector machine (interquartile range, 0.9-1).

Conclusion

Using prediagnostic serum samples, we identified a promising metabolites panel that accurately identifies patients with cirrhosis who progressed to HCC. Further validation of this panel is required.

Transforming Clinical Research: The Power of High-Throughput Omics Integration

High-throughput omics technologies have dramatically changed biological research, providing unprecedented insights into the complexity of living systems. This review presents a comprehensive examination of the current landscape of high-throughput omics pipelines, covering key technologies, data integration techniques and their diverse applications. It looks at advances in next-generation sequencing, mass spectrometry and microarray platforms and highlights their contribution to data volume and precision. In addition, this review looks at the critical role of bioinformatics tools and statistical methods in managing the large datasets generated by these technologies. By integrating multi-omics data, researchers can gain a holistic understanding of biological systems, leading to the identification of new biomarkers and therapeutic targets, particularly in complex diseases such as cancer. The review also looks at the integration of omics data into electronic health records (EHRs) and the potential for cloud computing and big data analytics to improve data storage, analysis and sharing. Despite significant advances, there are still challenges such as data complexity, technical limitations and ethical issues. Future directions include the development of more sophisticated computational tools and the application of advanced machine learning techniques, which are critical for addressing the complexity and heterogeneity of omics datasets. This review aims to serve as a valuable resource for researchers and practitioners, highlighting the transformative potential of high-throughput omics technologies in advancing personalized medicine and improving clinical outcomes.

Combining Enhanced Resolving Power with Duty Cycle Improvements on a Multi-Reflecting Time-of-Flight Mass Spectrometer

The combination of enhanced resolving power and improved duty cycle on a multireflecting time-of-flight mass spectrometer is described. Resolving power increases are achieved by extending the effective ion path length from 47 m to greater than 200 m. Path length increases are achieved through containment of ions within the analyzer for up to N = 5 passes using a pulsed deflection electrode. Resolving power was shown to increase from 220,000 to 402,000 (fwhm) at m/z 785 for N = 1 and N = 4 analyzer passes, respectively. Due to the timing of the pulsed deflection electrode, the approach is particularly suited to high resolution analysis over a targeted m/z range. Duty cycle enhancements are achieved for ions of the targeted m/z range via accumulation prior to orthogonal acceleration, providing signal improvements of 2 orders of magnitude. Achieving such high resolving powers at fast scan rates (30 Hz) can yield additional information such as fine isotope structure; when combined with ppb mass measurement accuracy, high confidence in analyte identification can be achieved. The technique is applied for N = 2 analyzer passes, demonstrating fine isotope structure for a typical UHPLC metabolite identification experiment at a 10 Hz acquisition rate. Additionally, mass spectrometry imaging data is acquired using DESI, demonstrating the improved image clarity achieved at >300,000 (fwhm).

Drug or Toxic? A Brief Understanding of the Edible Corolla of Rhododendron decorum Franch. by Bai Nationality with Comparative Metabolomics Analysis

Rhododendron is a traditional ornamental and medicinal plant in China, renowned for its aesthetic appeal and therapeutic properties. Regarding Rhododendron decorum Franch., mainly distributed in Yunnan Province, its corolla is regarded as an edible food by the Bai ethnic group in Yunnan Province. However, it is still unclear why the Bai people choose to use the Rhododendron species in their seasonal diet. Here, we employed comparative metabolomics analysis to explore the variations in the metabolites and the enriched biosynthesis pathways within the different floral organs of R. decorum Franch. from Heqing and Yulong County. The metabolite analysis showed that 1340 metabolites were identified from the floral organs in the two regions. Comparing the different flower organs of the same region, 85 differential accumulated metabolites (DAMs) were found from the androecium/gynoecium and corolla in the same region, and 66 DAMs were identified from the same organ in different regions. The KEGG pathway and network analysis revealed significant disparities in both the metabolite composition and enriched pathways among the different floral organs or when comparing the same floral organs across diverse regions, with geographical variations exerting even stronger influences. From the perspective of resource utilization, it was observed that the R. decorum Franch. populations in Heqing County exhibited the greater accumulation of secondary metabolites within their flowers, rendering them more advantageous for medicinal purposes, albeit potentially more toxic. This study provides novel insights into the utilization of corollaries for potential de novo pharmacy development.

LipidOne 2.0: A Web Tool for Discovering Biological Meanings Hidden in Lipidomic Data

LipidOne 2.0 (https://lipidone.eu) is a new web bioinformatic tool for the analysis of lipidomic data. It facilitates the exploration of the three structural levels of lipids: classes, molecular species, and lipid building blocks (acyl, alkyl, or alkenes chains). The tool's flexibility empowers users to seamlessly include or exclude experimental groups and lipid classes at any stage of the analysis. LipidOne 2.0 offers a range of mono- and multivariate statistical analyses, specifically tailored to each structural level. This includes a novel lipid biomarker identification function, integrating four diverse statistical parameters. LipidOne 2.0 incorporates Lipid Pathway analysis across all three structural levels of lipids. Users can identify lipid-involved reactions through case-control comparisons, generating lists of genes/enzymes and their activation states based on Z scores. Accessible without the need for registration, LipidOne 2.0 provides a user-friendly and efficient platform for exploring and analyzing lipidomic data. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Dataset preparation for LipidOne 2.0 Support Protocol: Lipid nomenclature from spectrometric experiments Basic Protocol 2: Uploading a dataset into LipidOne 2.0 Basic Protocol 3: Data mining of lipidomic dataset by LipidOne 2.0.

Challenges in Lipidomics Biomarker Identification: Avoiding the Pitfalls and Improving Reproducibility

Identification of features with high levels of confidence in liquid chromatography-mass spectrometry (LC-MS) lipidomics research is an essential part of biomarker discovery, but existing software platforms can give inconsistent results, even from identical spectral data. This poses a clear challenge for reproducibility in biomarker identification. In this work, we illustrate the reproducibility gap for two open-access lipidomics platforms, MS DIAL and Lipostar, finding just 14.0% identification agreement when analyzing identical LC-MS spectra using default settings. Whilst the software platforms performed more consistently using fragmentation data, agreement was still only 36.1% for MS2 spectra. This highlights the critical importance of validation across positive and negative LC-MS modes, as well as the manual curation of spectra and lipidomics software outputs, in order to reduce identification errors caused by closely related lipids and co-elution issues. This curation process can be supplemented by data-driven outlier detection in assessing spectral outputs, which is demonstrated here using a novel machine learning approach based on support vector machine regression combined with leave-one-out cross-validation. These steps are essential to reduce the frequency of false positive identifications and close the reproducibility gap, including between software platforms, which, for downstream users such as bioinformaticians and clinicians, can be an underappreciated source of biomarker identification errors.

Overview and limitations of database in global traditional medicines: A narrative review

The study of traditional medicine has garnered significant interest, resulting in various research areas including chemical composition analysis, pharmacological research, clinical application, and quality control. The abundance of available data has made databases increasingly essential for researchers to manage the vast amount of information and explore new drugs. In this article we provide a comprehensive overview and summary of 182 databases that are relevant to traditional medicine research, including 73 databases for chemical component analysis, 70 for pharmacology research, and 39 for clinical application and quality control from published literature (2000-2023). The review categorizes the databases by functionality, offering detailed information on websites and capacities to facilitate easier access. Moreover, this article outlines the primary function of each database, supplemented by case studies to aid in database selection. A practical test was conducted on 68 frequently used databases using keywords and functionalities, resulting in the identification of highlighted databases. This review serves as a reference for traditional medicine researchers to choose appropriate databases and also provides insights and considerations for the function and content design of future databases.

In Silico Screening Identification of Fatty Acids and Fatty Acid Derivatives with Antiseizure Activity: In Vitro and In Vivo Validation

High fat diets have been used as complementary treatments for seizure disorders for more than a century. Moreover, many fatty acids and derivatives, including the broad-spectrum antiseizure medication valproic acid, have been explored and used as pharmacological agents to treat epilepsy. In this work, we have explored the anticonvulsant potential of a large library of fatty acids and fatty acid derivatives, the LIPID MAPS Structure Database, using structure-based virtual screening to assess their ability to block the voltage-gated sodium channel 1.2 (NaV1.2), a validated target for antiseizure medications. Four of the resulting in silico hits were submitted for experimental confirmation using in vitro patch clamp experiments, and their protective role was evaluated in an acute mice seizure model, the Maximal Electroshock seizure model. These four compounds were found to protect mice against seizures. Two of them exhibited blocking effects on NaV1.2, CaV2.2, and CaV3.1.

Characterisation of Canine and Feline Breast Tumours, Their Metastases, and Corresponding Primary Cell Lines Using LA-REIMS and DESI-MS Imaging

Breast cancer, a complex disease with a significant prevalence to form metastases, necessitates novel therapeutic strategies to improve treatment outcomes. Here, we present the results of a comparative molecular study of primary breast tumours, their metastases, and the corresponding primary cell lines using Desorption Electrospray Ionisation (DESI) and Laser-Assisted Rapid Evaporative Ionisation Mass Spectrometry (LA-REIMS) imaging. Our results show that ambient ionisation mass spectrometry technology is suitable for rapid characterisation of samples, providing a lipid- and metabolite-rich spectrum within seconds. Our study demonstrates that the lipidomic fingerprint of the primary tumour is not significantly distinguishable from that of its metastasis, in parallel with the similarity observed between their respective primary cell lines. While significant differences were observed between tumours and the corresponding cell lines, distinct lipidomic signatures and several phospholipids such as PA(36:2), PE(36:1), and PE(P-38:4)/PE(O-38:5) for LA-REIMS imaging and PE(P-38:4)/PE(O-38:5), PS(36:1), and PI(38:4) for DESI-MSI were identified in both tumours and cells. We show that the tumours' characteristics can be found in the corresponding primary cell lines, offering a promising avenue for assessing tumour responsiveness to therapeutic interventions. A comparative analysis by DESI-MSI and LA-REIMS imaging revealed complementary information, demonstrating the utility of LA-REIMS in the molecular imaging of cancer.

LipidSig 2.0: integrating lipid characteristic insights into advanced lipidomics data analysis

In the field of lipidomics, where the complexity of lipid structures and functions presents significant analytical challenges, LipidSig stands out as the first web-based platform providing integrated, comprehensive analysis for efficient data mining of lipidomic datasets. The upgraded LipidSig 2.0 (https://lipidsig.bioinfomics.org/) simplifies the process and empowers researchers to decipher the complex nature of lipids and link lipidomic data to specific characteristics and biological contexts. This tool markedly enhances the efficiency and depth of lipidomic research by autonomously identifying lipid species and assigning 29 comprehensive characteristics upon data entry. LipidSig 2.0 accommodates 24 data processing methods, streamlining diverse lipidomic datasets. The tool's expertise in automating intricate analytical processes, including data preprocessing, lipid ID annotation, differential expression, enrichment analysis, and network analysis, allows researchers to profoundly investigate lipid properties and their biological implications. Additional innovative features, such as the 'Network' function, offer a system biology perspective on lipid interactions, and the 'Multiple Group' analysis aids in examining complex experimental designs. With its comprehensive suite of features for analyzing and visualizing lipid properties, LipidSig 2.0 positions itself as an indispensable tool for advanced lipidomics research, paving the way for new insights into the role of lipids in cellular processes and disease development.

Illuminating the dark space of neutral glycosphingolipidome by selective enrichment and profiling at multi-structural levels

Glycosphingolipids (GSLs) are essential components of cell membranes, particularly enriched in the nervous system. Altered molecular distributions of GSLs are increasingly associated with human diseases, emphasizing the significance of lipidomic profiling. Traditional GSL analysis methods are hampered by matrix effect from phospholipids and the difficulty in distinguishing structural isomers. Herein, we introduce a highly sensitive workflow that harnesses magnetic TiO2 nanoparticle-based selective enrichment, charge-tagging Paternò-Büchi reaction, and liquid chromatography-tandem mass spectrometry. This approach enables mapping over 300 distinct GSLs in brain tissues by defining sugar types, long chain bases, N-acyl chains, and the locations of desaturation and hydroxylation. Relative quantitation of GSLs across multiple structural levels provides evidence of dysregulated gene and protein expressions of FA2H and CerS2 in human glioma tissue. Based on the structural features of GSLs, our method accurately differentiates human glioma with/without isocitrate dehydrogenase genetic mutation, and normal brain tissue.

Skin Surface Sebum Analysis by ESI-MS

The skin surface is an important sample source that the metabolomics community has only just begun to explore. Alterations in sebum, the lipid-rich mixture coating the skin surface, correlate with age, sex, ethnicity, diet, exercise, and disease state, making the skin surface an ideal sample source for future noninvasive biomarker exploration, disease diagnosis, and forensic investigation. The potential of sebum sampling has been realized primarily via electrospray ionization mass spectrometry (ESI-MS), an ideal approach to assess the skin surface lipidome. However, a better understanding of sebum collection and subsequent ESI-MS analysis is required before skin surface sampling can be implemented in routine analyses. Challenges include ambiguity in definitive lipid identification, inherent biological variability in sebum production, and methodological, technical variability in analyses. To overcome these obstacles, avoid common pitfalls, and achieve reproducible, robust outcomes, every portion of the workflow-from sample collection to data analysis-should be carefully considered with the specific application in mind. This review details current practices in sebum sampling, sample preparation, ESI-MS data acquisition, and data analysis, and it provides important considerations in acquiring meaningful lipidomic datasets from the skin surface. Forensic researchers investigating sebum as a means for suspect elimination in lieu of adequate fingerprint ridge detail or database matches, as well as clinical researchers interested in noninvasive biomarker exploration, disease diagnosis, and treatment monitoring, can use this review as a guide for developing methods of best-practice.

Metabolomic profiling and accurate diagnosis of basal cell carcinoma by MALDI imaging and machine learning

Basal cell carcinoma (BCC), the most common keratinocyte cancer, presents a substantial public health challenge due to its high prevalence. Traditional diagnostic methods, which rely on visual examination and histopathological analysis, do not include metabolomic data. This exploratory study aims to molecularly characterize BCC and diagnose tumour tissue by applying matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) and machine learning (ML). BCC tumour development was induced in a mouse model and tissue sections containing BCC (n = 12) were analysed. The study design involved three phases: (i) Model training, (ii) Model validation and (iii) Metabolomic analysis. The ML algorithm was trained on MS data extracted and labelled in accordance with histopathology. An overall classification accuracy of 99.0% was reached for the labelled data. Classification of unlabelled tissue areas aligned with the evaluation of a certified Mohs surgeon for 99.9% of the total tissue area, underscoring the model's high sensitivity and specificity in identifying BCC. Tentative metabolite identifications were assigned to 189 signals of importance for the recognition of BCC, each indicating a potential tumour marker of diagnostic value. These findings demonstrate the potential for MALDI-MSI coupled with ML to characterize the metabolomic profile of BCC and to diagnose tumour tissue with high sensitivity and specificity. Further studies are needed to explore the potential of implementing integrated MS and automated analyses in the clinical setting.

Imaging and quantification of neuropeptides in mouse pituitary tissue by atmospheric pressure matrix-assisted laser desorption/ionization mass spectrometry

RATIONALE

Atmospheric pressure matrix-assisted laser desorption/ionization (AP-MALDI) mass spectrometry has enabled the untargeted analysis and imaging of neuropeptides and proteins in biological tissues under ambient conditions. Sensitivity in AP-MALDI can be improved by using sample-specific preparation methods.

METHODS

A comprehensive and detailed optimization strategy including instrument parameters, matrix spraying and sample tissue washing pretreatment was implemented to enhance the sensitivity and coverage of neuropeptides in mouse pituitary tissues by commercial AP-MALDI mass spectrometry imaging (MSI).

RESULTS

The sensitivity of a commercial AP-MALDI system for endogenous neuropeptides in mouse pituitary was enhanced by up to 15.2-fold by shortening the transmission gap from the sample plate to the inlet, attaching copper adhesive tape to an indium tin oxide-coated glass slide, optimizing the matrix spray solvent and using sample tissue washing pretreatment. Following careful optimization, the distributions of nine endogenous neuropeptides were successfully visualized in the pituitary. Furthermore, the quantitative capability of AP-MALDI for neuropeptides was evaluated and the concentrations of neuropeptides oxytocin and vasopressin in the pituitary posterior lobe were increased approximately twofold under hypertonic saline stress.

CONCLUSION

Mouse pituitary neuropeptides have emerged as important signaling molecules due to their role in stress response. This work indicates the potential of modified AP-MALDI as a promising AP MSI method for in situ visualization and quantification of neuropeptides in complex biological tissues.

A Targeted, Bioinert LC-MS/MS Method for Sensitive, Comprehensive Analysis of Signaling Lipids

Signaling lipids are key players in cellular processes. Despite their importance, no method currently allows their comprehensive monitoring in one analytical run. Challenges include a wide dynamic range, isomeric and isobaric species, and unwanted interaction along the separation path. Herein, we present a sensitive and robust targeted liquid chromatography-mass spectrometry (LC-MS/MS) approach to overcome these challenges, covering a broad panel of 17 different signaling lipid classes. It involves a simple one-phase sample extraction and lipid analysis using bioinert reversed-phase liquid chromatography coupled to targeted mass spectrometry. The workflow shows excellent sensitivity and repeatability in different biological matrices, enabling the sensitive and robust monitoring of 388 lipids in a single run of only 20 min. To benchmark our workflow, we characterized the human plasma signaling lipidome, quantifying 307 endogenous molecular lipid species. Furthermore, we investigated the signaling lipidome during platelet activation, identifying numerous regulations along important lipid signaling pathways. This highlights the potential of the presented method to investigate signaling lipids in complex biological systems, enabling unprecedentedly comprehensive analysis and direct insight into signaling pathways.

Identification of a 1-acyl-glycerol-3-phosphate acyltransferase from Mycobacterium tuberculosis, a key enzyme involved in triacylglycerol biosynthesis

Latent tuberculosis, caused by dormant Mycobacterium tuberculosis (Mtb), poses a threat to global health through the incubation of undiagnosed infections within the community. Dormant Mtb, which is phenotypically tolerant to antibiotics, accumulates triacylglycerol (TAG) utilizing fatty acids obtained from macrophage lipid droplets. TAG is vital to mycobacteria, serving as a cell envelope component and energy reservoir during latency. TAG synthesis occurs by sequential acylation of glycerol-3-phosphate, wherein the second acylation step is catalyzed by acylglycerol-3-phosphate acyltransferase (AGPAT), resulting in the production of phosphatidic acid (PA), a precursor for the synthesis of TAG and various phospholipids. Here, we have characterized a putative acyltransferase of Mtb encoded by Rv3816c. We found that Rv3816c has all four characteristic motifs of AGPAT, exists as a membrane-bound enzyme, and functions as 1-acylglycerol-3-phosphate acyltransferase. The enzyme could transfer the acyl group to acylglycerol-3-phosphate (LPA) from monounsaturated fatty acyl-coenzyme A of chain length 16 or 18 to produce PA. Complementation of Escherichia coli PlsC mutant in vivo by Rv3816c confirmed that it functions as AGPAT. Its active site mutants, H43A and D48A, were incapable of transferring the acyl group to LPA in vitro and were not able to rescue the growth defect of E. coli PlsC mutant in vivo. Identifying Rv3816c as AGPAT and comparing its properties with other AGPAT homologs is not only a step toward understanding the TAG biosynthesis in mycobacteria but has the potential to explore it as a drug target.

Tracing the lipidome in inborn errors of metabolism

Inborn errors of metabolism (IEM) represent a heterogeneous group of more than 1800 rare disorders, many of which are causing significant childhood morbidity and mortality. More than 100 IEM are linked to dyslipidaemia, but yet our knowledge in connecting genetic information with lipidomic data is limited. Stable isotope tracing studies of the lipid metabolism (STL) provide insights on the dynamic of cellular lipid processes and could thereby facilitate the delineation of underlying metabolic (patho)mechanisms. This mini-review focuses on principles as well as technical limitations of STL and describes potential clinical applications by discussing recently published STL focusing on IEM.

Interplatform comparison between three ion mobility techniques for human plasma lipid collision cross sections

The implementation of ion mobility spectrometry (IMS) in liquid chromatography-high-resolution mass spectrometry (LC-HRMS) workflows has become a valuable tool for improving compound annotation in metabolomics analyses by increasing peak capacity and by adding a new molecular descriptor, the collision cross section (CCS). Although some studies reported high repeatability and reproducibility of CCS determination and only few studies reported good interplatform agreement for small molecules, standardized protocols are still missing due to the lack of reference CCS values and reference materials. We present a comparison of CCS values of approximatively one hundred lipid species either commercially available or extracted from human plasma. We used three different commercial ion mobility technologies from different laboratories, drift tube IMS (DTIMS), travelling wave IMS (TWIMS) and trapped IMS (TIMS), to evaluate both instrument repeatability and interlaboratory reproducibility. We showed that CCS discrepancies of 0.3% (average) could occur depending on the data processing software tools. Moreover, eleven CCS calibrants were evaluated yielding mean RSD below 2% for eight calibrants, ESI Low concentration tuning mix (Tune Mix) showing the lowest RSD (< 0.5%) in both ion modes. Tune Mix calibrated CCS from the three different IMS instruments proved to be well correlated and highly reproducible (R2 > 0.995 and mean RSD ≤ 1%). More than 90% of the lipid CCS had deviations of less than 1%, demonstrating high comparability between techniques, and the possibility to use the CCS as molecular descriptor. We highlighted the need of standardized procedures for calibration, data acquisition, and data processing. This work demonstrates that using harmonized analytical conditions are required for interplatform reproducibility for CCS determination of human plasma lipids.

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.

Aza-Prilezhaev Aziridination-Enabled Multidimensional Analysis of Isomeric Lipids via High-Resolution U-Shaped Mobility Analyzer-Mass Spectrometry

Unsaturated lipids constitute a significant portion of the lipidome, serving as players of multifaceted functions involving cellular signaling, membrane structure, and bioenergetics. While derivatization-assisted liquid chromatography tandem mass spectrometry (LC-MS/MS) remains the gold standard technique in lipidome, it mainly faces challenges in efficiently labeling the carbon-carbon double bond (C═C) and differentiating isomeric lipids in full dimension. This presents a need for new orthogonal methodologies. Herein, a metal- and additive-free aza-Prilezhaev aziridination (APA)-enabled ion mobility mass spectrometric method is developed for probing multiple levels of unsaturated lipid isomerization with high sensitivity. Both unsaturated polar and nonpolar lipids can be efficiently labeled in the form of N-H aziridine without significant side reactions. The signal intensity can be increased by up to 3 orders of magnitude, achieving the nM detection limit. Abundant site-specific fragmentation ions indicate C═C location and sn-position in MS/MS spectra. Better yet, a stable monoaziridination product is dominant, simplifying the spectrum for lipids with multiple double bonds. Coupled with a U-shaped mobility analyzer, identification of geometric isomers and separation of different lipid classes can be achieved. Additionally, a unique pseudo MS3 mode with UMA-QTOF MS boosts the sensitivity for generating diagnostic fragments. Overall, the current method provides a comprehensive solution for deep-profiling lipidomics, which is valuable for lipid marker discovery in disease monitoring and diagnosis.

Mixed Alkyl/Aryl Phosphonates Identify Metabolic Serine Hydrolases as Antimalarial Targets

Malaria, caused by Plasmodium falciparum, remains a significant health burden. A barrier for developing anti-malarial drugs is the ability of the parasite to rapidly generate resistance. We demonstrated that Salinipostin A (SalA), a natural product, kills parasites by inhibiting multiple lipid metabolizing serine hydrolases, a mechanism with a low propensity for resistance. Given the difficulty of employing natural products as therapeutic agents, we synthesized a library of lipidic mixed alkyl/aryl phosphonates as bioisosteres of SalA. Two constitutional isomers exhibited divergent anti-parasitic potencies which enabled identification of therapeutically relevant targets. We also confirm that this compound kills parasites through a mechanism that is distinct from both SalA and the pan-lipase inhibitor, Orlistat. Like SalA, our compound induces only weak resistance, attributable to mutations in a single protein involved in multidrug resistance. These data suggest that mixed alkyl/aryl phosphonates are a promising, synthetically tractable anti-malarials with a low-propensity to induce resistance.

The 2024 Nucleic Acids Research database issue and the online molecular biology database collection

The 2024 Nucleic Acids Research database issue contains 180 papers from across biology and neighbouring disciplines. There are 90 papers reporting on new databases and 83 updates from resources previously published in the Issue. Updates from databases most recently published elsewhere account for a further seven. Nucleic acid databases include the new NAKB for structural information and updates from Genbank, ENA, GEO, Tarbase and JASPAR. The Issue's Breakthrough Article concerns NMPFamsDB for novel prokaryotic protein families and the AlphaFold Protein Structure Database has an important update. Metabolism is covered by updates from Reactome, Wikipathways and Metabolights. Microbes are covered by RefSeq, UNITE, SPIRE and P10K; viruses by ViralZone and PhageScope. Medically-oriented databases include the familiar COSMIC, Drugbank and TTD. Genomics-related resources include Ensembl, UCSC Genome Browser and Monarch. New arrivals cover plant imaging (OPIA and PlantPAD) and crop plants (SoyMD, TCOD and CropGS-Hub). The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). Over the last year the NAR online Molecular Biology Database Collection has been updated, reviewing 1060 entries, adding 97 new resources and eliminating 388 discontinued URLs bringing the current total to 1959 databases. It is available at http://www.oxfordjournals.org/nar/database/c/.