BATL: Bayesian annotations for targeted lipidomics

LipidIN: a comprehensive repository for flash platform-independent annotation and reverse lipidomics

Improving annotation accuracy, coverage, speed and depth of lipid profiles remains a significant challenge in traditional lipid annotation. We introduce LipidIN, an advanced framework designed for flash platform-independent annotation. LipidIN features a 168.5-million lipid fragmentation hierarchical library that encompasses all potential chain compositions and carbon-carbon double bond locations. The expeditious querying module achieves speeds exceeding one hundred billion queries per second across all mass spectral libraries. The lipid categories intelligence model is developed using three relative retention time rules, reducing false positive annotations and predicting unannotated lipids with a 5.7% estimated false discovery rate, covering 8923 lipids cross various species. More importantly, LipidIN integrates a Wide-spectrum Modeling Yield network for regenerating lipid fragment fingerprints to further improve accuracy and coverage with a 20% estimated recall boosting. We further demonstrate the utility of LipidIN in multiple tasks for lipid annotation and biomarker discovery in clinical cohorts.

Ultra-High-Performance Liquid Chromatography-Orbitrap-MS-Based Untargeted Lipidomics Reveal Lipid Characteristics of a Clinical Strain of Mycoplasma bovis from Holstein Cow

Mycoplasma bovis is a global concern for the cattle industry owing to its high rates of infection and resulting morbidity, but there are no reports on the lipid composition and metabolic pathways. This study examined the lipidomics of M. bovis to better investigate the biological characteristics of clinical isolates of M. bovis. The M. bovis strains NX114 and PG45, cultivated to log-phase, underwent untargeted lipidomics via ultra-high-performance liquid chromatography-mass spectrometry for differential analysis. Over 65 lipid types and 1476 lipid molecules were identified. We found that glycerophospholipids and sphingolipids constitute the predominant lipid components of M. bovis, with significant constituents of its cell membrane comprising ceramides, phosphatidylglycerols, sphingomyelins, triacylglycerols, hexosylceramides, ether-linked oxidized phosphatidylcholines, and oxidized phosphatidylglycerols. Secondly, 562 differentially expressed lipid molecules were identified, including 17 lipid subclasses (15 up-regulated and 2 down-regulated) with significant differences in relative content. These findings indicate that distinct M. bovis isolates contain structurally varied lipid molecules, with sphingomyelin, phosphatidylinositol, cardiolipin, and phosphatidylcholine being characteristic lipids. The glycerophospholipid metabolism pathway was identified as a key pathway regulating lipid metabolism in M. bovis by KEGG pathway enrichment analysis. The results indicated alterations in the lipid metabolism of M. bovis, offering insights into its pathogenic mechanisms.

Unconscious classification of quantitative electroencephalogram features from propofol versus propofol combined with etomidate anesthesia using one-dimensional convolutional neural network

Objective

Establishing a convolutional neural network model for the recognition of characteristic raw electroencephalogram (EEG) signals is crucial for monitoring consciousness levels and guiding anesthetic drug administration.

Methods

This trial was conducted from December 2023 to March 2024. A total of 40 surgery patients were randomly divided into either a propofol group (1% propofol injection, 10 mL: 100 mg) (P group) or a propofol-etomidate combination group (1% propofol injection, 10 mL: 100 mg, and 0.2% etomidate injection, 10 mL: 20 mg, mixed at a 2:1 volume ratio) (EP group). In the P group, target-controlled infusion (TCI) was employed for sedation induction, with an initial effect site concentration set at 5-6 μg/mL. The EP group received an intravenous push with a dosage of 0.2 mL/kg. Six consciousness-related EEG features were extracted from both groups and analyzed using four prediction models: support vector machine (SVM), Gaussian Naive Bayes (GNB), artificial neural network (ANN), and one-dimensional convolutional neural network (1D CNN). The performance of the models was evaluated based on accuracy, precision, recall, and F1-score.

Results

The power spectral density (94%) and alpha/beta ratio (72%) demonstrated higher accuracy as indicators for assessing consciousness. The classification accuracy of the 1D CNN model for anesthesia-induced unconsciousness (97%) surpassed that of the SVM (83%), GNB (81%), and ANN (83%) models, with a significance level of p < 0.05. Furthermore, the mean and mean difference ± standard error of the primary power values for the EP and P groups during the induced period were as follows: delta (23.85 and 16.79, 7.055 ± 0.817, p < 0.001), theta (10.74 and 8.743, 1.995 ± 0.7045, p < 0.02), and total power (24.31 and 19.72, 4.588 ± 0.7107, p < 0.001).

Conclusion

Large slow-wave oscillations, power spectral density, and the alpha/beta ratio are effective indicators of changes in consciousness during intravenous anesthesia with a propofol-etomidate combination. These indicators can aid anesthesiologists in evaluating the depth of anesthesia and adjusting dosages accordingly. The 1D CNN model, which incorporates consciousness-related EEG features, represents a promising tool for assessing the depth of anesthesia.

Clinical Trial Registration

https://www.chictr.org.cn/index.html.

Choline metabolism underpins macrophage IL-4 polarization and RELMα up-regulation in helminth infection

Type 2 cytokines like IL-4 are hallmarks of helminth infection and activate macrophages to limit immunopathology and mediate helminth clearance. In addition to cytokines, nutrients and metabolites critically influence macrophage polarization. Choline is an essential nutrient known to support normal macrophage responses to lipopolysaccharide; however, its function in macrophages polarized by type 2 cytokines is unknown. Using murine IL-4-polarized macrophages, targeted lipidomics revealed significantly elevated levels of phosphatidylcholine, with select changes to other choline-containing lipid species. These changes were supported by the coordinated up-regulation of choline transport compared to naïve macrophages. Pharmacological inhibition of choline metabolism significantly suppressed several mitochondrial transcripts and dramatically inhibited select IL-4-responsive transcripts, most notably, Retnla. We further confirmed that blocking choline metabolism diminished IL-4-induced RELMα (encoded by Retnla) protein content and secretion and caused a dramatic reprogramming toward glycolytic metabolism. To better understand the physiological implications of these observations, naïve or mice infected with the intestinal helminth Heligmosomoides polygyrus were treated with the choline kinase α inhibitor, RSM-932A, to limit choline metabolism in vivo. Pharmacological inhibition of choline metabolism lowered RELMα expression across cell-types and tissues and led to the disappearance of peritoneal macrophages and B-1 lymphocytes and an influx of infiltrating monocytes. The impaired macrophage activation was associated with some loss in optimal immunity to H. polygyrus, with increased egg burden. Together, these data demonstrate that choline metabolism is required for macrophage RELMα induction, metabolic programming, and peritoneal immune homeostasis, which could have important implications in the context of other models of infection or cancer immunity.

TRACES: A Lightweight Browser for Liquid Chromatography-Multiple Reaction Monitoring-Mass Spectrometry Chromatograms

In targeted metabolomic analysis using liquid chromatography–multiple reaction monitoring–mass spectrometry (LC-MRM-MS), hundreds of MRMs are performed in a single run, yielding a large dataset containing thousands of chromatographic peaks. Automation tools for processing large MRM datasets have been reported, but a visual review of chromatograms is still critical, as real samples with biological matrices often cause complex chromatographic patterns owing to non-specific, insufficiently separated, isomeric, and isotopic components. Herein, we report the development of new software, TRACES, a lightweight chromatogram browser for MRM-based targeted LC-MS analysis. TRACES provides rapid access to all MRM chromatograms in a dataset, allowing users to start ad hoc data browsing without preparations such as loading compound libraries. As a special function of the software, we implemented a chromatogram-level deisotoping function that facilitates the identification of regions potentially affected by isotopic signals. Using MRM libraries containing precursor and product formulae, the algorithm reveals all possible isotopic interferences in the dataset and generates deisotoped chromatograms. To validate the deisotoping function in real applications, we analyzed mouse tissue phospholipids in which isotopic interference by molecules with different fatty-acyl unsaturation levels is known. TRACES successfully removed isotopic signals within the MRM chromatograms, helping users avoid inappropriate regions for integration.