Human Subjects
Our clinical samples comprised 18 adolescent and young adults (50 percent females) admitted to the Emergency Department of the University Hospital of Salamanca (Spain) with moderate to severe acute ethanol intoxication [14–16]. Acute ethanol intoxication was defined by clinical signs and symptoms (e.g., confusion/disorientation, motor incoordination, unsteady gait, impaired reasoning, slurred speech), blood alcohol levels (BALs) of > 1 g/L, and consumption of at least 5 (50 g, males) or 4 (40 g, females) standard drinks during the 6 hours before admission. However, individuals failed to recall the total amount drunk or the time between the first and last ethanol intake. Exclusion criteria were the presence of other acute (e.g., trauma or infection) or chronic illness, medication use, or suspicion/confirmation of the use of illegal drugs (apart from cannabis). Table 1 describes the clinical, epidemiological, and analytical characteristics of the individuals in this study.
Table 1
Characteristics of study individuals displaying acute ethanol intoxication.
| Males (n = 9) | Females (n = 9) |
Age (years) | 19.67 (0.34) | 19.89 (0.50) |
BALs (g/L) | 2.42 (0.03) | 2.12 (0.04) |
Aspartate aminotransferase levels (IU/L) | 30.33 (2.26) | 19.11 (0.42) |
Alanine aminotransferase levels (IU/L) | 27.22 (3.43) | 14.78 (0.49) |
Alkaline phosphatase levels (IU/L) | 74.22 (3.41) | 59.78 (1.22) |
γ- glutamyl transpeptidase levels (IU/L) | 28.22 (3.55) | 12.00 (0.48) |
White blood cell count/µL | 8738.89 (274.88) | 8173.33 (169.82) |
Individuals who reported weekend drinking (%)* | 6 (75.0) | 8 (88.89) |
Quantitative variables presented as the mean (SEM) and qualitative variables presented as absolute frequencies (percentage). IU, international units. BALs: blood alcohol levels. * A single male individual refused to answer the questionnaire regarding drinking patterns. |
18 healthy controls (9 males and 9 females) recruited from medical and nursing students were also included in the study. Control individuals did not consume alcohol apart from sporadic light drinking, did not report alcohol consumption during the 72 hours prior to blood extraction, and did not partake in binge drinking episodes in the 3 months previous to the study. These subjects possessed normal hematological and plasma biochemical parameters and did not report any chronic or acute illness. The study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the University Hospital of Salamanca (November 22nd, 2012), and written informed consent was obtained from each participant. Blood samples were obtained from the patients upon admission for standard care and research purposes and were used to determine BALs and blood count, for liver function tests, and then snap-frozen in liquid nitrogen and stored at -80°C until use. Samples were processed and analyzed for study only after patients provided informed consent.
Animals and Treatment Strategy
C57/BL6 wild-type (WT, n = 24) and TLR4-knockout (TLR4-KO, n = 24) (C57/BL6 background, kindly provided by Dr. S. Akira, Osaka, Japan) mice were used in this study. 48 animals were used, with 6 mice per treatment group. Animals were distributed into 3–4 animals per cage separated by genotype and maintained with water and a solid diet ad libitum under controlled conditions of temperature (23ºC), humidity (60%), and light/dark cycles (12 h/12 h). All the experimental procedures were carried out in accordance with the guidelines approved by European Communities Council Directive (86/609/ECC) and Spanish Royal Decree 1201/2005 with the approval of the Ethical Committee of Animal Experimentation of the Príncipe Felipe Research Centre (Valencia, Spain) on June 19th, 2019 (Project identification code: 2019-08).
To model binge alcohol drinking, morning doses (9–10 a.m.) of saline or 25% (v/v) ethanol (3 g/kg) in isotonic saline were administered intraperitoneally to 30-day-old mice on 2 consecutive days with 2-day gaps without injections for 2 weeks (postnatal day (PND) 30 to PND 43) (as previously described by Pascual et al. (2007) [17]. Animals were anesthetized 24 h after the last (8th ) ethanol or saline administration (PND 44), and whole blood was collected from the hepatic portal vein. After centrifugation, the separated plasma was snap-frozen in liquid nitrogen and stored at -80°C until use.
EV Isolation from Human and Mouse Plasma
The isolation of plasma EVs used a total exosome isolation kit (Invitrogen, USA) and followed the manufacturer's instructions. 200 µL of initial plasma was used to isolate EVs, and these were collected and frozen at -80ºC until processing.
Lipid Extraction
Lipids were extracted from equal amounts of plasma-derived EVs (0.2 ml/sample), using a modified Folch extraction procedure. The last phase containing the lipids was transferred to fresh tubes, dry vacuumed with nitrogen and lipids were stored at -80ºC until further analysis. Dried samples were then resuspended with isopropanol for different LC/MS acquisition methods (positive and negative ion modes).
LC-MS/MS Analysis
In fully automated Q-TOF acquisition mode, a pooled human lipid extract representing the 36 samples (4 conditions × 9 replicates) was acquired by iterative MS/MS. Detailed experimental methods for chromatography and autoMS/MS mass spectrometry were followed as described [18, 19] with minor modifications. Briefly, sample separation was performed using an Agilent 1290 Infinity LC system coupled to the 6550 Accurate-Mass QTOF (Agilent Technologies, Santa Clara, CA, USA) with electrospray interface (Jet Stream Technology) operating in positive-ion mode (3500 V) or positive-ion mode (3000 V) and high sensitivity mode. The optimal conditions for the electrospray interface were gas temperature of 200°C, drying gas 12L/min, nebulizer 50 psi, sheath gas temperature of 300°C, and sheath gas flow 12 L/min. Lipids were separated on an Infinity Lab Poroshell 120 EC-C18 column (3.0 ×100 mm, 2.7 µm) (Agilent, Santa Clara, CA, USA). Under optimized conditions, the mobile phase consisted of solvent A (10 mM ammonium acetate, 0.2 mM ammonium fluoride in 9:1 water/methanol) and solvent B (10mM ammonium acetate, 0.2 mM ammonium fluoride in 2:3:5 acetonitrile/methanol/isopropanol) using the following gradient: 0 min 70%B, 1 min 70%B, 3.50 min 86%B, 10 min 86%B, 11 min 100%B, 17 min 100%B operating at 50°C and a constant flow rate of 0.6 mL/min. Injection volume was 5 µL for positive and negative modes.
The Agilent Mass Hunter Workstation Software was employed for the data acquisition. LC/MS Data Acquisition B.10.1 (Build 10.1.48) was operated in auto MS/MS, and the three most intense ions (charge states, 1–2) within 300–1700 m/z mass range (over a threshold of 5000 counts and 0.001%) were selected for MS/MS analysis. The quadrupole was set to a "narrow" resolution (1.3m/z), and MS/MS spectra (50–1700 m/z) were acquired until 25,000 total counts or an accumulation time limit of 333ms. To assure the desired mass accuracy of recorded ions, continuous internal calibration was performed during analyses using signals m/z 121.050873 and m/z 922.009798 for positive mode and signals m/z 119.03632 and m/z 980.016375 for negative mode. Additionally, all-ions MS/MS [20] data were acquired on individual samples, with an MS acquisition rate of three spectra/second and four scan segments 0, 10, 20, and 40 eV.
Lipid Annotator Database
5 sets of 5 iterative MS/MS data files from pooled human cell extracts were analyzed with Lipid Annotator software 1 as the first step in the lipidomics workflow. This study used a novel software tool (Lipid Annotator) [21] with a combination of Bayesian scoring, a probability density algorithm, and non-negative least-squares fit to search a theoretical lipid library (modified LipidBlast) developed by Kind et al. [22, 23] to annotate the MS/MS spectra.
Agilent MassHunter Lipid Annotator Version 1.0 was used for all other data analyses. Default method parameters were used, except only [M + H] + and [M + NH4 ] + precursors were considered for positive ion mode analysis, and only [M-H]– and [M + HAc-H]– precursors were considered for negative ion mode analysis. Agilent MassHunter Personal Compound Database and Library (PCDL) Manager Version B.08 SP1 was used to manage and edit the exported annotations.
Lipid Identification
The lipid Personal Compound Database and Library (PCDL) databases created were used for Batch Targeted Feature Extraction in Agilent Mass Hunter Qualitative version 10.0 on the respective batches of 36 all-ions MS/MS data files. The provided "Profinder - Lipids.m" method was adapted in Mass Hunter Qualitative software with modifications previously described by Sartain, M. et al., 2020 [19]. Data were analyzed using the Find by Formula (FbF) algorithm in MassHunter Qualitative Analysis. This approach uses a modified version of the FbF algorithm, which supports the all-ions MS/MS technique. Mass peaks in the low energy channel are first searched against the PCDL created for compounds with the same m/z values, and then a set of putative identifications is automatically compiled. For this list, the fragment ions in the MS/MS spectra from the PCDL are compared to the ions detected in the high-energy channel to confirm the presence of the correct fragments. The precursors and productions are extracted as ion chromatograms and evaluated using a coelution score. The software calculates a number that accounts for abundance, peak shape (symmetry), peak width, and retention time. The resulting compounds were reviewed in the Mass Hunter Qualitative version, and features not qualified were manually removed. Mass Hunter Qualitative results and qualified features were exported as a .cef file.
Bioinformatic Analyses
The strategy applied for this study was based on a transcriptomic analysis workflow. All bioinformatics and statistical analysis were performed using R software v.3.6.3 [24]. Figure 1 illustrates the experimental design.
Data Preprocessing
Data preprocessing included filter entities, normalization of abundance lipid matrix, and exploratory analyses. Mass Hunter Qualitative results (.cef file) were imported into Mass Profiler Professional (MPP) (Agilent Technologies) for statistical analysis, where separate experiments were created for positive and negative ion modes. Entities were filtered based on their frequency, selecting those consistently present in all replicates of at least 1 treatment. A percentile shift normalization algorithm (75%) was used, and datasets were baselined to the median of all samples. The median of their abundance values was calculated when dealing with duplicated lipids with different retention times. Data normalization was followed by exploratory analysis using cluster analysis, principal component analysis (PCA), and box and whisker plots by samples and lipids to detect abundance patterns between samples and lipids and batch effects anomalous behavior in the data. At this point, anomaly-behaving samples and outliers (values that lie over 1.5 x interquartile range (IQRs) below the first quartile (Q1) or above the third quartile (Q3) in the data set) were excluded for presenting a robust batch effect with a critical impact on differential abundance analysis.
Differential Lipid Abundance
Lipid abundance levels between groups were compared using the limma R package [25]. P-values were adjusted using the Benjamini & Hochberg (BH) procedure [26], and significant lipids were considered when the BH-adjusted p-value ≤ 0.05.
Class Enrichment Analysis
Class annotation was conducted using the RefMet database [27] and compared with the LIPID MAPS database [28]. The classification is hierarchical. As an initial step in this division, lipids were divided into several principal categories ("super classes") containing distinct main classes and sub classes of molecules, devising a standard manner of representing the chemical structures of individual lipids and their derivatives. Description of abbreviations is detailed in Table S1. Annotation was followed by ordering lipids according to the p-value and sign of the statistic obtained in the differential lipid abundance. Similar to a Gene Set Enrichment Analysis (GSEA) method, a class enrichment analysis was carried out using Lipid Set Enrichment Analysis (LSEA) implemented in the mdgsa R package [29]. The p-values were corrected for BH, and classes with a BH-adjusted p-value ≤ 0.05 were considered significant.
Comparisons
3 comparisons were performed for each group (human, WT mice, TLR4-KO mice) to analyze differential lipid abundance (Fig. 1): i) the ethanol effects in females (EEF), which compares ethanol-intoxicated females and control females; ii) the ethanol effects in males (EEM), which compares ethanol-intoxicated males and control males; and iii) sex-ethanol interaction (SEI), which compares EEF and EEM. Class enrichment analysis was assessed using the same three comparisons in human samples.
The statistics used to measure the differential patterns were the logarithm of fold change (LFC) to quantify the effect of differential lipid abundance analysis and the logarithm of odds ratio (LOR) to measure the enrichment of each functional class. A positive statistical sign indicates a higher mean for the variable in the first element of the comparison, whereas a negative statistical sign indicates a higher mean value for the second element. The SEI comparisons focus on finding differences between female and male comparisons. Thus, a positive statistic may indicate either upregulation in females and downregulation in males or a higher increase or a lower decrease of the variable in intoxicated female subjects. On the other hand, a negative statistic may indicate either upregulation in males and downregulation in females or a higher increase or a lower decrease of the variable in intoxicated male subjects. In this comparison, the behavior of each lipid across the groups must be assessed a posteriori, examining female and male comparisons (Fig. S1).
In addition, a correlation analysis was conducted between the differential abundance results in the different comparisons. Pearson's correlation coefficient measured the relationship between these differential profiles, providing an overall picture, while the intersection of the significant lipids between comparisons provides us with a specific view of the results of the comparisons. Both approaches complementarily improve the understanding of the results of the different contrasts evaluated.
Web Platform
All data and results generated in the different steps of bioinformatics strategy analysis are available on a web platform (http://bioinfo.cipf.es/sal), which is freely accessible to any user and allows the confirmation of the results described in this manuscript. The front-end was developed using the Angular Framework, the interactive graphics used in this web resource have been implemented with plotly [30], and the exploratory analysis cluster plot was generated with the ggplot2 R package [31].
This easy-to-use resource is divided into seven sections: 1) a summary of analysis results; the detailed results of the 2) exploratory analysis and 3) differential abundance for each of the studies; 4) class annotation results; 5) LSEA results, where the user can interact with the web platform through graphics and tables and search for specific information related to lipid species or classes; and 6–7), which include methods, bioinformatics scripts, and supplementary material.