A longitudinal serum NMR-based metabolomics dataset of ischemia-reperfusion injury in adult cardiac surgery

Salidroside inhibits the ferroptosis to alleviate lung ischemia reperfusion injury via the JAK2/STAT3 signalling pathway

OBJECTIVE

The present study aims to investigate the protective potential of salidroside in both lung ischemia/reperfusion injury (LIRI) mice model and cell hypoxia/reoxygenation (H/R)model and the involvement of ferroptosis and JAK2/STAT3 pathway.

MATERIALS AND METHODS

After we established the IR-induced lung injury model in mice, we administered salidroside and the ferroptosis inhibitor, ferrostatin-1, then assessed the lung tissue injury, ferroptosis (levels of reactive oxygen species level, malondialdehyde and glutathione), and inflammation in lung tissues. The levels of ferroptosis-related proteins (glutathione peroxidase 4, fibroblast-specific protein 1, solute carrier family 1 member 5 and glutaminase 2) in the lung tissue were measured with Western blotting. Next, BEAS-2B cells were used to establish an H/R cell model and treated with salidroside or ferrostatin-1 before the cell viability and the levels of lactate dehydrogenase (LDH), inflammatory factor, ferroptosis-related proteins were measured. The activation of the JAK2/STAT3 signaling pathway was measured with Western blotting, then its role was confirmed with STAT3 knockdown.

RESULTS

Remarkably, salidroside was found to alleviate ferroptosis, inflammation, and lung injury in LIRI mice and the cell injury in H/R cell model. Severe ferroptosis were observed in LIRI mice models and H/R-induced BEAS-2B cells, which was alleviated by salidroside. Furthermore, salidroside could inhibit JAK2/STAT3 activation induced by LIRI. STAT3 knockdown could enhance the effect of salidroside treatment on H/R-induced cell damage and ferroptosis in vitro.

CONCLUSIONS

Salidroside inhibits ferroptosis to alleviate lung ischemia reperfusion injury via the JAK2/STAT3 signaling pathway.

Early plasma proteomic biomarkers and prediction model of acute respiratory distress syndrome after cardiopulmonary bypass: a prospective nested cohort study

BACKGROUND

Early recognition of the risk of acute respiratory distress syndrome (ARDS) after cardiopulmonary bypass (CPB) may improve clinical outcomes. The main objective of this study was to identify proteomic biomarkers and develop an early prediction model for CPB-ARDS.

METHODS

The authors conducted three prospective nested cohort studies of all consecutive patients undergoing cardiac surgery with CPB at Union Hospital of Tongji Medical College Hospital. Plasma proteomic profiling was performed in ARDS patients and matched controls (Cohort 1, April 2021-July 2021) at multiple timepoints: before CPB (T1), at the end of CPB (T2), and 24 h after CPB (T3). Then, for Cohort 2 (August 2021-July 2022), biomarker expression was measured and verified in the plasma. Furthermore, lung ischemia/reperfusion injury (LIRI) models and sham-operation were established in 50 rats to explore the tissue-level expression of biomarkers identified in the aforementioned clinical cohort. Subsequently, a machine learning-based prediction model incorporating protein and clinical predictors from Cohort 2 for CPB-ARDS was developed and internally validated. Model performance was externally validated on Cohort 3 (January 2023-March 2023).

RESULTS

A total of 709 proteins were identified, with 9, 29, and 35 altered proteins between ARDS cases and controls at T1, T2, and T3, respectively, in Cohort 1. Following quantitative verification of several predictive proteins in Cohort 2, higher levels of thioredoxin domain containing 5 (TXNDC5), cathepsin L (CTSL), and NPC intracellular cholesterol transporter 2 (NPC2) at T2 were observed in CPB-ARDS patients. A dynamic online predictive nomogram was developed based on three proteins (TXNDC5, CTSL, and NPC2) and two clinical risk factors (CPB time and massive blood transfusion), with excellent performance (precision: 83.33%, sensitivity: 93.33%, specificity: 61.16%, and F1 score: 85.05%). The mean area under the receiver operating characteristics curve (AUC) of the model after 10-fold cross-validation was 0.839 (95% CI: 0.824-0.855). Model discrimination and calibration were maintained during external validation dataset testing, with an AUC of 0.820 (95% CI: 0.685-0.955) and a Brier Score of 0.177 (95% CI: 0.147-0.206). Moreover, the considerably overexpressed TXNDC5 and CTSL proteins identified in the plasma of patients with CPB-ARDS, exhibited a significant upregulation in the lung tissue of LIRI rats.

CONCLUSIONS

This study identified several novel predictive biomarkers, developed and validated a practical prediction tool using biomarker and clinical factor combinations for individual prediction of CPB-ARDS risk. Assessing the plasma TXNDC5, CTSL, and NPC2 levels might identify patients who warrant closer follow-up and intensified therapy for ARDS prevention following major surgery.

Tissue-specific metabolomic profiling after cardiopulmonary bypass in fetal sheep

Objective

Fetal cardiopulmonary bypass (CPB) is essential to fetal heart surgery, while its development is limited by vital organ dysfunction after CPB. Studying organ metabolism may help to solve this problem. The objective of this study was to describe the tissue-specific metabolic fingerprints of fetal sheep under CPB and to associate them with organ functions.

Methods

Ten pregnant ewes at 90-120 days of gestation were randomly divided into two groups. The bypass group underwent a 1-h fetal CPB, whereas the control group underwent only a fetal sternotomy. During bypass, echocardiography, blood gases, and blood biochemistry were measured. After bypass, lambs were sacrificed, and tissues of the heart, liver, brain, kidney, and placenta were harvested. The metabolites extracted from these tissues were analyzed using non-targeted metabolomics based on liquid chromatography-mass spectrometry techniques.

Results

All tissues except the placenta displayed significant metabolic changes, and the fetal heart displayed obvious functional changes. Fetal sheep that underwent CPB had common and tissue-specific metabolic signatures. These changes can be attributed to dysregulated lipid metabolism, altered amino acid metabolism, and the accumulation of plasticizer metabolism.

Conclusion

Fetal CPB causes tissue-specific metabolic changes in fetal sheep. Studying these metabolic changes, especially cardiac metabolism, is of great significance for the study of fetal CPB.

Systematic Review of NMR-Based Metabolomics Practices in Human Disease Research

Nuclear magnetic resonance (NMR) spectroscopy is one of the principal analytical techniques for metabolomics. It has the advantages of minimal sample preparation and high reproducibility, making it an ideal technique for generating large amounts of metabolomics data for biobanks and large-scale studies. Metabolomics is a popular “omics” technology and has established itself as a comprehensive exploratory biomarker tool; however, it has yet to reach its collaborative potential in data collation due to the lack of standardisation of the metabolomics workflow seen across small-scale studies. This systematic review compiles the different NMR metabolomics methods used for serum, plasma, and urine studies, from sample collection to data analysis, that were most popularly employed over a two-year period in 2019 and 2020. It also outlines how these methods influence the raw data and the downstream interpretations, and the importance of reporting for reproducibility and result validation. This review can act as a valuable summary of NMR metabolomic workflows that are actively used in human biofluid research and will help guide the workflow choice for future research.

Postconditioning with Irisin Attenuates Lung Ischemia/Reperfusion Injury by Suppressing Ferroptosis via Induction of the Nrf2/HO-1 Signal Axis

Iron-dependent lipid peroxidation causes ferroptosis. This study was aimed at verifying that irisin postconditioning can inhibit ferroptosis and minimize lung ischemia/reperfusion (I/R) damage via activating the Nrf2/HO-1 signal axis. We constructed a murine model of I/R lung damage. At the onset of reperfusion, irisin, ferroptosis inhibitor ferrostatin-1, and ferroptosis inducer Fe-citrate were all administered. We discovered that irisin could reduce lung I/R injury, consistent with ferrostatin-1's action. Furthermore, irisin suppressed ferroptosis in lung I/R damage, as evidenced by lower ROS, MDA, and Fe²⁺, as well as alterations in critical protein expression (GPX4 and ACSL4). However, Fe-citrate abolished the protective effects of irisin. Transcriptome research found that irisin increased the mRNA levels of Nrf2 and HO-1. Thus, we used siRNA to investigate the role of the Nrf2/HO-1 axis in irisin-mediated protection against hypoxia/reoxygenation (H/R) damage in MLE-12 cells. Irisin consistently reduced ferroptosis and improved mitochondrial dysfunction caused by H/R. Irisin's cytoprotective function was eliminated when Nrf2 was silenced. As a result, irisin postconditioning may protect against lung I/R damage by suppressing ferroptosis via the Nrf2/HO-1 signaling axis.

Pre- and Post-Resection Urine Metabolic Profiles of Bladder Cancer Patients: Results of Preliminary Studies on Time Series Metabolomics Analysis

Simple Summary

Bladder cancer is one of the most frequently diagnosed cancers worldwide and due to non-specific symptoms, it is often detected at a late stage. For this reason, possible diagnostic alternatives that could be used for non-invasive screening are still being sought. In recent years, metabolomics approach has been frequently used for this type of research, using urine or blood collected from two groups: patients with a given disease and healthy volunteers. Usually, to minimize the impact of between-subject differences, participants of the study are matched in terms of age, gender, or BMI. Another way to rule out the impact of this variability is to analyze samples taken at intervals from the same patient. Therefore, the aim of our study was to validate results obtained using the traditional approach on a small group of patients, from whom samples were taken before and after resection of the bladder tumor, in a given time frame.

Abstract

The incidence of bladder cancer (BCa) has remained high for many years. Nevertheless, its pathomechanism has not yet been fully understood and is still being studied. Therefore, multiplatform untargeted urinary metabolomics analysis has been performed in order to study differences in the metabolic profiles of urine samples collected at three time points: before transurethral resection of bladder tumor (TURBT), the day after the procedure and two weeks after TURBT. Collected samples were analyzed with the use of high-performance liquid chromatography hyphenated with time-of-flight mass spectrometry detection (HPLC-TOF/MS) and gas chromatography coupled with triple quadrupole mass spectrometry detection (GC-QqQ/MS, in a scan mode). Levels of metabolites selected in our previous study were assessed in order to confirm their potential to differentiate the healthy and diseased samples, regardless of the risk factors and individual characteristics. Hippuric acid, pentanedioic acid and uridine confirmed their potential for sample differentiation. Based on the results of statistical analysis for the paired samples (comparison of metabolic profiles of samples collected before TURBT and two weeks after), a set of metabolites belonging to nucleotide metabolism and methylation processes was also selected. Longitudinal studies proved to be useful for the evaluation of metabolic changes in bladder cancer.

Identifying metabolic alterations in newly diagnosed small cell lung cancer patients

BACKGROUND

Small cell lung cancer (SCLC) is a malignant disease with poor prognosis. At the time of diagnosis most patients are already in a metastatic stage. Current diagnosis is based on imaging, histopathology, and immunohistochemistry, but no blood-based biomarkers have yet proven to be clinically successful for diagnosis and screening. The precise mechanisms of SCLC are not fully understood, however, several genetic mutations, protein and metabolic aberrations have been described. We aim at identifying metabolite alterations related to SCLC and to expand our knowledge relating to this aggressive cancer.

METHODS

A total of 30 serum samples of patients with SCLC, collected at the time of diagnosis, and 25 samples of healthy controls were included in this study. The samples were analyzed with nuclear magnetic resonance spectroscopy. Multivariate, univariate and pathways analyses were performed.

RESULTS

Several metabolites were identified to be altered in the pre-treatment serum samples of small-cell lung cancer patients compared to healthy individuals. Metabolites involved in tricarboxylic acid cycle (succinate: fold change (FC) = 2.4, p = 0.068), lipid metabolism (LDL triglyceride: FC = 1.3, p = 0.001; LDL-1 triglyceride: FC = 1.3, p = 0.012; LDL-2 triglyceride: FC = 1.4, p = 0.009; LDL-6 triglyceride: FC = 1.5, p < 0.001; LDL-4 cholesterol: FC = 0.5, p = 0.007; HDL-3 free cholesterol: FC = 0.7, p = 0.002; HDL-4 cholesterol FC = 0.8, p < 0.001; HDL-4 apolipoprotein-A1: FC = 0.8, p = 0.005; HDL-4 apolipoprotein-A2: FC ≥ 0.7, p ≤ 0.001), amino acids (glutamic acid: FC = 1.7, p < 0.001; glutamine: FC = 0.9, p = 0.007, leucine: FC = 0.8, p < 0.001; isoleucine: FC = 0.8, p = 0.016; valine: FC = 0.9, p = 0.032; lysine: FC = 0.8, p = 0.004; methionine: FC = 0.8, p < 0.001; tyrosine: FC = 0.7, p = 0.002; creatine: FC = 0.9, p = 0.030), and ketone body metabolism (3-hydroxybutyric acid FC = 2.5, p < 0.001; acetone FC = 1.6, p < 0.001), among other, were found deranged in SCLC.

CONCLUSIONS

This study provides novel insight into the metabolic disturbances in pre-treatment SCLC patients, expanding our molecular understanding of this malignant disease.

Characterising Alzheimer's disease through integrative NMR- and LC-MS-based metabolomics

Background

Alzheimer's Disease (AD) is a complex and multifactorial disease and novel approaches are needed to illuminate the underlying pathology. Metabolites comprise the end-product of genes, transcripts, and protein regulations and might reflect disease pathogenesis. Blood is a common biofluid used in metabolomics; however, since extracellular vesicles (EVs) hold cell-specific biological material and can cross the blood-brain barrier, their utilization as biological material warrants further investigation. We aimed to investigate blood- and EV-derived metabolites to add insigts to the pathological mechanisms of AD.

Methods

Blood samples were collected from 10 AD and 10 Mild Cognitive Impairment (MCI) patients, and 10 healthy controls. EVs were enriched from plasma using 100,000×g, 1 h, 4 °C with a wash. Metabolites from serum and EVs were measured using liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy. Multivariate and univariate analyses were employed to identify altered metabolites in cognitively impaired individuals.

Results

While no significant EV-derived metabolites were found differentiating patients from healthy individuals, six serum metabolites were found important; valine (p = 0.001, fold change, FC = 0.8), histidine (p = 0.001, FC = 0.9), allopurinol riboside (p = 0.002, FC = 0.2), inosine (p = 0.002, FC = 0.3), 4-pyridoxic acid (p = 0.006, FC = 1.6), and guanosine (p = 0.004, FC = 0.3). Pathway analysis revealed branched-chain amino acids, purine and histidine metabolisms to be downregulated, and vitamin B6 metabolism upregulated in patients compared to controls.

Conclusion

Using a combination of LC-MS and NMR methodologies we identified several altered mechanisms possibly related to AD pathology. EVs require additional optimization prior to their possible utilization as a biological material for AD-related metabolomics studies.

Nuclear Magnetic Resonance Spectroscopy in Clinical Metabolomics and Personalized Medicine: Current Challenges and Perspectives

Personalized medicine is probably the most promising area being developed in modern medicine. This approach attempts to optimize the therapies and the patient care based on the individual patient characteristics. Its success highly depends on the way the characterization of the disease and its evolution, the patient’s classification, its follow-up and the treatment could be optimized. Thus, personalized medicine must combine innovative tools to measure, integrate and model data. Towards this goal, clinical metabolomics appears as ideally suited to obtain relevant information. Indeed, the metabolomics signature brings crucial insight to stratify patients according to their responses to a pathology and/or a treatment, to provide prognostic and diagnostic biomarkers, and to improve therapeutic outcomes. However, the translation of metabolomics from laboratory studies to clinical practice remains a subsequent challenge. Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the two key platforms for the measurement of the metabolome. NMR has several advantages and features that are essential in clinical metabolomics. Indeed, NMR spectroscopy is inherently very robust, reproducible, unbiased, quantitative, informative at the structural molecular level, requires little sample preparation and reduced data processing. NMR is also well adapted to the measurement of large cohorts, to multi-sites and to longitudinal studies. This review focus on the potential of NMR in the context of clinical metabolomics and personalized medicine. Starting with the current status of NMR-based metabolomics at the clinical level and highlighting its strengths, weaknesses and challenges, this article also explores how, far from the initial “opposition” or “competition”, NMR and MS have been integrated and have demonstrated a great complementarity, in terms of sample classification and biomarker identification. Finally, a perspective discussion provides insight into the current methodological developments that could significantly raise NMR as a more resolutive, sensitive and accessible tool for clinical applications and point-of-care diagnosis. Thanks to these advances, NMR has a strong potential to join the other analytical tools currently used in clinical settings.

Metabolic changes during carbon monoxide poisoning: An experimental study

Carbon monoxide (CO) is the leading cause of death by poisoning worldwide. The aim was to explore the effects of mild and severe poisoning on blood gas parameters and metabolites. Eleven pigs were exposed to CO intoxication and had blood collected before and during poisoning. Mild CO poisoning (carboxyhaemoglobin, COHb 35.2 ± 7.9%) was achieved at 32 ± 13 minutes, and severe poisoning (69.3 ± 10.2% COHb) at 64 ± 23 minutes from baseline (2.9 ± 0.5% COHb). Blood gas parameters and metabolites were measured on a blood gas analyser and nuclear magnetic resonance spectrometer, respectively. Unsupervised principal component, analysis of variance and Pearson's correlation tests were applied. A P-value ≤ .05 was considered statistically significant. Mild poisoning resulted in a 28.4% drop in oxyhaemoglobin (OHb) and 12-fold increase in COHb, while severe poisoning in a 65% drop in OHb and 24-fold increase in COHb. Among others, metabolites implicated in regulation of metabolic acidosis (lactate, P < .0001), energy balance (pyruvate, P < .0001; 3-hydroxybutyrc acid, P = .01), respiration (citrate, P = .007; succinate, P = .0003; fumarate, P < .0001), lipid metabolism (glycerol, P = .002; choline, P = .0002) and antioxidant-oxidant balance (glutathione, P = .03; hypoxanthine, P < .0001) were altered, especially during severe poisoning. Our study adds new insights into the deranged metabolism of CO poisoning and leads the way for further investigation.