Metabolomics: A useful tool for ischemic stroke research

Curcumin-Loaded Gelatin Nanoparticles Cross the Blood-Brain Barrier to Treat Ischemic Stroke by Attenuating Oxidative Stress and Neuroinflammation

Background

Ischemic stroke is a medical emergency for which effective treatment remains inadequate. Curcumin (Cur) is a natural polyphenolic compound that is regarded as a potent neuroprotective agent. Compared to synthetic pharmaceuticals, Cur possesses minimal side effects and exhibits multiple mechanisms of action, offering significant advantages in the treatment of ischemic stroke. However, drawbacks such as poor water solubility and transmembrane permeability limit the efficacy of Cur. In recent years, nano-delivery systems have attracted great interest in the field of stroke therapy as an effective method to improve drug solubility and cross the blood-brain barrier (BBB).

Methods

In this study, a novel nanomedicine (Cur@GAR NPs) for ischemic stroke treatment was developed based on Cur-loaded gelatin nanoparticles (Cur@Gel NPs) that were then functionalized and modified with rabies virus glycoprotein (RVG29) to target brain tissue. The stability, antimicrobial properties, antioxidant properties, neuroprotective effects, neuronal cell uptake, and biocompatibility of Cur@GAR NPs were investigated in vitro. The in vivo therapeutic effect of Cur@GAR NPs on ischemic stroke was investigated in a middle cerebral artery occlusion (MCAO) rat model using the Morris water maze test and the open field test, and the potential mechanism of action was further investigated by histological analysis.

Results

The resulting Cur@GAR NPs improved the solubility of Cur and exhibited good dispersion. In vitro studies have shown that Cur@GAR NPs exhibit great antimicrobial properties, antioxidant properties and intracellular reactive oxygen species (ROS) protection. Notably, RVG29 significantly enhanced the uptake of Cur@GAR NPs by SH-SY5Y cells. Furthermore, in vivo studies verified the role of Cur@GAR NPs in reducing nerve damage and supporting neurological recovery. In the MCAO rat model, Cur@GAR NPs significantly attenuated neuroinflammation, reduced neuronal apoptosis and restored behavioral functions to a great extent.

Conclusion

Together these findings implied that Cur@GAR NPs could provide a novel and promising approach for effective ischemic stroke treatment.

Metabolomics-driven approaches for identifying therapeutic targets in drug discovery

Identification of therapeutic targets can directly elucidate the mechanism and effect of drug therapy, which is a central step in drug development. The disconnect between protein targets and phenotypes under complex mechanisms hampers comprehensive target understanding. Metabolomics, as a systems biology tool that captures phenotypic changes induced by exogenous compounds, has emerged as a valuable approach for target identification. A comprehensive overview was provided in this review to illustrate the principles and advantages of metabolomics, delving into the application of metabolomics in target identification. This review outlines various metabolomics-based methods, such as dose-response metabolomics, stable isotope-resolved metabolomics, and multiomics, which identify key enzymes and metabolic pathways affected by exogenous substances through dose-dependent metabolite-drug interactions. Emerging techniques, including single-cell metabolomics, artificial intelligence, and mass spectrometry imaging, are also explored for their potential to enhance target discovery. The review emphasizes metabolomics' critical role in advancing our understanding of disease mechanisms and accelerating targeted drug development, while acknowledging current challenges in the field.

Enantioselective toxicity effect and mechanisms of bifenthrin enantiomers on normal human hepatocytes

In recent decades, the toxicity of chiral pesticides to non-target organisms has attracted increasing attention. Cellular metabolic disorders are essential sensitive molecular initiating event for toxicological effects. BF is a typical chiral pesticide, and the liver is the main organ for BF accumulation. This study aimed to investigate the potential molecular mechanism of BF enantiomers' different toxic effects on L02 by a non-targeted metabolomic approach. Results revealed that the BF enantiomers exhibited different metabolic responses. In total, 51 and 36 differential metabolites were perturbed by 1S-cis-BF and 1R-cis-BF at the value of variable importance, respectively. When L02 were exposed to 1R-cis-BF, the significantly disturbed metabolic pathways were nicotinate and nicotinamide metabolism and pyrimidine metabolism. By comparison, more significantly perturbed metabolic pathways were received when the L02 were exposed to 1S-cis-BF, including glycine, serine and threonine metabolism, nicotinate and nicotinamide metabolism, arginine and proline metabolism, cysteine and methionine metabolism, glycerolipid metabolism, histidine metabolism, pyrimidine metabolism, amino sugar and nucleotide sugar metabolism and arginine biosynthesis. The results offer a new perspective in understanding the role of selective cytotoxicity of BF enantiomers, and help to evaluate the risk to human health at the enantiomeric level.

Efficacy of Astragalus membranaceus-Carthamus tinctorius in cerebral ischemia/reperfusion injury: Insights from metabolomics and mass spectrometry imaging

BACKGROUND

The combination of Astragalus membranaceus and Carthamus tinctorius (AC) exhibits significant therapeutic effects in cerebral ischemia/reperfusion injury (CIRI). Understanding the metabolic characteristics of brain microregions and disturbances in tissues and systemic circulation is crucial for elucidating the mechanisms of CIRI and the therapeutic benefits of AC. However, in situ metabolic regulation of the complex brain structure has not been adequately studied, and the therapeutic mechanism of AC requires immediate clarification.

PURPOSE

The present study aimed to unveil the specific metabolic reprogramming of CIRI at systemic and microregional levels, identify key metabolic pathways and metabolites, and elucidate the therapeutic mechanisms of AC.

METHODS

Air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI), a newly developed technique, was used to investigate metabolites in brain microregions. Hematoxylin-eosin, Nissl, and immunofluorescence staining were performed to visualize the microscopic changes associated with spatial metabolism. A comprehensive metabolomics study was conducted on serum, brain tissue, and microregions, along with neurological assessments, cerebral infarction measurements, and Evans blue experiments, to assess the systemic and local metabolic effects of AC treatment for CIRI.

RESULTS

AC significantly reduced neurological damage, minimized infarct size, and repaired blood-brain barrier damage in CIRI rats. AFADESI-MSI demonstrated that the metabolic imbalance caused by CIRI primarily occurs in the cerebral cortex, hippocampus, caudate putamen, thalamus, cerebellar cortex, and fiber tract regions. Significant changes in 16 metabolites were observed in these regions, corresponding to neuron damage, glial cell activation, and neural repair. 20 metabolites from serum and 4 from brain tissue varied significantly with the sham group. Comprehensive metabolomics analysis indicated a close relationship among serum, tissue, and microregional metabolism. CIRI-induced systemic and localized metabolic disorders involve 14 metabolic pathways. AC conferred therapeutic benefits in CIRI by reversing various metabolic imbalances.

CONCLUSION

AFADESI-MSI efficiently visualized brain microregion metabolism. Comprehensive metabolomics analysis revealed detailed insights into the specific metabolic reprogramming in CIRI and the therapeutic impacts of AC. AC demonstrated significant clinical potential as an adjunct therapy to existing CIRI treatments.

Hypoxanthine is a metabolic biomarker for inducing GSDME-dependent pyroptosis of endothelial cells during ischemic stroke

Rationale: Stroke induces metabolic changes in the body, and metabolites have become potential biomarkers for stroke. However, the specific metabolites involved in stroke and the mechanisms underlying brain injury during stroke remain unclear. Methods: Surface-enhanced Raman spectroscopy (SERS) and liquid chromatography-mass spectrometry (LC‒MS) analysis of clinical serum samples from 69 controls and 51 ischemic stroke patients who underwent reperfusion within 24 hours were performed to identify differentially abundant metabolites. Mice were subjected to transient middle cerebral artery occlusion (tMCAO) and then intravenously injected with hypoxanthine. The infarct area was evaluated via tetrazolium chloride (TTC) staining, and behavior tests were conducted. Blood-brain barrier (BBB) leakage was assessed by Evans blue and IgG staining. Human blood vessel organoids were used to investigate the mechanism of hypoxanthine-induced pyroptosis of endothelial cells. Results: SERS and LC‒MS revealed the metabolic profiles of serum from stroke patients and controls with high sensitivity, speed and accuracy. Hypoxanthine levels were significantly elevated in the acute stage of ischemic stroke in both patients and mice (p < 0.001 after Bonferroni correction). In addition, increasing hypoxanthine increased the infarct area and aggravated BBB leakage and neurobehavioral deficits in mice after ischemic stroke. Further mechanistic studies using endothelial cells, human blood vessel organoids, and stroke mice demonstrated that hypoxanthine-mediated gasdermin E (GSDME)-dependent pyroptosis of endothelial cells occurs through intracellular Ca2+ overload. Conclusion: Our study identified hypoxanthine as an important metabolite that induces vascular injury and BBB disruption in stroke through triggering GSDME-dependent pyroptosis of endothelial cells.

Metabolomics-Based Study of the Protective Effect of 4-Hydroxybenzyl Alcohol on Ischemic Astrocytes

Ischemic stroke is a common and dangerous disease in clinical practice. Astrocytes (ASs) are essential for maintaining the metabolic balance of the affected regions during the disease process. 4-Hydroxybenzyl alcohol (4HBA) from Gastrodia elata Bl. has potential neuroprotective properties due to its ability to cross the blood-brain barrier. In an in vitro experiment, we replicated the oxygen-glucose deprivation/reoxygenation model, and used methyl thiazoly tertrazolium, flow cytometry, kits, and other technical means to clarify the protective effect of 4HBA on primary ASs. In in vivo experiments, the 2VO model was replicated, and immunofluorescence and immunohistochemistry techniques were used to clarify the protective effect of 4HBA on ASs and the maintenance of the blood-brain barrier. Differential metabolites and related pathways were screened and verified using metabolomics analysis and western blot. 4HBA noticeably amplified AS cell survival, reduced mitochondrial dysfunction, and mitigated oxidative stress. It demonstrated a protective effect on ASs in both environments and was instrumental in stabilizing the blood-brain barrier. Metabolomic data indicated that 4HBA regulated nucleic acid and glutathione metabolism, influencing purines, pyrimidines, and amino acids, and it activated the N-methyl-D-aspartate/p-cAMP-response element binding protein/brain-derived neurotrophic factor signaling pathway via N-methyl-D-aspartate R1/N-methyl-D-aspartate 2C receptors. Our findings suggest that 4HBA is a potent neuroprotective agent against ischemic stroke, enhancing AS cell survival and function while stabilizing the blood-brain barrier. The N-methyl-D-aspartate/p-cAMP-response element binding protein/brain-derived neurotrophic factor signaling pathway is activated by 4HBA.

An Overview of Pre-Analytical Factors Impacting Metabolomics Analyses of Blood Samples

Discrepant sample processing remains a significant challenge within blood metabolomics research, introducing non-biological variation into the measured metabolome and biasing downstream results. Inconsistency during the pre-analytical phase can influence experimental processes, producing metabolome measurements that are non-representative of in vivo composition. To minimize variation, there is a need to create and adhere to standardized pre-analytical protocols for blood samples intended for use in metabolomics analyses. This will allow for reliable and reproducible findings within blood metabolomics research. In this review article, we provide an overview of the existing literature pertaining to pre-analytical factors that influence blood metabolite measurements. Pre-analytical factors including blood tube selection, pre- and post-processing time and temperature conditions, centrifugation conditions, freeze-thaw cycles, and long-term storage conditions are specifically discussed, with recommendations provided for best practices at each stage.

Comparative Targeted Metabolomics of Ischemic Stroke: Thrombi and Serum Profiling for the Identification of Stroke-Related Metabolites

Ischemic stroke is one of the leading causes of death and permanent disability in the world. Rapid diagnosis and intervention are crucial for reducing its consequences on individuals and societies. Therefore, identifying reliable biomarkers for early detection, prognostics, and therapy can facilitate the early prediction and prevention of stroke. Metabolomics has been shown as a promising tool for biomarker discovery since many post-ischemic metabolites can be found in the plasma or serum of the patient. In this research, we performed a comparative targeted metabolomic analysis of stroke thrombi, stroke patient serums, and healthy control serums in order to determine the alteration in the patients' metabolomes, which might serve as biomarkers for early prediction or stroke prevention. The most statistically altered metabolites characterized in the patient serums compared with the control serums were glutamate and serotonin, followed by phospholipids and triacylglycerols. In stroke thrombi compared with the patients' serums, the most significantly altered metabolites were classified as lipids, with choline-containing phospholipids and sphingomyelins having the highest discriminatory score. The results of this preliminary study could help in understanding the roles of different metabolic changes that occur during thrombosis and cerebral ischemia and possibly suggest new metabolic biomarkers for ischemic stroke.

Identification of Urine Metabolic Markers of Stroke Risk Using Untargeted Nuclear Magnetic Resonance Analysis

Stroke remains the second leading cause of mortality worldwide, and the third leading cause of death and morbidity combined, affecting more than 12 million people every year. Stroke pathophysiology results from complex interactions of several risk factors related to age, family history, gender, lifestyle, and the presence of cardiovascular and metabolic diseases. Despite all the evidence, it is not possible to fully prevent stroke onset. In recent years, there has been an exploration of innovative methodologies for metabolite analysis aimed at identifying novel stroke biomarkers. Utilizing Nuclear Magnetic Resonance (NMR) spectroscopy, we investigated small molecule variations in urine across different stages of stroke risk. The Framingham Stroke Risk Score was used in people over 63 years of age living in long-term care facilities (LTCFs) to calculate the probability of suffering a stroke: low stroke risk (LSR, control), moderate stroke risk (MSR), and high stroke risk (HSR). Univariate statistical analysis showed that urinary 4-hydroxyphenylacetate levels increased while glycolate levels decreased across the different stroke risk groups, from the LSR to the HSR groups. Trimethylamine N-oxide (TMAO) had average concentration values that were significantly higher in elderly people in the HSR group, while trigonelline levels were significantly lower in the MSR group. These metabolic markers can be used for early detection and to differentiate stages of stroke risk more efficiently.

Systems-level computational modeling in ischemic stroke: from cells to patients

Ischemic stroke, a significant threat to human life and health, refers to a class of conditions where brain tissue damage is induced following decreased cerebral blood flow. The incidence of ischemic stroke has been steadily increasing globally, and its disease mechanisms are highly complex and involve a multitude of biological mechanisms at various scales from genes all the way to the human body system that can affect the stroke onset, progression, treatment, and prognosis. To complement conventional experimental research methods, computational systems biology modeling can integrate and describe the pathogenic mechanisms of ischemic stroke across multiple biological scales and help identify emergent modulatory principles that drive disease progression and recovery. In addition, by running virtual experiments and trials in computers, these models can efficiently predict and evaluate outcomes of different treatment methods and thereby assist clinical decision-making. In this review, we summarize the current research and application of systems-level computational modeling in the field of ischemic stroke from the multiscale mechanism-based, physics-based and omics-based perspectives and discuss how modeling-driven research frameworks can deliver insights for future stroke research and drug development.

Diagnosing cancer-associated ischemic stroke: A systematic review of hematological biomarkers

BACKGROUND AND AIM

Patients suffering from cancer are reported to have an increased risk of ischemic stroke (IS). We aimed to identify cancer-associated biomarkers found to differentiate between IS associated with cancer from those not associated with cancer.

SUMMARY OF REVIEW

We performed a systematic search of PubMed and EMBASE databases according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The study is reported in PROSPERO (#CRD42022355129). In total, 5563 papers were screened, of which 49 papers were included. Seven biomarkers were identified which had the potential to differentiate between patients who had cancer or stroke or both conditions. D-dimer was the most frequently monitored biomarker, and high levels were significantly associated with cancer-related strokes in (42/44) studies. Fibrinogen was significantly associated with cancer-related strokes in 11/27 studies. A higher level of C-reactive protein, investigated in 19 studies, was associated with cancer-related strokes, but conclusive multivariate analysis was not performed. Finally, the four cancer-associated antigens CA125, CA153, CA199, and carcinoembryonic antigen were only reported on in three to six studies, respectively. These studies all originated from the Guangxi province in China. CA125 was associated with an increased risk of IS in four of six studies.

CONCLUSION

Increased D-dimer seems associated with cancer-related IS. CRP may also be a candidate as a cancer-associated stroke biomarker, but this requires further verification. Fibrinogen and the more specific cancer biomarkers have not yet been proven helpful for detecting cancer-related strokes.

Advanced Mass Spectrometry-Based Biomarker Identification for Metabolomics of Diabetes Mellitus and Its Complications

Over the years, there has been notable progress in understanding the pathogenesis and treatment modalities of diabetes and its complications, including the application of metabolomics in the study of diabetes, capturing attention from researchers worldwide. Advanced mass spectrometry, including gas chromatography-tandem mass spectrometry (GC-MS/MS), liquid chromatography-tandem mass spectrometry (LC-MS/MS), and ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-Q-TOF-MS), etc., has significantly broadened the spectrum of detectable metabolites, even at lower concentrations. Advanced mass spectrometry has emerged as a powerful tool in diabetes research, particularly in the context of metabolomics. By leveraging the precision and sensitivity of advanced mass spectrometry techniques, researchers have unlocked a wealth of information within the metabolome. This technology has enabled the identification and quantification of potential biomarkers associated with diabetes and its complications, providing new ideas and methods for clinical diagnostics and metabolic studies. Moreover, it offers a less invasive, or even non-invasive, means of tracking disease progression, evaluating treatment efficacy, and understanding the underlying metabolic alterations in diabetes. This paper summarizes advanced mass spectrometry for the application of metabolomics in diabetes mellitus, gestational diabetes mellitus, diabetic peripheral neuropathy, diabetic retinopathy, diabetic nephropathy, diabetic encephalopathy, diabetic cardiomyopathy, and diabetic foot ulcers and organizes some of the potential biomarkers of the different complications with the aim of providing ideas and methods for subsequent in-depth metabolic research and searching for new ways of treating the disease.

Strategy to Empower Nontargeted Metabolomics by Triple-Dimensional Combinatorial Derivatization with MS-TDF Software

Chemical derivatization is a widely employed strategy in metabolomics to enhance metabolite coverage by improving chromatographic behavior and increasing the ionization rates in mass spectroscopy (MS). However, derivatization might complicate MS data, posing challenges for data mining due to the lack of a corresponding benchmark database. To address this issue, we developed a triple-dimensional combinatorial derivatization strategy for nontargeted metabolomics. This strategy utilizes three structurally similar derivatization reagents and is supported by MS-TDF software for accelerated data processing. Notably, simultaneous derivatization of specific metabolite functional groups in biological samples produced compounds with stable but distinct chromatographic retention times and mass numbers, facilitating discrimination by MS-TDF, an in-house MS data processing software. In this study, carbonyl analogues in human plasma were derivatized using a combination of three hydrazide-based derivatization reagents: 2-hydrazinopyridine, 2-hydrazino-5-methylpyridine, and 2-hydrazino-5-cyanopyridine (6-hydrazinonicotinonitrile). This approach was applied to identify potential carbonyl biomarkers in lung cancer. Analysis and validation of human plasma samples demonstrated that our strategy improved the recognition accuracy of metabolites and reduced the risk of false positives, providing a useful method for nontargeted metabolomics studies. The MATLAB code for MS-TDF is available on GitHub at https://github.com/CaixiaYuan/MS-TDF.

Advancing stroke therapy: the potential of MOF-based nanozymes in biomedical applications

In this study, we explored the growing use of metal-organic framework (MOF)-based Nanozymes in biomedical research, with a specific emphasis on their applications in stroke therapy. We have discussed the complex nature of stroke pathophysiology, highlighting the crucial role of reactive oxygen species (ROS), and acknowledging the limitations of natural enzymes in addressing these challenges. We have also discussed the role of nanozymes, particularly those based on MOFs, their structural similarities to natural enzymes, and their potential to improve reactivity in various biomedical applications. The categorization of MOF nanozymes based on enzyme-mimicking activities is discussed, and their applications in stroke therapy are explored. We have reported the potential of MOF in treating stroke by regulating ROS levels, alleviation inflammation, and reducing neuron apoptosis. Additionally, we have addressed the challenges in developing efficient antioxidant nanozyme systems for stroke treatment. The review concludes with the promise of addressing these challenges and highlights the promising future of MOF nanozymes in diverse medical applications, particularly in the field of stroke treatment.

From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.

Simultaneously quantifying hundreds of acylcarnitines in multiple biological matrices within ten minutes using ultrahigh-performance liquid-chromatography and tandem mass spectrometry

Acylcarnitines are metabolic intermediates of fatty acids and branched-chain amino acids having vital biofunctions and pathophysiological significances. Here, we developed a high-throughput method for quantifying hundreds of acylcarnitines in one run using ultrahigh performance liquid chromatography and tandem mass spectrometry (UPLC-MS/MS). This enabled simultaneous quantification of 1136 acylcarnitines (C0-C26) within 10-min with good sensitivity (limit of detection < 0.7 fmol), linearity (correlation coefficient > 0.992), accuracy (relative error < 20%), precision (coefficient of variation (CV), CV < 15%), stability (CV < 15%), and inter-technician consistency (CV < 20%, n = 6). We also established a quantitative structure-retention relationship (goodness of fit > 0.998) for predicting retention time (tR) of acylcarnitines with no standards and built a database of their multiple reaction monitoring parameters (tR, ion-pairs, and collision energy). Furthermore, we quantified 514 acylcarnitines in human plasma and urine, mouse kidney, liver, heart, lung, and muscle. This provides a rapid method for quantifying acylcarnitines in multiple biological matrices.

The identification of novel stroke-related sphingolipid biomarkers using UPLC-MS/MS

BACKGROUND

Stroke is a prominent contributor to global mortality and morbidity, thus necessitating the establishment of dependable diagnostic indicators. The objective of this study was to ascertain metabolites linked to sphingolipid metabolism and assess their viability as diagnostic markers for stroke.

METHODS

Two cohorts, consisting of 56 S patients and 56 healthy volunteers, were incorporated into this investigation. Metabolite data was obtained through the utilization of Ultra Performance Liquid Chromatography and Tandem Mass Spectrometry (UPLC-MS/MS). The mass spectrometry data underwent targeted analysis and quantitative evaluation utilizing the multiple reaction monitoring mode of triple quadrupole mass spectrometry. Various data analysis techniques, including Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA), least absolute shrinkage and selection operator (LASSO) regression, Support Vector Machine (SVM), logistic regression, and Receiver Operating Characteristic (ROC) curves were employed.

RESULTS

A comprehensive analysis detected a total of 129 metabolites related to sphingolipid metabolism, encompassing ceramides, 1-phosphoceramides, phytoceramides, glycosphingolipids, sphingomyelins, and sphingomyelins. The implementation of OPLS-DA analysis revealed significant disparities between individuals with stroke and controls, as it successfully identified 31 metabolites that exhibited significant differential expression between the two groups. Furthermore, functional enrichment analysis indicated the participation of these metabolites in diverse biological processes. Six metabolic markers, namely CerP(d18:1/20:3), CerP(d18:1/18:1), CerP(d18:1/18:0), CerP(d18:1/16:0), SM(d18:1/26:1), and Cer(d18:0/20:0), were successfully validated as potential diagnostic markers for stroke. The utilization of ROC analysis further confirmed their diagnostic potential, while a logistic regression model incorporating these markers demonstrated robust efficacy in distinguishing stroke patients from healthy controls.

CONCLUSION

these identified metabolic markers exhibit clinical significance and hold promise as valuable tools for the diagnosis of stroke.