Mechanism Exploration of Youth Ischemic Stroke by Metabolomics Analysis

doi:10.21203/rs.3.rs-1595016/v1

Download PDF

Research Article

Mechanism Exploration of Youth Ischemic Stroke by Metabolomics Analysis

https://doi.org/10.21203/rs.3.rs-1595016/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

The onset age of acute ischemic stroke (IS) is gradually younger, which brings great challenges and burden to the medical system. For stroke patients, time is the brain cells, so any method contributes to rapid identification or diagnosis of stroke is extremely meaningful. Metabolomics analysis provides us with new means and ideas for discovering potential stroke-related biomarkers and understanding the pathogenesis of youth IS. In this study, fifty-five patients with IS were assigned in two groups(senior or junior) according to the age(60-year-old) and were performed serum metabolomics analysis based on Liquid Chromatography coupled to tandem Mass Spectrometry (LC-MS) system. Forty differential metabolites were selected, mainly ceramides, lipids, and amino acids, which are mainly related to aging, cardiovascular and cerebrovascular diseases, and antitumor mechanisms. The pathway analysis showed that the differential metabolites were mainly enriched in sphingolippid metabolism, glycine metabolism, serine and threonine metabolism, glycerol phospholipid metabolism, biosynthesis of unsaturated fatty acids, linoleic acid metabolism, and vitamin B6 metabolism. Our study suggests that the mechanism of IS in young adults was most likely associated with elevated plasma lactose ceramide levels and abnormal sphingolipid metabolic pathway.

Ischemic Stroke

Metabolomics

Lactose Ceramide

Mechanism

Young Adults

Ischemic stroke (IS) is caused by cerebrovascular stenosis and occlusion that lead to the disorders of oxygen and nutrient supply for the brain tissue, which is followed by a succession of pathophysiological changes for clinical symptoms[1–2]. China is facing the biggest challenge in stroke worldwide. As the global burden of disease (GBD) 2016 stoke study estimated, the highest lifetime risk of stroke from the age of 25 years is Chinese (39.3%)[3], which means that stroke is no longer considered a disease of the elderly. In 2018, the number of deaths of stroke was 1.570 million which ranked third among the leading causes of death behind malignant tumours and heart disease[4]. Data from the Hospital Quality Monitoring System showed that IS accounts for 81.9% inpatients with stroke during 2018[4]. Previous studies had shown that smoking, hypertension, and diabetes are common risk factors for IS[5–6]. However, the reason of young patients with IS remains unclear, suggesting an insufficient understanding of the pathogenesis of youth IS[7–8].

The diagnosis of IS is mainly based on computed tomography (CT), magnetic resonance imaging (MRI), and angiographic[9], which are high sensitive, but time consuming or device-dependent[10–11]. The longer hypoxia and ischemia, the higher the risk of permanent brain injury and chronic disability. Therefore, rapid and accurate diagnostic biomarkers are needed to be well developed.

Metabolomics analysis is an emerging science devoted to the quantitative assessment of small-molecule metabolites in a given biological sample, thus to excavate the link between metabolic features and disease phenotypes[12]. It adequately addresses the problem of blood-brain barrier and disease heterogeneity[13], serving as an ideal method for discovering IS biomarker. Metabolomics analysis has shown great promise and success in predicting risk, but none marker is actually used in the clinical diagnosis or treatment of stroke patients. This study aims to explore the biomarkers of youth IS by analysing different metabolites among junior and senior groups. As a result, pave the way to the key pathogenesis of IS.

Clinical Data Collection

Patients were eligible if they had presented clinical symptoms of acute IS within seven days and confirmed by brain imaging with CT or MRI, complying with the diagnostic criteria according to the recommendations of AHA/ASA[14]. Exclusion criteria were: patients with serious organ failure; malignancy carcinoma; autoimmune diseases; metabolic disease (e.g. hypothyroidism); patients refused to provide consent.

The national institutes of health stroke scale (NIHSS) score was calculated at admission to assess the severity of stroke. The basic clinical information including gender, age, blood pressure, medical history, and auxiliary examination.

Sample Collection and Metabolomics Detection

Human plasma (whole blood) samples were collected for QE-based metabolomics analysis. The samples was collected into heparin tubes by venipuncture after an overnight fasting. Plasma was immediately separated by centrifugation at 3000 rpm for 10 min, and then stored at –80℃ until Liquid Chromatography coupled to tandem Mass Spectrometry (LC-MS) detection.

Dunn, W.B. et al.and Want, E.J. et al had previously described the detailed accounts of LC-MS detection principle[15-16]. Samples were slowly thawed at 4℃ and then were placed in a centrifuge at 4,000 rpm for 5 min. After mixing 200μL of the supernatant liquid with 600μL of methanol, the mixture was exposed to ultrasonics for 30 min. After a second centrifugation, the new supernatant liquid was dried under a mild flow of auxiliary gas, then added to 200μL of pure methanol for redissolve and transferred into the sample vial after adding the internal label (1 mg/ml of dichloro- phenylalanine, 5μL). Plasma metabolomics data was collected on a column (ACQUITY UPLC HSS T3 (2.1*100mm 1.8 μm)) combined with LC-MS (Waters, UPLC; Thermo, Q Exactive) analysis platform in the following parameters: heater temperature,300℃; sheath gas flow rate,45 arb; auxiliary gas flow rate,15 arb; sweep gas flow rate,1 arb; capillary temperature,350℃. The spray voltage was 3.0 KV in the electron spray ionization (ESI)+ modes and that was 3.2 KV in the ESI- modes; S-Lens RF level was 30% and 60%, respectively.

The Quality Control (QC) samples, a mixture of equal volume of the trial samples,were used for LC-MS analysis before, between, and after the trial samples.The detection process that adding one QC sample per 10 trail samples was taken to ensure the reliability and reproducibility of the analysis results.

Statistical Analysis

Continuous variable was expressed as mean±SD, median and interquartile range according to its distribution. Student t test or nonparametric test were used to examine the difference between two groups. Categorical variable was shown as percentage, and the Chi-square test was used to examine the difference between groups. Statistical analyses were performed using IBM SPSS 18.0. The reported P-value was two-tailed, and P＜0.05 was considered to be statistically significant.

Metabolomics data analysis was based on the LC-MS system combined quadrupole Orbitrap mass spectrometer (Q Exactive Orbitrap, Thermo Fisher Scientific, USA)[17-18], including ESI+ model and ESI- model. Initial data from LC-MS detection were import into the Compound Discoverer3.1 (CD) software for spectrograph-progress and database-search to obtain the qualitative and quantitative results of the metabolites.

After data management of single-peak filtration, missing value recording, and normalization, data formatting and modeling were performed for the Multivariate Analysis (MVA) by SIMCA-P software (Umetrics AB, Umea, Sweden)[19]. Principal component analysis (PCA) and orthogonal projections to latent structures-discriminant analysis (OPLS-DA)[20] were taken to discover more reliable differential metabolites between two groups. Traditional univariate analysis (UVA) methods (e.g. Student t-test) pay more attention to independent changes of metabolite level while MVA methods concentrate on the relationship in biological processes (promotion / antagonistic) between metabolites. Combining two type of methods for filter can help to avoid false positive errors or model overfitting caused by using only one class of statistical analysis methods[21-22].

Substances that met the next two criteria were selected as differential metabolites: P value of Student’s t text was less than 0.05 and Variable Importance in the Projection (VIP) of the OPLS-DA model was more than 1.0. The results of differential metabolites was visualized in the form of volcano plot. In addition, commercial databases including Kyoto Encyclopedia of Genes and Genomes(KEGG) (http://www.genome.jp/kegg/)and MetaboAnalyst(http://www.metaboanalyst.ca/) were utilized to search for the meaningful pathways of metabolites[23].

The whole experimental process is shown in Figure 1.

Basic Characteristics of Patients

Fifty-five patients who met the trial criteria were included from November 30,2020 to March 27,2021. Patients were divided into junior group (age ≤60 years old, N=24) and senior group (age ＞60 years old, N=31).The baseline demographic and disease characteristics are balanced between groups (Table 1). There are no significant differences between the junior and the senior group in sex, medical history, principle auxiliary examination, systollc blood pressure, rate of thrombolysis and NIHSS score.

Table 1. Descriptive Statistics of Study Population
Variables	junior	senior	P value
Number	24	31
Demographic characteristics
Age,y,mean(SD)	53.4±5.2	72.1±7.0	<0.001
Female,%	9(37.5)	14(45.2)	0.568
Medical history
Hypertenion,%	14(58.3)	16(51.6)	0.620
Diabetes melitus,%	8(33.3)	7(22.6)	0.375
Smoking,%	5(20.8)	6(19.4)	1.000
Drinking,%	4(16.7)	6(19.4)	1.000
Previous cardiovascular and cerebrovascular diseases,%	4(16.7)	9(29.0)	0.284
Auxiliary examination
Carotid atherosclerosis,%	15(62.5)	21(67.7)
Fasting blood-glucose,mean(SD)	6.6±2.2	7.2±4.5	0.547
TG,mean(SD)	1.7±1.2	1.7±1.1	0.959
TC,mean(SD)	4.6±0.6	4.4±1.0	0.352
LDL,mean(SD)	2.8±0.7	2.6±1.1	0.401
Blood pressure characteristics
Systollc blood pressure mmHg,mean(SD)	157.5±22.8	151.1±19.8	0.276
Dlastollc blood pressure mmHg,mean(SD)	94.3±13.9	85.6±10.9	0.013
Thrombolysis,%	5(20.8)	12(38.7)	0.155
NIHSS,mean(SD)	6.88(5.1)	6.84(6.3)	0.982
0-1,%	3(12.5)	3(9.7)
2-4,%	6(25.0)	10(32.3)
5-15,%	14(58.3)	14(45.2)
16-20,%	1(4.2)	2(6.5)
21-42,%	0(0.0)	2(6.5)

Multivariate Statistic Analysis

The PCA score plots show no significant difference between the group junior and senior, with R2X[1]=0.09 and R2X[2]=0.082 in ESI+ mode(Figure 2a) and R2X[1]=0.129 and R2X[2]=0.078 in ESI- mode(Figure 2b). The quality control samples are closely gathered together in both the ESI+ and ESI- modes, indicating that the analysis system and the results are stable and reliable.

The statistical method of OPLS-DA is used to model the relationship between metabolite expression and sample category for the prediction of sample category. Both the permutation test results of OPLS-DA model in ESI+ and ESI- mode show the significant discrepancy between two groups with the samples all within the 95% confidence interval (hotelling's t-squared ellipse). Q2=-0.97 in ESI+ mode(Figure 2c), and Q2=-1.03 in ESI- mode(Figure 2d). The results reveal a good robustness of the model with no overfitting existence.

Differential Metabolites Analysis

Under the filter strategy of Univariate Analysis combined with Multivariate Analysis, 40 differential metabolites are selected (VIP>1.0, P<0.05), including 18 from ESI+ mode and 22 from ESI- mode which are visualized by volcano plots(Figure 3a, 3b). The differential metabolites are mainly ceramides, lipids, and amino acids, which are mainly related to aging, cardiovascular diseases, cerebrovascular diseases, and antitumor mechanisms.

Fourteen differential metabolites with VIP> 2 are shown in Table 2, including seven from both ESI+ and ESI- mode. In the ESI+ mode, C16 lactosyl ceramide, hexadecanamide, and acetyl-L-carnitine are up-regulated in the junior group, while dihydrosphingosine, 2-amino-1,3,4-octadecanetriol, arachidoyl ethanolamide, and choline are down-regulated compared to the senior group. In the ESI- mode, testosterone sulfate is up-regulated, while carnitine hydrochloride, tretinoin, phosphatidylinositol, (R)-2-hydroxystearic acid, eicosatrienoic acid, and linoleic acid are down-regulated.

Table 2. Results of Differential Metabolites (VIP> 2)
MS2 NAME	CHANGE	VIP	P-VALUE	FOLD CHANGE	ESI
Dihydrosphingosine	↓	2.71961	0.001949	0.671091886	+
2-Amino-1,3,4-octadecanetriol	↓	2.70002	0.002341	0.617616494	+
C16 Lactosyl Ceramide (d18:1/16:0)	↑	2.62468	0.025526	3.529571713	+
Hexadecanamide	↑	2.60350	0.004725	1.415260653	+
Arachidoyl Ethanolamide	↓	2.56377	0.004757	0.606560793	+
Acetyl-L-carnitine	↑	2.53171	0.009356	2.740594559	+
Choline	↓	2.18583	0.011085	0.855065369	+
Carnitine (Dl) Hydrochloride	↓	2.90806	0.002970	0.611185518	-
Tretinoin	↓	2.73664	0.004504	0.59221435	-
Testosterone sulfate	↑	2.49990	0.001125	2.078929089	-
Phosphatidylinositol lyso 18:0	↓	2.32769	0.043117	0.831578852	-
(R)-2-hydroxystearic acid	↓	2.26779	0.002748	0.775020864	-
Eicosatrienoic acid	↓	2.18754	0.011764	0.760404101	-
Linoleic acid	↓	2.15654	0.003687	0.57008088	-

Metabolic Pathway Analysis

All differential metabolites were performed KEGG annotation analysis and comprehensive metabolic pathway analyses (including enrichment analysis and topological analysis) for searching key pathways which were the highest correlation with metabolite differences. The result show that in ESI+ mode(Figure 4a), differentially expressed compounds mainly concentrate on the metabolic pathways of sphingolipid metabolism, glycine, serine and threonine metabolism, and glycerophospholipid metabolism. In ESI- mode(Figure 4b), the differences are mainly enriched in pathway of biosynthesis of unsaturated fatty acids, linoleic acid metabolism, and vitamin B6 metabolism.

The incidence of IS in young adults is increasing gradually, which occurred with a considerably wider range of risk factors than older patients. More than 150 causes of early-onset IS has been identified, but the specific pathogenesis remains unclear[24]. This study intends to explore molecular mechanism and screen biomarkers of youth stroke via metabolomics data analysis based on LC-MS/MS system. Totally, 40 differential metabolites were selected, including 14 with VIP value> 2 and Student's t-test P value <0.05, which mainly were ceramides, lipids, and amino acids. After database search and article analysis, elevated plasma level of lactose ceramide and abnormal sphingolipid metabolism pathway were considered the most likely mechanism associated with the onset of stroke in young patients.

Experimental evidence is accumulating which suggesting a crucial role of sphingolipids in the pathogenesis of IS. Glycosphingolipids (GSLs) are important structural molecules constituting the cell membrane and siganl transduction regulators producing a variety of different biological functions[25]. Ceramides has been proved not only to regulate cell proliferation, differentiation, senescence and apoptosis, but also to participate in cellular stress responses[26]. In particular, several different animal models of ischemia/reperfusion injury have shown that ceramide accumulation in ischemic tissue might be a crucial trigger of apoptosis.

Endogenous ceramides are generated by three different biochemical pathways[27]: 1) Ceramides can be produced via the de novo synthesis involving several catalytic steps which fianlly N-acylation of sphinganine to convert ceramide; 2) Ceramide can also be formed via the salvage pathway by re-acylation of sphingoid long chain bases, such as sphingosin; 3) Ceramides can be generated through hydrolysis of complex sphingolipids. In our study, dihydrosphingosine and 2-amino-1,3,4-octadecanetriol (phytosphingosine), both synthetic precursors of ceramide[28], were significantly reduced in young patients, whereas C16 lactosyl ceramide was significantly increased. It indicates more sphingolipid precursors might be used to synthesize lactoceramide (LacCer) in junior group than senior group, which may contribute to the onset of youth IS.

Edsfeldt, Andreas et al. analyzed several GSLs in homogenates from 200 human carotid plaques using mass spectrometry and discovered that glucosylceramide, lactosylceramide, ceramide, dihydroceramide, sphingomyelin, and sphingosine-1-phosphate (S1P) were all significantly increased in symptom-associated plaques compared with plaques from asymptomatic patients[29]. Their study also revealed that increased level of ceramides were correlated with inflammatory cytokines and served as histological markers of plaque instability. However, unstable plaques were considered to be highrisk and prone to thrombotic complication, such as cerebral emboli caused ischemic events[30].

It has already been shown that critical effects of ceramides in various neurodegenerative and inflammatory diseases are physiological condition-exerted and chain length-specific[31]. Alterations of long chain ceramides (≧C16), as well as their respective dihydro-ceramides and precursors, were regulators in apoptotic cell death[32]. Elevated plasma levels of ceramides were predictors of both risk and severity at admission in IS patients, which was confirmed by the study of 202 age and sex matched control patients[33]. Moreover, the serum level of LacCer was strong correlated with arterial stiffness and could be used to indicate vascular dysfunction. Rabbits fed high fat and high cholesterol diet showed a marked increase of LacCer accompanied by extensive atherosclerosis, which was prevented by treatment of the glycosphingolipid glycosyltransferase inhibitor D-PDMP[34]. Kim, Minjoo et al. revealed LacCer were independent predictors of increased arterial stiffness in middle-aged individuals[35].

Further study has elucidated that LacCer stimulates the GTP load on the Ras to initiate cell proliferation via a series of signal transductions which results in the phosphorylation of MAPK p44 [36]. In human smooth muscle cell (SMC), oxidative LDL (ox-LDL) could dose-dependently activate the synthesis of LacCer by rapid phosphorylation of LacCer synthetase[37]. It was reported that D-PDMP reduced ox-LDL guided SMC multiplication[38], indicating that the activation of LacCer synthetase and the increased of LacCer were essential to ox-LDL induced arterial SMC proliferation.

Deguchi, Hiroshi et al. reported a new mechanism for cross-talk between sphingolipid metabolism and thrombin generation[39], which implied sphingosine disrupted interactions between factor Va and the Gla domain of factor Xa in the prothrombinase complex. Thus, certain sphingolipids might be bioactive lipid mediators of thrombin generation to modulate cell growth and death, blood coagulation, and inflammation. Certain factors induced hypercoagulability were high risk factors of thromboembolic diseases[40]. The role of down-regulatied sphingosine in coagulation state and in onset of youth IS need to be furhter studied.

Generally, the onset of youth IS maybe caused by up-regualted lactoceramide and abnormal sphingolipid metabolism, which are correlate with plaque instability, arterial stiffness, cell proliferation, high sensitive of ox-LDL and hypercoagulability. Our result indicates a potential biomarker for the early onset of IS, but the potential mechanism needs more further studies for confirmation.

Limitation

The present study has some limitations. The sample size was relatively small, which might limit the generalizability of the results. This study is a prospective cohort study of single-center and the current findings are preliminary, which needs further validation in more control cohort studies. The relationship between lactose ceramide and the mechanism of young stroke still needs to be verified by further precise studies.

Ischemic Stroke

Liquid Chromatography

Mass Spectrometry

ESI

Electron Spray Ionization

Quadrupole Exactive Orbitrap

Quality Control

VIP

Variable Importance in the Projection

There are still many spaces need to be explored about the pathogenesis of IS, and the development of untargeted metabolomics provides us a better method to further investigate relevant mechanisms. This study analysed differential plasma circulation metabolites in 24 young and 31 elderly stroke patients and revealed that the disorder metabolism of lactose ceramide and the abnormal sphingolipid metabolic pathway is most likely to be associated with the mechanism of early onset of stroke.

Ethics approval

This study was conducted in accordance with the principles of the Declaration of Helsinki and had been approved by the Ethics Committee of Changhai Hospital.

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Consent for publication

Not applicable

Availability of data and materials

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors have no relevant financial or non-financial interests to disclose and have no conflict of interest.

Funding

This work was supported by Shanghai Natural Foundation (No.19ZR1478300).

Authors' contributions

Jian Zhou and Jian Dong contributed to the study conception and design. Material preparation, data collection and analysis were performed by Fude Wang, Pengcheng Du, Zilin Lu and Sheng Chang. The first draft of the manuscript was written and polished by Xianfei Liu and Shuangshuang Li, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Acknowledgements

Throughout the writing of this dissertation I have received a great deal of support and assistance. I would first like to thank my supervisor, Jian Zhou, for his guidance through each stage of the process. I would particularly like to thank my tutors, Jian Dong and Shuangshuang Li, whose expertise was invaluable in formulating the research questions and methodology. In addition, I would like to acknowledge my teammates for their wonderful collaboration and patient support.

Tuttolomondo, A., Puleo, M. G., Velardo, M. C., Corpora, F., Daidone, M., & Pinto, A. (2020). Molecular Biology of Atherosclerotic Ischemic Strokes. International journal of molecular sciences, 21(24), 9372. https://doi.org/10.3390/ijms21249372
George, P. M., & Steinberg, G. K. (2015). Novel Stroke Therapeutics: Unraveling Stroke Pathophysiology and Its Impact on Clinical Treatments. Neuron, 87(2), 297–309. https://doi.org/10.1016/j.neuron.2015.05.041
GBD 2016 Lifetime Risk of Stroke Collaborators, Feigin, V. L., Nguyen, G., Cercy, K., Johnson, C. O., Alam, T., Parmar, P. G., Abajobir, A. A., Abate, K. H., Abd-Allah, F., Abejie, A. N., Abyu, G. Y., Ademi, Z., Agarwal, G., Ahmed, M. B., Akinyemi, R. O., Al-Raddadi, R., Aminde, L. N., Amlie-Lefond, C., Ansari, H., … Roth, G. A. (2018). Global, Regional, and Country-Specific Lifetime Risks of Stroke, 1990 and 2016. The New England journal of medicine, 379(25), 2429–2437. https://doi.org/10.1056/NEJMoa1804492
Wang, Y. J., Li, Z. X., Gu, H. Q., Zhai, Y., Jiang, Y., Zhao, X. Q., Wang, Y. L., Yang, X., Wang, C. J., Meng, X., Li, H., Liu, L. P., Jing, J., Wu, J., Xu, A. D., Dong, Q., Wang, D., Zhao, J. Z., & China Stroke Statistics 2019 Writing Committee (2020). China Stroke Statistics 2019: A Report From the National Center for Healthcare Quality Management in Neurological Diseases, China National Clinical Research Center for Neurological Diseases, the Chinese Stroke Association, National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention and Institute for Global Neuroscience and Stroke Collaborations. Stroke and vascular neurology, 5(3), 211–239. https://doi.org/10.1136/svn-2020-000457
Boehme, A. K., Esenwa, C., & Elkind, M. S. (2017). Stroke Risk Factors, Genetics, and Prevention. Circulation research, 120(3), 472–495. https://doi.org/10.1161/CIRCRESAHA.116.308398
Maaijwee, N. A., Rutten-Jacobs, L. C., Schaapsmeerders, P., van Dijk, E. J., & de Leeuw, F. E. (2014). Ischaemic stroke in young adults: risk factors and long-term consequences. Nature reviews. Neurology, 10(6), 315–325. https://doi.org/10.1038/nrneurol.2014.72
Poisson, S. N., Schardt, T. Q., Dingman, A., & Bernard, T. J. (2014). Etiology and treatment of arterial ischemic stroke in children and young adults. Current treatment options in neurology, 16(10), 315. https://doi.org/10.1007/s11940-014-0315-4
von Sarnowski, B., Putaala, J., Grittner, U., Gaertner, B., Schminke, U., Curtze, S., Huber, R., Tanislav, C., Lichy, C., Demarin, V., Basic-Kes, V., Ringelstein, E. B., Neumann-Haefelin, T., Enzinger, C., Fazekas, F., Rothwell, P. M., Dichgans, M., Jungehulsing, G. J., Heuschmann, P. U., Kaps, M., … sifap1 Investigators (2013). Lifestyle risk factors for ischemic stroke and transient ischemic attack in young adults in the Stroke in Young Fabry Patients study. Stroke, 44(1), 119–125. https://doi.org/10.1161/STROKEAHA.112.665190
Martinez, G., Katz, J. M., Pandya, A., Wang, J. J., Boltyenkov, A., Malhotra, A., Mushlin, A. I., & Sanelli, P. C. (2021). Cost-Effectiveness Study of Initial Imaging Selection in Acute Ischemic Stroke Care. Journal of the American College of Radiology : JACR, 18(6), 820–833. https://doi.org/10.1016/j.jacr.2020.12.013
Shen, J., Li, X., Li, Y., & Wu, B. (2017). Comparative accuracy of CT perfusion in diagnosing acute ischemic stroke: A systematic review of 27 trials. PloS one, 12(5), e0176622. https://doi.org/10.1371/journal.pone.0176622
Ludewig, P., Gdaniec, N., Sedlacik, J., Forkert, N. D., Szwargulski, P., Graeser, M., Adam, G., Kaul, M. G., Krishnan, K. M., Ferguson, R. M., Khandhar, A. P., Walczak, P., Fiehler, J., Thomalla, G., Gerloff, C., Knopp, T., & Magnus, T. (2017). Magnetic Particle Imaging for Real-Time Perfusion Imaging in Acute Stroke. ACS nano, 11(10), 10480–10488. https://doi.org/10.1021/acsnano.7b05784
Nicholson, J. K., & Lindon, J. C. (2008). Systems biology: Metabonomics. Nature, 455(7216), 1054–1056. https://doi.org/10.1038/4551054a
Makris, K., Haliassos, A., Chondrogianni, M., & Tsivgoulis, G. (2018). Blood biomarkers in ischemic stroke: potential role and challenges in clinical practice and research. Critical reviews in clinical laboratory sciences, 55(5), 294–328. https://doi.org/10.1080/10408363.2018.1461190
Sacco, R. L., Kasner, S. E., Broderick, J. P., Caplan, L. R., Connors, J. J., Culebras, A., Elkind, M. S., George, M. G., Hamdan, A. D., Higashida, R. T., Hoh, B. L., Janis, L. S., Kase, C. S., Kleindorfer, D. O., Lee, J. M., Moseley, M. E., Peterson, E. D., Turan, T. N., Valderrama, A. L., Vinters, H. V., … Council on Nutrition, Physical Activity and Metabolism (2013). An updated definition of stroke for the 21st century: a statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke, 44(7), 2064–2089. https://doi.org/10.1161/STR.0b013e318296aeca
Dunn, W. B., Broadhurst, D., Begley, P., Zelena, E., Francis-McIntyre, S., Anderson, N., Brown, M., Knowles, J. D., Halsall, A., Haselden, J. N., Nicholls, A. W., Wilson, I. D., Kell, D. B., Goodacre, R., & Human Serum Metabolome (HUSERMET) Consortium (2011). Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature protocols, 6(7), 1060–1083. https://doi.org/10.1038/nprot.2011.335
Want, E. J., Wilson, I. D., Gika, H., Theodoridis, G., Plumb, R. S., Shockcor, J., Holmes, E., & Nicholson, J. K. (2010). Global metabolic profiling procedures for urine using UPLC-MS. Nature protocols, 5(6), 1005–1018. https://doi.org/10.1038/nprot.2010.50
Wang, J., Christison, T. T., Misuno, K., Lopez, L., Huhmer, A. F., Huang, Y., & Hu, S. (2014). Metabolomic profiling of anionic metabolites in head and neck cancer cells by capillary ion chromatography with Orbitrap mass spectrometry. Analytical chemistry, 86(10), 5116–5124. https://doi.org/10.1021/ac500951v
Xiao, J. F., Zhou, B., & Ressom, H. W. (2012). Metabolite identification and quantitation in LC-MS/MS-based metabolomics. Trends in analytical chemistry : TRAC, 32, 1–14. https://doi.org/10.1016/j.trac.2011.08.009
Wiklund, S., Johansson, E., Sjöström, L., Mellerowicz, E. J., Edlund, U., Shockcor, J. P., Gottfries, J., Moritz, T., & Trygg, J. (2008). Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models. Analytical chemistry, 80(1), 115–122. https://doi.org/10.1021/ac0713510
Alvarez-Zapata, R., Sánchez-Medina, A., Chan-Bacab, M., García-Sosa, K., Escalante-Erosa, F., García-Rodríguez, R. V., & Peña-Rodríguez, L. M. (2015). Chemometrics-enhanced high performance liquid chromatography-ultraviolet detection of bioactive metabolites from phytochemically unknown plants. Journal of chromatography. A, 1422, 213–221. https://doi.org/10.1016/j.chroma.2015.10.026
Tzoulaki, I., Ebbels, T. M., Valdes, A., Elliott, P., & Ioannidis, J. P. (2014). Design and analysis of metabolomics studies in epidemiologic research: a primer on -omic technologies. American journal of epidemiology, 180(2), 129–139. https://doi.org/10.1093/aje/kwu143
Richardson, T. G., Sanderson, E., Palmer, T. M., Ala-Korpela, M., Ference, B. A., Davey Smith, G., & Holmes, M. V. (2020). Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: A multivariable Mendelian randomisation analysis. PLoS medicine, 17(3), e1003062. https://doi.org/10.1371/journal.pmed.1003062
Xia, J., Sinelnikov, I. V., Han, B., & Wishart, D. S. (2015). MetaboAnalyst 3.0--making metabolomics more meaningful. Nucleic acids research, 43(W1), W251–W257. https://doi.org/10.1093/nar/gkv380
Putaala J. (2020). Ischemic Stroke in Young Adults. Continuum (Minneapolis, Minn.), 26(2), 386–414. https://doi.org/10.1212/CON.0000000000000833
Gomez-Larrauri, A., Presa, N., Dominguez-Herrera, A., Ouro, A., Trueba, M., & Gomez-Muñoz, A. (2020). Role of bioactive sphingolipids in physiology and pathology. Essays in biochemistry, 64(3), 579–589. https://doi.org/10.1042/EBC20190091
Hannun, Y. A., & Obeid, L. M. (2011). Many ceramides. The Journal of biological chemistry, 286(32), 27855–27862. https://doi.org/10.1074/jbc.R111.254359
Kitatani, K., Idkowiak-Baldys, J., & Hannun, Y. A. (2008). The sphingolipid salvage pathway in ceramide metabolism and signaling. Cellular signalling, 20(6), 1010–1018. https://doi.org/10.1016/j.cellsig.2007.12.006
Gault, C. R., Obeid, L. M., & Hannun, Y. A. (2010). An overview of sphingolipid metabolism: from synthesis to breakdown. Advances in experimental medicine and biology, 688, 1–23. https://doi.org/10.1007/978-1-4419-6741-1_1
Edsfeldt, A., Dunér, P., Ståhlman, M., Mollet, I. G., Asciutto, G., Grufman, H., Nitulescu, M., Persson, A. F., Fisher, R. M., Melander, O., Orho-Melander, M., Borén, J., Nilsson, J., & Gonçalves, I. (2016). Sphingolipids Contribute to Human Atherosclerotic Plaque Inflammation. Arteriosclerosis, thrombosis, and vascular biology, 36(6), 1132–1140. https://doi.org/10.1161/ATVBAHA.116.305675
Casscells, W., Naghavi, M., & Willerson, J. T. (2003). Vulnerable atherosclerotic plaque: a multifocal disease. Circulation, 107(16), 2072–2075. https://doi.org/10.1161/01.CIR.0000069329.70061.68
Park, W. J., & Park, J. W. (2015). The effect of altered sphingolipid acyl chain length on various disease models. Biological chemistry, 396(6-7), 693–705. https://doi.org/10.1515/hsz-2014-0310
Luger, S., Schwebler, A., Vutukuri, R., Bouzas, N. F., Labocha, S., Schreiber, Y., Brunkhorst, R., Steinmetz, H., Pfeilschifter, J., & Pfeilschifter, W. (2018). Beta adrenoceptor blockade ameliorates impaired glucose tolerance and alterations of the cerebral ceramide metabolism in an experimental model of ischemic stroke. Therapeutic advances in neurological disorders, 11, 1756286418769830. https://doi.org/10.1177/1756286418769830
Gui, Y. K., Li, Q., Liu, L., Zeng, P., Ren, R. F., Guo, Z. F., Wang, G. H., Song, J. G., & Zhang, P. (2020). Plasma levels of ceramides relate to ischemic stroke risk and clinical severity. Brain research bulletin, 158, 122–127. https://doi.org/10.1016/j.brainresbull.2020.03.009
Chatterjee, S., Bedja, D., Mishra, S., Amuzie, C., Avolio, A., Kass, D. A., Berkowitz, D., & Renehan, M. (2014). Inhibition of glycosphingolipid synthesis ameliorates atherosclerosis and arterial stiffness in apolipoprotein E-/- mice and rabbits fed a high-fat and -cholesterol diet. Circulation, 129(23), 2403–2413. https://doi.org/10.1161/CIRCULATIONAHA.113.007559
Kim, M., Jung, S., Lee, S. H., & Lee, J. H. (2015). Association between arterial stiffness and serum L-octanoylcarnitine and lactosylceramide in overweight middle-aged subjects: 3-year follow-up study. PloS one, 10(3), e0119519. https://doi.org/10.1371/journal.pone.0119519
Bhunia, A. K., Han, H., Snowden, A., & Chatterjee, S. (1996). Lactosylceramide stimulates Ras-GTP loading, kinases (MEK, Raf), p44 mitogen-activated protein kinase, and c-fos expression in human aortic smooth muscle cells. The Journal of biological chemistry, 271(18), 10660–10666. https://doi.org/10.1074/jbc.271.18.10660
Chatterjee, S., Kolmakova, A., & Rajesh, M. (2008). Regulation of lactosylceramide synthase (glucosylceramide beta1-->4 galactosyltransferase); implication as a drug target. Current drug targets, 9(4), 272–281. https://doi.org/10.2174/138945008783954952
Chatterjee, S., Berliner, J. A., Subbanagounder, G. G., Bhunia, A. K., & Koh, S. (2004). Identification of a biologically active component in minimally oxidized low density lipoprotein (MM-LDL) responsible for aortic smooth muscle cell proliferation. Glycoconjugate journal, 20(5), 331–338. https://doi.org/10.1023/B:GLYC.0000033629.54962.68
Deguchi, H., Yegneswaran, S., & Griffin, J. H. (2004). Sphingolipids as bioactive regulators of thrombin generation. The Journal of biological chemistry, 279(13), 12036–12042. https://doi.org/10.1074/jbc.M302531200
Tang, X., Fang, M., Cheng, R., Zhang, Z., Wang, Y., Shen, C., Han, Y., Lu, Q., Du, Y., Liu, Y., Sun, Z., Zhu, L., Mwangi, J., Xue, M., Long, C., & Lai, R. (2020). Iron-Deficiency and Estrogen Are Associated With Ischemic Stroke by Up-Regulating Transferrin to Induce Hypercoagulability. Circulation research, 127(5), 651–663. https://doi.org/10.1161/CIRCRESAHA.119.31645

Download PDF

Reviewers agreed at journal
06 Sep, 2022
Reviewers invited by journal
01 Jun, 2022
Editor invited by journal
26 May, 2022
Editor assigned by journal
29 Apr, 2022
First submitted to journal
28 Apr, 2022

You are reading this latest preprint version

Mechanism Exploration of Youth Ischemic Stroke by Metabolomics Analysis

Status:

Version 1

Abstract

Figures

Introduction

METHODS

RESULTS

DICUSSION

Limitation

Abbreviations

Conclusion

Declarations

References

Status:

Version 1