Metabolomic profiling of fatty acid biomarkers for intracerebral hemorrhage stroke

Genetically predicted causality between gut microbiota, blood metabolites, and intracerebral hemorrhage: a bidirectional Mendelian randomization study

Background

Recent research linked changes in the gut microbiota and serum metabolite concentrations to intracerebral hemorrhage (ICH). However, the potential causal relationship remained unclear. Therefore, the current study aims to estimate the effects of genetically predicted causality between gut microbiota, serum metabolites, and ICH.

Methods

Summary data from genome-wide association studies (GWAS) of gut microbiota, serum metabolites, and ICH were obtained separately. Gut microbiota GWAS (N = 18,340) were acquired from the MiBioGen study, serum metabolites GWAS (N = 7,824) from the TwinsUK and KORA studies, and GWAS summary-level data for ICH from the FinnGen R9 (ICH, 3,749 cases; 339,914 controls). A two-sample Mendelian randomization (MR) study was conducted to explore the causal effects between gut microbiota, serum metabolites, and ICH. The random-effects inverse variance-weighted (IVW) MR analyses were performed as the primary results, together with a series of sensitivity analyses to assess the robustness of the results. Besides, a reverse MR was conducted to evaluate the possibility of reverse causation. To validate the relevant findings, we further selected data from the UK Biobank for analysis.

Results

MR analysis results revealed a nominal association (p < 0.05) between 17 gut microbial taxa, 31 serum metabolites, and ICH. Among gut microbiota, the higher level of genus Eubacterium xylanophilum (odds ratio (OR): 1.327, 95% confidence interval (CI):1.154-1.526; Bonferroni-corrected p = 7.28 × 10-5) retained a strong causal relationship with a higher risk of ICH after the Bonferroni corrected test. Concurrently, the genus Senegalimassilia (OR: 0.843, 95% CI: 0.778-0.915; Bonferroni-corrected p = 4.10 × 10-5) was associated with lower ICH risk. Moreover, after Bonferroni correction, only two serum metabolites remained out of the initial 31 serum metabolites. One of the serum metabolites, Isovalerate (OR: 7.130, 95% CI: 2.648-19.199; Bonferroni-corrected p = 1.01 × 10-4) showed a very strong causal relationship with a higher risk of ICH, whereas the other metabolite was unidentified and excluded from further analysis. Various sensitivity analyses yielded similar results, with no heterogeneity or directional pleiotropy observed.

Conclusion

This two-sample MR study revealed the significant influence of gut microbiota and serum metabolites on the risk of ICH. The specific bacterial taxa and metabolites engaged in ICH development were identified. Further research is required in the future to delve deeper into the mechanisms behind these findings.

Plasma lipidomic profiling reveals six candidate biomarkers for the prediction of incident stroke in patients with hypertension

INTRODUCTION

The burden of stroke in patients with hypertension is very high, and its prediction is critical.

OBJECTIVES

We aimed to use plasma lipidomics profiling to identify lipid biomarkers for predicting incident stroke in patients with hypertension.

METHODS

This was a nested case-control study. Baseline plasma samples were collected from 30 hypertensive patients with newly developed stroke, 30 matched patients with hypertension, 30 matched patients at high risk of stroke, and 30 matched healthy controls. Lipidomics analysis was performed by ultrahigh-performance liquid chromatography-tandem mass spectrometry, and differential lipid metabolites were screened using multivariate and univariate statistical methods. Machine learning methods (least absolute shrinkage and selection operator, random forest) were used to identify candidate biomarkers for predicting stroke in patients with hypertension.

RESULTS

Co-expression network analysis revealed that the key molecular alterations of the lipid network in stroke implicate glycerophospholipid metabolism and choline metabolism. Six lipid metabolites were identified as candidate biomarkers by multivariate statistical and machine learning methods, namely phosphatidyl choline(40:3p)(rep), cholesteryl ester(20:5), monoglyceride(29:5), triglyceride(18:0p/18:1/18:1), triglyceride(18:1/18:2/21:0) and coenzyme(q9). The combination of these six lipid biomarkers exhibited good diagnostic and predictive ability, as it could indicate a risk of stroke at an early stage in patients with hypertension (area under the curve = 0.870; 95% confidence interval: 0.783-0.957).

CONCLUSIONS

We determined lipidomic signatures associated with future stroke development and identified new lipid biomarkers for predicting stroke in patients with hypertension. The biomarkers have translational potential and thus may serve as blood-based biomarkers for predicting hypertensive stroke.

Multi-time point metabolomics reveals key metabolic features from the ultra-early stage of intracerebral hemorrhage in mice

Despite decades of intensive research, there are still very limited options for the effective treatment of intracerebral hemorrhage (ICH). Recently, mounting evidence has indicated that the ultra-early stage (<3 h), serving as the primary phase of ICH, plays a pivotal role and may even surpass other stages in terms of its significance. Therefore, uncovering the metabolic alterations induced by ICH in the ultra-early stage is of crucial importance. To investigate this, the collagenase ICH mouse model was employed in this study. ICH or sham-operated mice were euthanized at the ultra-early stage of 3 h and the acute stage of 24 h and 72 h after the operation. Then, the metabolic changes in the perihematomal tissues were detected by liquid chromatography coupled with tandem mass spectrometry. In total, alterations in the levels of 465 metabolites were detected. A total of 136 metabolites were significantly changed at 3 h. At 24 h and 72 h, the amounts were 132 and 126, respectively. Additionally, the key corresponding metabolic pathways for these time points were analyzed through KEGG. To gather additional information, quantitative real-time transcription polymerase chain reaction, enzyme-linked immunosorbent assay and Western blots were performed to validate the metabolic changes. Overall, ICH significantly alters important physiological functions such as cysteine metabolism, purine metabolism, synaptic alterations, the synaptic vesicle cycle, and the ATP-binding cassette transporter system. These might be the key pathologic mechanisms of the ultra-early stage induced by ICH.

BCL-3 Promotes Intracerebral Hemorrhage Progression by Increasing Blood-Brain Barrier Permeability, Inflammation, and Cell Apoptosis via Endoplasmic Reticulum Stress

Background

Intracerebral hemorrhage (ICH) is among the common types of stroke with high mortality and morbidity. Molecular biomarker selection is crucial for ICH diagnosis and treatment. However, the identification of ICH-related biomarkers remains inadequate.

Materials and Methods

In vivo and in vitro ICH models were generated and transfected with silenced B-cell lymphoma-3 (BCL-3 and siRNA BCL-3), overexpressed BCL-3, and endoplasmic reticulum stress (ERS) agonist (2-CLHA). Hematoxylin-eosin staining and transmission electron microscopy were used to observe the transfected cells. RNA sequencing was performed in vivo on the sham and ICH groups. The blood-brain barrier (BBB) permeability was evaluated by determining Evans blue dye extravasation, transendothelial electrical resistance, and paracellular permeability. Moreover, tight junction-, cell apoptosis-, and endoplasmic reticulum stress- (ERS-) related proteins were evaluated through real-time quantitative PCR, western blotting, immunohistochemistry, and TUNEL staining. The levels of inflammatory cytokines were measured through the enzyme-linked immunosorbent assay.

Results

RNA-seq revealed that BCL-3 acts as a key player. BCL-3 promotes ICH progression by increasing BBB permeability, ERS, inflammation, and cell apoptosis. Silencing of BCL-3 slows ICH progression by reducing BBB permeability and inflammation and terminating cell apoptosis and ERS in vitro and in vivo.

Conclusion

Our study identified ICH biomarkers and elucidated the role of BCL-3 in ICH for the first time.

Untargeted metabonomic analysis of a cerebral stroke model in rats: a study based on UPLC-MS/MS

Introduction

Brain tissue damage caused by ischemic stroke can trigger changes in the body's metabolic response, and understanding the changes in the metabolic response of the gut after stroke can contribute to research on poststroke brain function recovery. Despite the increase in international research on poststroke metabolic mechanisms and the availability of powerful research tools in recent years, there is still an urgent need for poststroke metabolic studies. Metabolomic examination of feces from a cerebral ischemia-reperfusion rat model can provide new insights into poststroke metabolism and identify key metabolic pathways, which will help reveal diagnostic and therapeutic targets as well as inspire pathophysiological studies after stroke.

Methods

We randomly divided 16 healthy adult pathogen-free male Sprague-Dawley (SD) rats into the normal group and the study group, which received middle cerebral artery occlusion/reperfusion (MCAO/R). Ultra-performance liquid chromatography-tandem mass spectrometry (UPLCMS/MS) was used to determine the identities and concentrations of metabolites across all groups, and filtered high-quality data were analyzed for differential screening and differential metabolite functional analysis.

Results

After 1 and 14 days of modeling, compared to the normal group, rats in the study group showed significant neurological deficits (p < 0.001) and significantly increased infarct volume (day 1: p < 0.001; day 14: p = 0.001). Mass spectra identified 1,044 and 635 differential metabolites in rat feces in positive and negative ion modes, respectively, which differed significantly between the normal and study groups. The metabolites with increased levels identified in the study group were involved in tryptophan metabolism (p = 0.036678, p < 0.05), arachidonic acid metabolism (p = 0.15695), cysteine and methionine metabolism (p = 0.24705), and pyrimidine metabolism (p = 0.3413), whereas the metabolites with decreased levels were involved in arginine and proline metabolism (p = 0.15695) and starch and sucrose metabolism (p = 0.52256).

Discussion

We determined that UPLC-MS/MS could be employed for untargeted metabolomics research. Moreover, tryptophan metabolic pathways may have been disordered in the study group. Alterations in the tryptophan metabolome may provide additional theoretical and data support for elucidating stroke pathogenesis and selecting pathways for intervention.

Intracranial hemorrhage management in the multi-omics era

Intracranial hemorrhage (ICH) is a devastating disorder. Neuroprotective strategies that prevent tissue injury and improve functional outcomes have been identified in multiple animal models of ICH. However, these potential interventions in clinical trials produced generally disappointing results. With progress in omics, studies of omics data, including genomics, transcriptomics, epigenetics, proteomics, metabolomics, and the gut microbiome, may help promote precision medicine. In this review, we focused on introducing the applications of all omics in ICH and shed light on all of the considerable advantages to systematically analyze the necessity and importance of multiple omics technology in ICH.

Machine learning-based identification of the novel circRNAs circERBB2 and circCHST12 as potential biomarkers of intracerebral hemorrhage

BACKGROUND

The roles and potential diagnostic value of circRNAs in intracerebral hemorrhage (ICH) remain elusive.

METHODS

This study aims to investigate the expression profiles of circRNAs by RNA sequencing and RT-PCR in a discovery cohort and an independent validation cohort. Bioinformatics analysis was performed to identify the potential functions of circRNA host genes. Machine learning classification models were used to assess circRNAs as potential biomarkers of ICH.

RESULTS

A total of 125 and 284 differentially expressed circRNAs (fold change > 1.5 and FDR < 0.05) were found between ICH patients and healthy controls in the discovery and validation cohorts, respectively. Nine circRNAs were consistently altered in ICH patients compared to healthy controls. The combination of the novel circERBB2 and circCHST12 in ICH patients and healthy controls showed an area under the curve of 0.917 (95% CI: 0.869-0.965), with a sensitivity of 87.5% and a specificity of 82%. In combination with ICH risk factors, circRNAs improved the performance in discriminating ICH patients from healthy controls. Together with hsa_circ_0005505, two novel circRNAs for differentiating between patients with ICH and healthy controls showed an AUC of 0.946 (95% CI: 0.910-0.982), with a sensitivity of 89.1% and a specificity of 86%.

CONCLUSION

We provided a transcriptome-wide overview of aberrantly expressed circRNAs in ICH patients and identified hsa_circ_0005505 and novel circERBB2 and circCHST12 as potential biomarkers for diagnosing ICH.

Blood-based protein biomarkers for the diagnosis of acute stroke: A discovery-based SWATH-MS proteomic approach

Background and purposes

Recent developments in high-throughput proteomic approach have shown the potential to discover biomarkers for diagnosing acute stroke and to elucidate the pathomechanisms specific to different stroke subtypes. We aimed to determine blood-based protein biomarkers to diagnose total stroke (IS+ICH) from healthy controls, ischemic stroke (IS) from healthy controls, and intracerebral hemorrhage (ICH) from healthy control subjects within 24 h using a discovery-based SWATH-MS proteomic approach.

Methods

In this discovery phase study, serum samples were collected within 24 h from acute stroke (IS & ICH) patients and healthy controls and were subjected to SWATH-MS-based untargeted proteomics. For protein identification, a high-pH fractionated peptide library for human serum proteins (obtained from SCIEX) comprising of 465 proteins was used. Significantly differentially expressed (SDE) proteins were selected using the following criteria: >1.5-fold change for upregulated, < 0.67 for downregulated, p-value < 0.05, and confirmed/tentative selection using Boruta random forest. Protein–protein interaction network analysis and the functional enrichment analysis were conducted using STRING 11 online tool, g:Profiler tool and Cytoscape 3.9.0 software. The statistical analyses were conducted in R version 3.6.2.

Results

Our study included 40 stroke cases (20 IS, 20 ICH) within 24 h and 40 age-, sex-, hypertension-, and diabetes-matched healthy controls. We quantified 375 proteins between the stroke cases and control groups through SWATH-MS analysis. We observed 31 SDE proteins between total stroke and controls, 16 SDE proteins between IS and controls, and 41 SDE proteins between ICH and controls within 24 h. Four proteins [ceruloplasmin, alpha-1-antitrypsin (SERPINA1), von Willebrand factor (vWF), and coagulation factor XIII B chain (F13B)] commonly differentiated total stroke, IS, and ICH from healthy control subjects. The most common significant pathways in stroke cases involved complement and coagulation cascades, platelet degranulation, immune-related processes, acute phase response, lipid-related processes, and pathways related to extracellular space and matrix.

Conclusion

Our discovery phase study identified potential protein biomarker candidates for the diagnosis of acute stroke and highlighted significant pathways associated with different stroke subtypes. These potential biomarker candidates warrant further validation in future studies with a large cohort of stroke patients to investigate their diagnostic performance.

The Functions of Cytochrome P450 ω-hydroxylases and the Associated Eicosanoids in Inflammation-Related Diseases

The cytochrome P450 (CYP) ω-hydroxylases are a subfamily of CYP enzymes. While CYPs are the main metabolic enzymes that mediate the oxidation reactions of many endogenous and exogenous compounds in the human body, CYP ω-hydroxylases mediate the metabolism of multiple fatty acids and their metabolites via the addition of a hydroxyl group to the ω- or (ω-1)-C atom of the substrates. The substrates of CYP ω-hydroxylases include but not limited to arachidonic acid, docosahexaenoic acid, eicosapentaenoic acid, epoxyeicosatrienoic acids, leukotrienes, and prostaglandins. The CYP ω-hydroxylases-mediated metabolites, such as 20-hyroxyleicosatrienoic acid (20-HETE), 19-HETE, 20-hydroxyl leukotriene B4 (20-OH-LTB₄), and many ω-hydroxylated prostaglandins, have pleiotropic effects in inflammation and many inflammation-associated diseases. Here we reviewed the classification, tissue distribution of CYP ω-hydroxylases and the role of their hydroxylated metabolites in inflammation-associated diseases. We described up-regulation of CYP ω-hydroxylases may be a pathogenic mechanism of many inflammation-associated diseases and thus CYP ω-hydroxylases may be a therapeutic target for these diseases. CYP ω-hydroxylases-mediated eicosanods play important roles in inflammation as pro-inflammatory or anti-inflammatory mediators, participating in the process stimulated by cytokines and/or the process stimulating the production of multiple cytokines. However, most previous studies focused on 20-HETE,and further studies are needed for the function and mechanisms of other CYP ω-hydroxylases-mediated eicosanoids. We believe that our studies of CYP ω-hydroxylases and their associated eicosanoids will advance the translational and clinal use of CYP ω-hydroxylases inhibitors and activators in many diseases.

Temporal metabolomic alteration in rat brains of experimental intracerebral hemorrhage

BACKGROUND

Intracerebral hemorrhage (ICH) is the top lethal and disabling form of stroke. The pathophysiology of ICH is not fully understood yet. Metabolites are indicators and regulators of cellular processes. However, the overall brain metabolic pattern and the temporal alterations after ICH remain unknown.

METHODS

A total of 40 male rats were randomly assigned to sham group and ICH group. ICH was induced by collagenase Ⅶ. Body weight was assessed. Neurological deficits were evaluated by modified neurological severity score. Then, the perihematomal brain tissues were collected for metabolites detection using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS). The metabolic profiles were displayed by principal component analysis (PCA), partial least-squares-discriminant analysis (PLS-DA) and cluster analysis. The significant differential metabolites were screened by fold change > 2.0, the false discovery rate (FDR) < 0.05 and Variable Importance of Projection (VIP) > 1. Next, the relevant metabolic pathways were discerned by MetaboAnalyst website. A metabolite-protein interaction network was subsequentially constructed to further annotate the function of differential metabolites.

RESULTS

Rats suffered from compromised body weight increasement and impaired neurological function. The metabolomics profiles of brain tissues in the post-ICH rats were markedly different from those in the sham group on days 3 and 14. Thirty-four metabolites (bilirubin, uric acid, 6-Methylnicotinamide et al.) were abnormally upregulated in the acute stage, while 27 metabolites were disturbed in the recovery stage, including bilirubin, uric acid, and histamine et al. Seven and three metabolic pathways altered in the acute and recovery stage, respectively. Metabolite-protein interaction analysis revealed that the disturbed metabolites may participate in ICH pathophysiology by altering amino acid metabolism, peroxisome proliferators-activated receptor signaling pathway, fatty acid metabolism and urea cycle in the acute stage, while influencing amino acid metabolism, urea cycle and peroxisome in the recovery stage.

CONCLUSIONS

Our study mapped the pathological metabolomics profiles of the post-ICH rat brains in the acute and recovery phases. This work will assist in discovering novel therapeutic targets and treatments for ICH.