Identification of three potential novel biomarkers for early diagnosis of acute ischemic stroke via plasma lipidomics.

INTRODUCTION: Acute ischemic stroke (AIS) accounts for the majority of all stroke, globally the second leading cause of death. Due to its rapid development after onset, its early diagnosis is crucial. OBJECTIVES: We aim to identify potential highly reliable blood-based biomarkers for early diagnosis of AIS using quantitative plasma lipid profiling via a machine learning approach. METHODS: Lipidomics was used for quantitative plasma lipid profiling, based on ultra-performance liquid chromatography tandem mass spectrometry. Our samples were divided into a discovery and a validation set, each containing 30 AIS patients and 30 health controls (HC). Differentially expressed lipid metabolites were screened based on the criteria VIP > 1, p < 0.05, and fold change > 1.5 or < 0.67. The least absolute shrinkage and selection operator (LASSO) and random forest algorithms in machine learning were used to select differential lipid metabolites as potential biomarkers. RESULTS: Three key differential lipid metabolites, CarnitineC10:1, CarnitineC10:1-OH and Cer(d18:0/16:0), were identified as potential biomarkers for early diagnosis of AIS. The former two, associated with thermogenesis, were down-regulated, whereas the latter, associated with necroptosis and sphingolipd metabolism, was upregulated. Univariate and multivariate logistic regressions showed that these three lipid metabolites and the resulting diagnostic model exhibited a strong ability in discriminating between AIS patients and HCs in both the discovery and validation sets, with an area under the curve above 0.9. CONCLUSIONS: Our work provides valuable information on the pathophysiology of AIS and constitutes an important step toward clinical application of blood-based biomarkers for diagnosing AIS.

ALB, HP, OAF and RBP4 as novel protein biomarkers for identifying cured patients with pulmonary tuberculosis by DIA.

BACKGROUND: Pulmonary tuberculosis (TB) is a serious infectious disease that lacks robust blood-based biomarkers to identify cured TB. Some discharged patients are not fully cured and may relapse or even develop multidrug-resistant TB. This study is committed to finding proteomic-based plasma biomarkers to support establishing laboratory standards for clinical TB cure. METHODS: Data-independent acquisition (DIA) was used to obtain the plasma protein expression profiles of TB patients at different treatment stages compared with healthy controls. Multivariate statistical methods and bioinformatics were used to analyze the data. RESULTS: Bioinformatic analysis suggests coagulation dysfunction and vitamin and lipid metabolism disturbances in TB. Albumin (ALB), haptoglobin (HP), out at first protein homolog (OAF), and retinol-binding protein 4 (RBP4) can be used to establish a diagnostic model for the efficacy evaluation of TB with an area under the curve of 0.963, which could effectively distinguish untreated TB patients from cured patients. CONCLUSIONS: Our research demonstrated that ALB, HP, OAF and RBP4 can be potential biomarkers for evaluating the efficacy of TB. These findings may provide experimental data for establishing the laboratory indicators of clinical TB cure and providing clinicians with new targets for exploring the underlying mechanisms of TB pathogenesis.

Plasma Quantitative Lipid Profiles: Identification of CarnitineC18:1-OH, CarnitineC18:2-OH and FFA (20:1) as Novel Biomarkers for Pre-warning and Prognosis in Acute Myocardial Infarction.

This study was aimed to determine the association between potential plasma lipid biomarkers and early screening and prognosis of Acute myocardial infarction (AMI). In the present study, a total of 795 differentially expressed lipid metabolites were detected based on ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). Out of these metabolites, 25 lipid metabolites were identified which showed specifical expression in the AMI group compared with the healthy control (HC) group and unstable angina (UA) group. Then, we applied the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) methods to obtain three lipid molecules, including CarnitineC18:1-OH, CarnitineC18:2-OH and FFA (20:1). The three lipid metabolites and the diagnostic model exhibited well predictive ability in discriminating between AMI patients and UA patients in both the discovery and validation sets with an area under the curve (AUC) of 0.9. Univariate and multivariate logistic regression analyses indicated that the three lipid metabolites may serve as potential biomarkers for diagnosing AMI. A subsequent 1-year follow-up analysis indicated that the three lipid biomarkers also had prominent performance in predicting re-admission of patients with AMI due to cardiovascular events. In summary, we used quantitative lipid technology to delineate the characteristics of lipid metabolism in patients with AMI, and identified potential early diagnosis biomarkers of AMI via machine learning approach.

Plasma microRNA expression profiles in Chinese patients with rheumatoid arthritis.

The outstanding characteristics of circulatory microRNAs (miRNAs) attract much attention in research on disease biomarkers and disease pathogenesis. This study aimed to identify the expression profiles of plasma miRNAs in patients with rheumatoid arthritis (RA). Thirty-three miRNAs were screened using an miRNA array, of which 9 miRNAs were validated as differentially expressed in the plasma of RA patients compared with healthy controls (HCs). miRNA-4634 (miR-4634), miR-181d and miR-4764-5p expression levels were increased, whereas miR-342-3p, miR-3926, miR-3925-3p, miR-122-3p, miR-9-5p and miR-219-2-3p expression levels were decreased in RA patients. The areas under the curve (AUCs) were generated to estimate the sensitivity and specificity of each miRNA or the panel of all 9 miRNAs as biomarkers for RA. AUCs for 9 individual miRNAs ranged from 0.6254 to 0.818; however, the AUC for the panel of 9 miRNAs reached 0.964. Levels of miR-122-3p, miR-3925-3p, miR-342-3p and miR-4764-5p expression showed significant differences between RA and other control groups. miR-4764-5p, miR-4634, miR-9-5p and miR-219-2-3p exhibited significant correlations with either plasma cytokine and chemokine levels or clinical features. In conclusion, this study identified 9-plasma miRNAs signature in Chinese patients with RA which may serve as noninvasive biomarkers for the diagnosis of RA.

[Effects of lipopolysaccharide and phytohaemagglutinin on the level of microRNAs in mouse peripheral blood mononuclear cells].

OBJECTIVE: To investigate the effects of lipopolysaccharide (LPS) and phytohaemagglutinin (PHA) on the level of microRNAs in mouse peripheral blood mononuclear cells (PBMCs). METHODS: The mouse PBMCs were cultured in vitro and stimulated with 100 ng/mL LPS for 1 hour or 2 hours and 2.5 µg/mL phytohaemagglutinin (PHA) for 2 hours or 4 hours, respectively. The levels of miR-9, miR-122-3p, miR-181d and miR-342-3p in both PBMCs and culture supernatants were determined by real-time quantitative PCR (qRT-PCR). RESULTS: The levels of miR-9, miR-122-3p, miR-181d and miR-342-3p in PBMCs and culture supernatants had no significant difference between blank control and LPS stimulation group, nor did between LPS stimulation 2 hours group and 1 hour group. The levels of miR-9, miR-122-3p and miR-342-3p in PBMCs and culture supernatant had no significant difference between blank control and PHA stimulation group, nor did between PHA stimulation 4 hours group and 2 hours group. However, compared with blank control, the expression of miR-181d significantly increased after PHA stimulation, while the time of stimulation did not affect the miR-181d level significantly. CONCLUSION: The stimulation of PHA could promote the expression and secretion of miR-181d in mouse PBMCs.

Functional characterization of myeloid-derived suppressor cell subpopulations during the development of experimental arthritis.

Recent evidence indicates the existence of subpopulations of myeloid-derived suppressor cells (MDSCs) with distinct phenotypes and functions. Here, we characterized the role of MDSC subpopulations in the pathogenesis of autoimmune arthritis in a collagen-induced arthritis (CIA) mouse model. The splenic CD11b(+) Gr-1(+) MDSC population expanded in CIA mice, and these cells could be subdivided into polymorphonuclear (PMN) and mononuclear (MO) MDSC subpopulations based on Ly6C and Ly6G expression. During CIA, the proportion of splenic MO-MDSCs was increased in association with the severity of joint inflammation, while PMN-MDSCs were decreased. MO-MDSCs expressed higher levels of surface CD40 and CD86 protein, but lower levels of Il10, Tgfb1, Ccr5, and Cxcr2 mRNA. PMN-MDSCs exhibited a more potent capacity to suppress polyclonal T-cell proliferation in vitro, compared with MO-MDSCs. Moreover, the adoptive transfer of PMN-MDSCs, but not MO-MDSCs, decreased joint inflammation, accompanied by reduced levels of serum cytokine secretion and the frequencies of Th1 and Th17 cells in draining lymph nodes. These results suggest that there could be a shift from potently suppressive PMN-MDSCs to poorly suppressive MO-MDSCs during the development of experimental arthritis, which might reflect the failure of expanded MDSCs to suppress autoimmune arthritis.