Metabonomics-based omics study and atherosclerosis

Preliminary study of metabonomic changes during the progression of atherosclerosis in miniature pigs

BACKGROUND

To explore potential biomarkers for early diagnosis of atherosclerosis (AS) and provide basic data for further research on AS, the characteristics of serum metabolomics during the progression of AS in mini-pigs were observed dynamically.

METHODS

An AS model in Bama miniature pigs was established by a high-cholesterol and high-fat diet. Fasting serum samples were collected monthly for metabolomics and serum lipid detection. At the end of the treatment period, pathological analysis of the abdominal aorta and coronary artery was performed to evaluate the lesions of AS, thereby distinguishing the susceptibility of mini-pigs to AS. The metabolomics was detected using a high-resolution untargeted metabolomic approach. Statistical analysis was used to identify metabolites associated with AS susceptibility.

RESULTS

Based on pathological analysis, mini-pigs were divided into two groups: a susceptible group (n = 3) and a non-susceptible group (n = 6). A total of 1318 metabolites were identified, with significant shifting of metabolic profiles over time in both groups. Dynamic monitoring analysis highlighted 57 metabolites that exhibited an obvious trend of differential changes between two groups with the advance of time. The KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis indicated significant disorders in cholesterol metabolism, primary bile acid metabolism, histidine metabolism, as well as taurine and hypotaurine metabolism.

CONCLUSIONS

During the progression of AS in mini-pigs induced by high-cholesterol/high-fat diet, the alterations in serum metabolic profile exhibited a time-dependent pattern, accompanied by notable disturbances in lipid metabolism, cholesterol metabolism, and amino acid metabolism. These metabolites may become potential biomarkers for early diagnosis of AS.

Large-scale Metabolomic Analysis Reveals Potential Biomarkers for Early Stage Coronary Atherosclerosis

Coronary atherosclerosis (CAS) is the pathogenesis of coronary heart disease, which is a prevalent and chronic life-threatening disease. Initially, this disease is not always detected until a patient presents with seriously vascular occlusion. Therefore, new biomarkers for appropriate and timely diagnosis of early CAS is needed for screening to initiate therapy on time. In this study, we used an untargeted metabolomics approach to identify potential biomarkers that could enable highly sensitive and specific CAS detection. Score plots from partial least-squares discriminant analysis clearly separated early-stage CAS patients from controls. Meanwhile, the levels of 24 metabolites increased greatly and those of 18 metabolites decreased markedly in early CAS patients compared with the controls, which suggested significant metabolic dysfunction in phospholipid, sphingolipid, and fatty acid metabolism in the patients. Furthermore, binary logistic regression showed that nine metabolites could be used as a combinatorial biomarker to distinguish early-stage CAS patients from controls. The panel of nine metabolites was then tested with an independent cohort of samples, which also yielded satisfactory diagnostic accuracy (AUC = 0.890). In conclusion, our findings provide insight into the pathological mechanism of early-stage CAS and also supply a combinatorial biomarker to aid clinical diagnosis of early-stage CAS.

Changes in the plasma membrane in metabolic disease: impact of the membrane environment on G protein-coupled receptor structure and function

Drug development targeting GPCRs often utilizes model heterologous cell expression systems, reflecting an implicit assumption that the membrane environment has little functional impact on these receptors or on their responsiveness to drugs. However, much recent data have illustrated that membrane components can have an important functional impact on intrinsic membrane proteins. This review is directed toward gaining a better understanding of the structure of the plasma membrane in health and disease, and how this organelle can influence GPCR structure, function and regulation. It is important to recognize that the membrane provides a potential mode of lateral allosteric regulation of GPCRs and can affect the effectiveness of drugs and their biological responses in various disease states, which can even vary among individuals across the population. The type 1 cholecystokinin receptor is reviewed as an exemplar of a class A GPCR that is affected in this way by changes in the plasma membrane.

LINKED ARTICLES

This article is part of a themed section on Molecular Pharmacology of GPCRs. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v175.21/issuetoc.

Lipidomics: new insight into kidney disease

Due to the incidence of type-2 diabetes and hypertension, chronic kidney disease (CKD) has emerged as a major public health problem worldwide. CKD results in premature death from accelerated cardiovascular disease and various other complications. Early detection, careful monitoring of renal function, and response to therapeutic intervention are critical for prevention of CKD progression and its complications. Unfortunately, traditional biomarkers of renal function are insufficiently sensitive or specific to detect early stages of disease when therapeutic intervention is most effective. Therefore, more sensitive biomarkers of kidney disease are needed for early diagnosis, monitoring, and effective treatment. CKD results in profound changes in lipid and lipoprotein metabolism that, in turn, contribute to progression of CKD and its cardiovascular complications. Lipids and lipid-derived metabolites play diverse and critically important roles in the structure and function of cells, tissues, and biofluids. Lipidomics is a branch of metabolomics, which encompasses the global study of lipids and their biologic function in health and disease including identification of biomarkers for diagnosis, prognosis, prevention, and therapeutic response for various diseases. This review summarizes recent developments in lipidomics and its application to various kidney diseases including chronic glomerulonephritis, IgA nephropathy, chronic renal failure, renal cell carcinoma, diabetic nephropathy, and acute renal failure in clinical and experimental research. Analytical technologies, data analysis, as well as currently known metabolic biomarkers of kidney diseases are addressed. Future perspectives and potential limitations of lipidomics are discussed.

Metabolomics in dyslipidemia

Hyperlipidemia is an important public health problem with increased incidence and prevalence worldwide. Current clinical biomarkers, triglyceride, total cholesterol, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol lack the necessary specificity and sensitivity and only increase significantly after serious dyslipidemia. Therefore, sensitive biomarkers are needed for hyperlipidemia. Hyperlipidemia-specific biomarkers would improve clinical diagnosis and therapeutic treatment at early disease stages. The aim of metabolomics is to identify untargeted and global small-molecule metabolite profiles from cells, biofluids, and tissues. This method offers the potential for a holistic approach to improve disease diagnoses and our understanding of underlying pathologic mechanisms. This review summarizes analytical techniques, data collection and analysis for metabolomics, and metabolomics in hyperlipidemia animal models and clinical studies. Mechanisms of hypolipemia and antilipemic drug therapy are also discussed. Metabolomics provides a new opportunity to gain insight into metabolic profiling and pathophysiologic mechanisms of hyperlipidemia.

Enhanced frequency and potential mechanism of B regulatory cells in patients with lung cancer

BACKGROUND

Regulatory T cells (Tregs) and B cells (Bregs) play an important role in the development of lung cancer. The present study aimed to investigate the phenotype of circulating Tregs and Bregs in patients with lung cancer and explore potential mechanism by which lung cancer cells act on the development of both.

METHODS

Patients with lung cancer (n = 268) and healthy donors (n = 65) were enrolled in the study. Frequencies of Tregs and Bregs were measured by flow cytometry with antibodies against CD4, CD25, CD127, CD45RA, CD19, CD24, CD27 and IL-10 before and after co-cultures. qRT-PCR was performed to evaluate the mRNA levels of RANTES, MIP-1α, TGF-β, IFN-γ and IL-4.

RESULTS

We found a lower frequency of Tregs and a higher frequency of Bregs in patients with lung cancer compared to healthy donors. Co-culture of lung cancer cells with peripheral blood mononuclear cells could polarize the lymphocyte phenotype in the similar pattern. Lipopolysaccharide (LPS)-stimulated lung cancer cells significantly modulated regulatory cell number and function in an in vitro model.

CONCLUSION

We provide initial evidence that frequencies of peripheral Tregs decreased or Bregs increased in patients with lung cancer, which may be modulated directly by lung cancer cells. It seems cancer cells per se plays a crucial role in the development of tumor immunity.

Urinary Metabolomics on the Biochemical Profiles in Diet-Induced Hyperlipidemia Rat Using Ultraperformance Liquid Chromatography Coupled with Quadrupole Time-of-Flight SYNAPT High-Definition Mass Spectrometry

Ultraperformance liquid chromatography coupled with quadrupole time-of-flight synapt high-definition mass spectrometry metabolomics was used to characterize the urinary metabolic profiling of diet-induced hyperlipidaemia in a rat model. Analysis was done by orthogonal partial least squares discriminant analysis, correlation analysis, heat map analysis, and KEGG pathways analysis. Potential biomarkers were chosen by S-plot and were identified by accurate mass, isotopic pattern, and MS/MS fragments information. Significant differences in fatty acid, amino acid, nucleoside, and bile acid were observed, indicating the perturbations of fatty acid, amino acid, nucleoside, and bile acid metabolisms in diet-induced hyperlipidaemia rats. This study provides further insight into the metabolic profiling across a wide range of biochemical pathways in response to diet-induced hyperlipidaemia.

Gene polymorphisms and chronic obstructive pulmonary disease

The genetic component was suggested to contribute to the development of chronic obstructive pulmonary disease (COPD), a major and growing public health burden. The present review aims to characterize the evidence that gene polymorphisms contribute to the aetiology of COPD and related traits, and explore the potential relationship between certain gene polymorphisms and COPD susceptibility, severity, lung function, phenotypes, or drug effects, even though limited results from related studies lacked consistency. Most of these studies were association studies, rather than confirmatory studies. More large-sized and strictly controlled studies are needed to prove the relationship between gene polymorphisms and the reviewed traits. More importantly, prospective confirmatory studies beyond initial association studies will be necessary to evaluate true relationships between gene polymorphisms and COPD and help individualized treatment for patients with COPD.

Genetic network and gene set enrichment analysis to identify biomarkers related to cigarette smoking and lung cancer

OBJECTIVES

Cigarette smoking is the most demonstrated risk factor for the development of lung cancer, while the related genetic mechanisms are still unclear.

METHODS

The preprocessed microarray expression dataset was downloaded from Gene Expression Omnibus database. Samples were classified according to the disease state, stage and smoking state. A new computational strategy was applied for the identification and biological interpretation of new candidate genes in lung cancer and smoking by coupling a network-based approach with gene set enrichment analysis.

MEASUREMENTS

Network analysis was performed by pair-wise comparison according to the disease states (tumor or normal), smoking states (current smokers or nonsmokers or former smokers), or the disease stage (stages I-IV). The most activated metabolic pathways were identified by gene set enrichment analysis.

RESULTS

Panels of top ranked gene candidates in smoking or cancer development were identified, including genes involved in cell proliferation and drug metabolism like cytochrome P450 and WW domain containing transcription regulator 1. Semaphorin 5A and protein phosphatase 1F are the common genes represented as major hubs in both the smoking and cancer related network. Six pathways, e.g. cell cycle, DNA replication, RNA transport, protein processing in endoplasmic reticulum, vascular smooth muscle contraction and endocytosis were commonly involved in smoking and lung cancer when comparing the top ten selected pathways.

CONCLUSION

New approach of bioinformatics for biomarker identification and validation can probe into deep genetic relationships between cigarette smoking and lung cancer. Our studies indicate that disease-specific network biomarkers, interaction between genes/proteins, or cross-talking of pathways provide more specific values for the development of precision therapies for lung.