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Microvesicles/exosomes as potential novel biomarkers of metabolic diseases.

ABSTRACT: Biomarkers are of tremendous importance for the prediction, diagnosis, and observation of the therapeutic success of common complex multifactorial metabolic diseases, such as type II diabetes and obesity. However, the predictive power of the traditional biomarkers used (eg, plasma metabolites and cytokines, body parameters) is apparently not sufficient for reliable monitoring of stage-dependent pathogenesis starting with the healthy state via its initiation and development to the established disease and further progression to late clinical outcomes. Moreover, the elucidation of putative considerable differences in the underlying pathogenetic pathways (eg, related to cellular/tissue origin, epigenetic and environmental effects) within the patient population and, consequently, the differentiation between individual options for disease prevention and therapy - hallmarks of personalized medicine - plays only a minor role in the traditional biomarker concept of metabolic diseases. In contrast, multidimensional and interdependent patterns of genetic, epigenetic, and phenotypic markers presumably will add a novel quality to predictive values, provided they can be followed routinely along the complete individual disease pathway with sufficient precision. These requirements may be fulfilled by small membrane vesicles, which are so-called exosomes and microvesicles (EMVs) that are released via two distinct molecular mechanisms from a wide variety of tissue and blood cells into the circulation in response to normal and stress/pathogenic conditions and are equipped with a multitude of transmembrane, soluble and glycosylphosphatidylinositol-anchored proteins, mRNAs, and microRNAs. Based on the currently available data, EMVs seem to reflect the diverse functional and dysfunctional states of the releasing cells and tissues along the complete individual pathogenetic pathways underlying metabolic diseases. A critical step in further validation of EMVs as biomarkers will rely on the identification of unequivocal correlations between critical disease states and specific EMV signatures, which in future may be determined in rapid and convenient fashion using nanoparticle-driven biosensors.

SUBMITTER: Muller G

PROVIDER: S-EPMC3422911 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Microvesicles/exosomes as potential novel biomarkers of metabolic diseases.

Müller Günter G

Diabetes, metabolic syndrome and obesity : targets and therapy 20120807

Biomarkers are of tremendous importance for the prediction, diagnosis, and observation of the therapeutic success of common complex multifactorial metabolic diseases, such as type II diabetes and obesity. However, the predictive power of the traditional biomarkers used (eg, plasma metabolites and cytokines, body parameters) is apparently not sufficient for reliable monitoring of stage-dependent pathogenesis starting with the healthy state via its initiation and development to the established dis ...[more]

PMID: 22924003

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Project description:Recently, there have been emerging interests in the area of microvesicles and exosome (MV/E) released from brain cells in relation to neurodegenerative diseases. However, only limited studies focused on MV/E released post-traumatic brain injury (TBI) as they highlight on the mechanistic roles of released proteins. This study sought to examine if CSF samples from severe TBI patients contain MV/E with unique protein contents. First, nanoparticle tracking analysis determined MV/E from TBI have a mode of 74-98 nm in diameter, while control CSF MV/E have a mode of 99-104 nm. Also, there are more MV/E were isolated from TBI CSF (27.8-33.6 × 108/mL) than from control CSF (13.1-18.5 × 108/mL). Transmission electron microscopy (TEM) visualization also confirmed characteristic MV/E morphology. Using targeted immunoblotting approach, we observed the presence of several known TBI biomarkers such as αII-spectrin breakdown products (BDPs), GFAP, and its BDPs and UCH-L1 in higher concentrations in MV/E from TBI CSF than their counterparts from control CSF. Furthermore, we found presynaptic terminal protein synaptophysin and known exosome marker Alix enriched in MV/E from human TBI CSF. In parallel, we conducted nRPLC-tandem mass spectrometry-based proteomic analysis of two control and two TBI CSF samples. Ninety-one proteins were identified with high confidence in MV/E from control CSF, whereas 466 proteins were identified in the counterpart from TBI CSF. MV/E isolated from human CSF contain cytoskeletal proteins, neurite-outgrowth related proteins, and synaptic proteins, extracellular matrix proteins, and complement protein C1q subcomponent subunit B. Taken together, following severe TBI, the injured human brain released increased number of extracellular microvesicles/exosomes (MV/E) into CSF. These TBI MV/E contain several known TBI biomarkers and previously undescribed brain protein markers. It is also possible that such TBI-specific MV/E might contain cell to cell communication factors related to both cell death signaling a well as neurodegeneration pathways.

Project description:The gut microbiome is of utmost importance to human health. While a healthy microbiome can be represented by a variety of structures, its functional capacity appears to be more important. Gene content of the community can be assessed by "shotgun" metagenomics, but this approach is still too expensive. High-throughput amplicon-based surveys are a method of choice for large-scale surveys of links between microbiome, diseases, and diet, but the algorithms for predicting functional composition need to be improved to achieve good precision. Here we show how feature engineering based on microbial phenotypes, an advanced method for functional prediction from 16S rRNA sequencing data, improves identification of alterations of the gut microbiome linked to the disease. We processed a large collection of published gut microbial datasets of inflammatory bowel disease (IBD) patients to derive their community phenotype indices (CPI)-high-precision semiquantitative profiles aggregating metabolic potential of the community members based on genome-wide metabolic reconstructions. The list of selected metabolic functions included metabolism of short-chain fatty acids, vitamins, and carbohydrates. The machine-learning approach based on microbial phenotypes allows us to distinguish the microbiome profiles of healthy controls from patients with Crohn's disease and from ones with ulcerative colitis. The classifiers were comparable in quality to conventional taxonomy-based classifiers but provided new findings giving insights into possible mechanisms of pathogenesis. Feature-wise partial dependence plot (PDP) analysis of contribution to the classification result revealed a diversity of patterns. These observations suggest a constructive basis for defining functional homeostasis of the healthy human gut microbiome. The developed features are promising interpretable candidate biomarkers for assessing microbiome contribution to disease risk for the purposes of personalized medicine and clinical trials.

Dataset Information

Microvesicles/exosomes as potential novel biomarkers of metabolic diseases.

Publications

Microvesicles/exosomes as potential novel biomarkers of metabolic diseases.

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