Methods in molecular biology
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Recent advancements in high-throughput technologies such as mass spectrometry have led to an increase in the rate at which data is generated and accumulated. As a result, standard statistical methods no longer suffice as a way of analyzing such gigantic amounts of data. Network analysis, the evaluation of how nodes relate to one another, has over the years become an integral tool for analyzing high throughput proteomic data as they provide a structure that helps reduce the complexity of the underlying data. ⋯ These tools enable the visualization of proteins as networks of signaling, regulatory, and biochemical interactions. In this chapter, we provide an overview of networks and network theory fundamentals for the analysis of proteomics data. We further provide an overview of interaction databases and network tools which are frequently used for analyzing proteomics data.
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Detection of Differential DNA Methylation Under Stress Conditions Using Bisulfite Sequence Analysis.
DNA methylation is the most important epigenetic change affecting gene expression in plants grown under normal as well as under stress conditions. Therefore, researchers study differential DNA methylation under distinct environmental conditions and their relationship with transcriptome abundance. Up to date, more than 25 methods and techniques are available to detect DNA methylation based on different principles. ⋯ This technique allows a single nucleotide resolution of 5-methylcytosine on a genome scale. WGBS technique workflow involves DNA fragmentation, processing through end blunting, terminal A(s) addition at 3' end and adaptor ligation, bisulfite treatment, PCR amplification, sequencing libraries and assembling, and finally alignment with the reference genome and data analysis. Despite the fact that WGBS is more reliable than the conventional clone-based bisulfite sequencing, it is costly, requires large amount of DNA and its output data is not easily handled.
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Extracellular vesicle (EV)-associated RNAs (EV-RNA) are under intense investigation due to their potential role in health and disease. Several approaches are currently employed to isolate blood-derived EVs for RNA analysis, most of which are either time-consuming and expensive, such as methods based on EVs physical properties (ultracentrifugation and Optiprep density gradient), or also copurify blood contaminants, mostly protein aggregates and immune complexes, (such as chemical precipitation). In addition, there is a lack of standardized protocols for the extraction of EV-RNA and very little consensus on the technological platforms and normalization tools for assessing the expression levels of different RNA species. ⋯ In this book chapter we propose a protocol that might overcome some of the abovementioned issues through antibody-based isolation of blood-derived EVs followed by extraction and expression analysis of small-RNA species (miRNA) by reverse transcriptase quantitative PCR (RT-qPCR). The advantages of immunoaffinity approaches over other isolation methods are multiple and include: (1) the selective enrichment of specific EV subpopulations with restricted tissue/cell origin, (2) reduction of matrix effects and blood contaminants that may confound miRNA profiling from complex biological fluids and (3) easy coupling to conventional quantitative assays (e.g., RT-qPCR). In conclusion, we describe a protocol for standard enrichment and quantitative analysis of EV-miRNAs from blood and we warrant for technological improvements, such as the use of novel biomaterials, surface chemistries, binding agents and assay/sensor design that may further improve it.
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Even though it is a pandemic health problem worldwide, the pathogenesis of obesity is poorly understood. Recently, emerging studies verified that microRNAs (miRNAs) are involved in complicated metabolic processes including adipocyte differentiation, fat cell formation (adipogenesis), obesity-related insulin resistance and inflammation. ⋯ MiRNAs may play an important part in regulating metabolic functions in adipose tissues and, by extension, obesity and its associated disorders. Consequently, they may be potential candidates for therapeutic targets and biomarkers.
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Direct oral anticoagulants (DOACs) can be quantified using methods that can be performed in any clinical or research laboratory using manual or automated instrument platforms. Dabigatran etexilate, the oral direct thrombin inhibitor, can be quantified by drug-calibrated clot or chromogenic-based assays using either thrombin or ecarin as substrates. Oral direct anti-Xa inhibitors, such as rivaroxaban, apixaban, and edoxaban, can be quantified with drug-calibrated anti-Xa kits or reagents as typically used for measuring heparins (unfractionated, low molecular weight, or pentasaccharides).