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University of Edinburgh - Articles and news items
In recent years, mass spectrometry (MS) based proteomics has moved from being a qualitative tool (used to mainly identify proteins) to a more reliable analysis tool, allowing relative quantitation as well as absolute quantitation of a large number of proteins. However, the developed quantitative methods are either specific for certain types of samples or certain types of mass spectrometers. In some cases, developing expertise on how to use a given method may take a long time and the use of these methods is therefore limited to few laboratories. Other quantitative methods are suitable for simple standard protein mixes which are far from the complexity of real samples. As a consequence, the number of available quantitative methods is high and choosing the right one is challenging.
microRNAs (miRNA) are a class of non-coding RNA that regulate the precise amounts of proteins expressed in a cell at a given time. These molecules were discovered in worms in 1993 and only known to exist in humans in the last decade. Despite the youth of the miRNA field, miRNA misexpression is known to occur in a range of human disease conditions and drugs based on modulating miRNA expression are now in development for treatment of cancer, cardiovascular, metabolic and inflammatory diseases. In the last six years, an increasing number of reports have also illuminated diverse roles of cellular miRNAs in viral infection and a miRNA-targeting therapy is currently in phase II clinical trials for treatment of the Hepatitis C virus. Here we review the literature related to miRNAs that regulate viral replication and highlight the factors that will influence the use of miRNA manipulation as a broader antiviral therapeutic strategy.
microRNAs (miRNA) are a class of small noncoding RNA that bind to messenger RNAs (mRNA) and regulate the amount of specific proteins that get expressed. These small RNAs are derived from longer primary transcripts that fold back on themselves to produce stem-loop structures which are recognised and processed by Drosha and co-factors in the nucleus followed by Dicer and co-factors in the cytoplasm, resulting in a ~ 22 nucleotide (nt) duplex RNA, for review see1,2. One strand of the duplex is preferentially incorporated into the RNA-induced silencing complex (RISC) where it then mediates binding to target mRNAs. These interactions lead to decreased protein getting produced from the transcript, due to RNA destabilisation and/or inhibited translation3 (Figure 1). miRNA-mRNA recognition generally requires perfect complementarity with only the first 6-8 nt of a miRNA, termed the ‘seed’ site4. Each miRNA therefore has the potential to interact with hundreds of target mRNAs3,4 and the majority of human protein-coding genes contain miRNA binding sites under selective pressure5. Therapeutic interest in miRNAs has been supported by studies in model organisms demonstrating key functions of individual miRNAs in cancer, cardiac disease, metabolic disease, neuronal and immune cell function6.
Applying statistical inference in genomics with evidence based pathways: Towards elucidating new functional correlations of biomarkers
Cancer Biology, Issue 1 2010 / 22 February 2010 / Peter Ghazal, Professor of Molecular Genetics and Biomedicine, University of Edinburgh and Head of Division of Pathway Medicine and Associate Director of Centre for Systems Biology, Edinburgh, Al Ivens, Head of Data Analysis, Fios Genomics Ltd and Thorsten Forster, Statistical Bioinformatician, Division of Pathway Medicine, University of Edinburgh
In conventional pharmacogenomic studies, genetic polymorphisms (including single nucleotide and copy number variations) are elucidated from case-control distribution of individuals usually representing ethnicity, severity of disease, and positive or negative response to treatment. However, the interpretation of a single genetic marker in this context is complicated, as the same marker may lead to multiple different phenotypes. Likewise, similar phenotypes can arise under different genetic backgrounds (models). This problem has led to the emergence of integrative approaches for combining statistical inference methods with bioinformatics-based text-mining, sequencing and expression analyses. An increasingly popular approach based on network analysis is challenged by the fact that a certain amount of the interpretation of the data is left to the subjective choices of the user. More rigorous statistical methods can be applied. However, these methods are also challenged by the fact that the developed measures of significance are provided outside the context of biology. In this perspective, we outline statistical methods and the development of evidence-based pathway analysis for identifying new biomarker correlations.
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