In vivo ribosomal RNA turnover is down-regulated in leukaemic cells in chronic lymphocytic leukaemia: Correspondence

British Journal of Haematology (Impact Factor: 4.71). 10/2010; 151(2). DOI: 10.1111/j.1365-2141.2010.08334.x
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    ABSTRACT: CD4(+) memory cell development is dependent upon T cell receptor (TCR) signal strength, antigen dose and the cytokine milieu, all of which are altered in type 1 diabetes (T1D). We hypothesized that CD4(+) T cell turnover would be greater in type 1 diabetes subjects compared to controls. In vitro studies of T cell function are unable to evaluate dynamic aspects of immune cell homoeostasis. Therefore, we used deuterium oxide ((2) H2 O) to assess in vivo turnover of CD4(+) T cell subsets in T1D (n = 10) and control subjects (n = 10). Serial samples of naive, memory and regulatory (Treg ) CD4(+) T cell subsets were collected and enrichment of deoxyribose was determined by gas chromatography-mass spectrometry (GC-MS). Quantification of T cell turnover was performed using mathematical models to estimate fractional enrichment (f, n = 20), turnover rate (k, n = 20), proliferation (p, n = 10) and disappearance (d*, n = 10). Although turnover of Tregs was greater than memory and naive cells in both controls and T1D subjects, no differences were seen between T1D and controls in Treg or naive kinetics. However, turnover of CD4(+) memory T cells was faster in those with T1D compared to control subjects. Measurement and modelling of incorporated deuterium is useful for evaluating the in vivo kinetics of immune cells in T1D and could be incorporated into studies of the natural history of disease or clinical trials designed to alter the disease course. The enhanced CD4(+) memory T cell turnover in T1D may be important in understanding the pathophysiology and potential treatments of autoimmune diabetes.
    Clinical & Experimental Immunology 06/2013; 172(3):363-74. DOI:10.1111/cei.12064 · 3.04 Impact Factor
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    ABSTRACT: Protein-protein interaction (PPI) networks are associated with multiple types of biases partly rooted in technical limitations of the experimental techniques. Another source of bias are the different frequencies with which proteins have been studied for interaction partners. It is generally believed that proteins with a large number of interaction partners tend to be essential, evolutionarily conserved, and involved in disease. It has been repeatedly reported that proteins driving tumor formation have a higher number of PPI partners. However, it has been noticed before that the degree distribution of PPI networks is biased toward disease proteins, which tend to have been studied more often than non-disease proteins. At the same time, for many poorly characterized proteins no interactions have been reported yet. It is unclear to which extent this study bias affects the observation that cancer proteins tend to have more PPI partners. Here, we show that the degree of a protein is a function of the number of times it has been screened for interaction partners. We present a randomization-based method that controls for this bias to decide whether a group of proteins is associated with significantly more PPI partners than the proteomic background. We apply our method to cancer proteins and observe, in contrast to previous studies, no conclusive evidence for a significantly higher degree distribution associated with cancer proteins as compared to non-cancer proteins when we compare them to proteins that have been equally often studied as bait proteins. Comparing proteins from different tumor types, a more complex picture emerges in which proteins of certain cancer classes have significantly more interaction partners while others are associated with a smaller degree. For example, proteins of several hematological cancers tend to be associated with a higher number of interaction partners as expected by chance. Solid tumors, in contrast, are usually associated with a degree distribution similar to those of equally often studied random protein sets. We discuss the biological implications of these findings. Our work shows that accounting for biases in the PPI network is possible and increases the value of PPI data.
    Frontiers in Genetics 08/2015; 6:260. DOI:10.3389/fgene.2015.00260