Journal club

The University of Manchester, UK.
Nature (Impact Factor: 41.46). 09/2009; 460(7256):669. DOI: 10.1038/460669e
Source: PubMed
5 Reads
  • Source
    • "Misregulation of iron homeostasis has also been reported in PrP Sc replicating cells lines, mouse and hamster models of prion disease, and familial cases associated with PrP102L mutation, demonstrating the generality of this phenomenon across species and experimental models (Fernaeus et al., 2005; Fernaeus and Land, 2005; Petersen et al., 2005; Hur et al., 2002; Kim et al., 2007, Singh et al., 2009). A systems biology approach to this question reveals a similar association between brain iron mis-metabolism and prion disorders, reinforcing the significance of this phenomenon in prion disease pathogenesis (Hwang et al., 2009; Kell, 2009). The underlying cause of this change has been difficult to understand due to the complex nature of brain iron homeostasis compounded further by disease pathogenesis. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Prion disease associated neurotoxicity is mainly attributed to PrP-scrapie (PrP(Sc)), the disease associated isoform of a normal protein, the prion protein (PrP(C)). Participation of other proteins and processes is suspected, but their identity and contribution to the pathogenic process is unclear. Emerging evidence implicates imbalance of brain iron homeostasis as a significant cause of prion disease-associated neurotoxicity. The underlying cause of this change, however, remains unclear. We demonstrate that iron is sequestered in heat and SDS-stable protein complexes in sporadic-Creutzfeldt-Jakob-disease (sCJD) brains, creating a phenotype of iron deficiency. The underlying cause is change in the characteristics of ferritin, an iron storage protein that becomes aggregated, detergent-insoluble, and partitions with denatured ferritin using conventional methods of ferritin purification. A similar phenotype of iron deficiency is noted in the lumbar spinal cord (SC) tissue of scrapie infected hamsters, a site unlikely to be affected by massive neuronal death and non-specific iron deposition. As a result, the iron uptake protein transferrin (Tf) is upregulated in scrapie infected SC tissue, and increases with disease progression. A direct correlation between Tf and PrP(Sc) suggests sequestration of iron in dysfunctional ferritin that either co-aggregates with PrP(Sc) or is rendered dysfunctional by PrP(Sc) through an indirect process. Surprisingly, amplification of PrP(Sc)in vitro by the protein-misfolding-cyclic-amplification (PMCA) reaction using normal brain homogenate as substrate does not increase the heat and SDS-stable pool of iron even though both PrP(Sc) and ferritin aggregate by this procedure. These observations highlight important differences between PrP(Sc)-protein complexes generated in vivo during disease progression and in vitro by the PMCA reaction, and the significance of these complexes in PrP(Sc)-associated neurotoxicity.
    Neurobiology of Disease 12/2011; 45(3):930-8. DOI:10.1016/j.nbd.2011.12.012 · 5.08 Impact Factor
  • Source
    • "In the post-genomic era we are beginning to be able to properly consider molecular biology as the integrated system it evidently is through the burgeoning discipline of systems biology (Kell 2009). Underpinning systems biology are ideas connecting data-rich experimental approaches and computational simulations (Mendes et al. 2009) of the underlying biochemistry to move toward ever more accurate depictions of how life operates at the molecular level. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Text mining methods have added considerably to our capacity to extract biological knowledge from the literature. Recently the field of systems biology has begun to model and simulate metabolic networks, requiring knowledge of the set of molecules involved. While genomics and proteomics technologies are able to supply the macromolecular parts list, the metabolites are less easily assembled. Most metabolites are known and reported through the scientific literature, rather than through large-scale experimental surveys. Thus it is important to recover them from the literature. Here we present a novel tool to automatically identify metabolite names in the literature, and associate structures where possible, to define the reported yeast metabolome. With ten-fold cross validation on a manually annotated corpus, our recognition tool generates an f-score of 78.49 (precision of 83.02) and demonstrates greater suitability in identifying metabolite names than other existing recognition tools for general chemical molecules. The metabolite recognition tool has been applied to the literature covering an important model organism, the yeast Saccharomyces cerevisiae, to define its reported metabolome. By coupling to ChemSpider, a major chemical database, we have identified structures for much of the reported metabolome and, where structure identification fails, been able to suggest extensions to ChemSpider. Our manually annotated gold-standard data on 296 abstracts are available as supplementary materials. Metabolite names and, where appropriate, structures are also available as supplementary materials. Electronic supplementary material The online version of this article (doi:10.1007/s11306-010-0251-6) contains supplementary material, which is available to authorized users.
    Metabolomics 03/2011; 7(1):94-101. DOI:10.1007/s11306-010-0251-6 · 3.86 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Exposure to a variety of toxins and/or infectious agents leads to disease, degeneration and death, often characterised by circumstances in which cells or tissues do not merely die and cease to function but may be more or less entirely obliterated. It is then legitimate to ask the question as to whether, despite the many kinds of agent involved, there may be at least some unifying mechanisms of such cell death and destruction. I summarise the evidence that in a great many cases, one underlying mechanism, providing major stresses of this type, entails continuing and autocatalytic production (based on positive feedback mechanisms) of hydroxyl radicals via Fenton chemistry involving poorly liganded iron, leading to cell death via apoptosis (probably including via pathways induced by changes in the NF-κB system). While every pathway is in some sense connected to every other one, I highlight the literature evidence suggesting that the degenerative effects of many diseases and toxicological insults converge on iron dysregulation. This highlights specifically the role of iron metabolism, and the detailed speciation of iron, in chemical and other toxicology, and has significant implications for the use of iron chelating substances (probably in partnership with appropriate anti-oxidants) as nutritional or therapeutic agents in inhibiting both the progression of these mainly degenerative diseases and the sequelae of both chronic and acute toxin exposure. The complexity of biochemical networks, especially those involving autocatalytic behaviour and positive feedbacks, means that multiple interventions (e.g. of iron chelators plus antioxidants) are likely to prove most effective. A variety of systems biology approaches, that I summarise, can predict both the mechanisms involved in these cell death pathways and the optimal sites of action for nutritional or pharmacological interventions.
    Archives of Toxicology 08/2010; 84(11):825-89. DOI:10.1007/s00204-010-0577-x · 5.98 Impact Factor
Show more