The structural effects of divalent cations and insulin on phospholipid model membranes /

It is well accepted that structural studies with model membranes are of considerable value in understanding the structure of biological membranes. Many studies with models of pure phospholipids have been done; but the effects of divalent cations and protein on these models would make these studies more applicable to intact membrane. The present study, performed with above view, is a structural analysis of divalent io~cardio1ipin complexes using the technique of x-ray diffraction. Cardiolipin, precipitated from dilute solution by divalent ionscalcium, magnesium and barium, contains little water and the structure formed is similar to the structure of pure cardiolipin with low water content. The calcium-cardiolipin complex forms a pure hexagonal type II phase that exists from 40 to 400 C. The molar ratio of calcium and cardiolipin in the complex is 1 : 1. Cardiolipin, precipitated with magnesium and barium forms two co-existing phases, lamellar and hexagonal, the relative quantity of the two phases being dependent on temperature. The hexagonal phase type II consisting of water filled channels formed by adding calcium to cardiolipin may have a remarkable permeability property in intact membrane. Pure cardiolipin and insulin at pH 3.0 and 4.0 precipitate but form no organised structure. Lecithin/cardiolipin and insulin precipitated at pH 3.0 give a pure lamellar phase. As the lecithin/cardiolipin molar ratio changes from 93/7 to SO/50, (a) the repeat distance of the lamellar changes from 72.8 X to 68.2 A; (b) the amount of protein bound increases in such a way that cardiolipin/insulin molar ratio in the complex reaches a maximum constant value at lecithin/cardiolipin molar ratio 70/30. A structural model based on these data shows that the molecular arrangement of lipid and protein is a lipid bilayer coated with protein molecules. The lipid-protein interaction is chiefly electrostatic and little, if any, hydrophobic bonding occurs in this particular system. So, the proposed model is essentially the same as Davson-Daniellifs model of biological membrane.

Disease-Gene Association Using a Genetic Algorithm

Understanding the relationship between genetic diseases and the genes associated with them is an important problem regarding human health. The vast amount of data created from a large number of high-throughput experiments performed in the last few years has resulted in an unprecedented growth in computational methods to tackle the disease gene association problem. Nowadays, it is clear that a genetic disease is not a consequence of a defect in a single gene. Instead, the disease phenotype is a reflection of various genetic components interacting in a complex network. In fact, genetic diseases, like any other phenotype, occur as a result of various genes working in sync with each other in a single or several biological module(s). Using a genetic algorithm, our method tries to evolve communities containing the set of potential disease genes likely to be involved in a given genetic disease. Having a set of known disease genes, we first obtain a protein-protein interaction (PPI) network containing all the known disease genes. All the other genes inside the procured PPI network are then considered as candidate disease genes as they lie in the vicinity of the known disease genes in the network. Our method attempts to find communities of potential disease genes strongly working with one another and with the set of known disease genes. As a proof of concept, we tested our approach on 16 breast cancer genes and 15 Parkinson's Disease genes. We obtained comparable or better results than CIPHER, ENDEAVOUR and GPEC, three of the most reliable and frequently used disease-gene ranking frameworks.

Regulation of Systemic Acquired Resistance through the Interaction of Arabidopsis thaliana Transcription factors TGAI and TGA2 with NPRI

Arabidopsis thaliana is an established model plant system for studying plantpathogen interactions. The knowledge garnered from examining the mechanism of induced disease resistance in this model system can be applied to eliminate the cost and danger associated with current means of crop protection. A specific defense pathway, known as systemic acquired resistance (SAR), involves whole plant protection from a wide variety of bacterial, viral and fungal pathogens and remains induced weeks to months after being triggered. The ability of Arabidopsis to mount SAR depends on the accumulation of salicylic acid (SA), the NPRI (non-expressor of pathogenesis related gene 1) protein and the expression of a subset of pathogenesis related (PR) genes. NPRI exerts its effect in this pathway through interaction with a closely related class of bZIP transcription factors known as TGA factors, which are named for their recognition of the cognate DNA motif TGACG. We have discovered that one of these transcription factors, TGA2, behaves as a repressor in unchallenged Arabidopsis and acts to repress NPRI-dependent activation of PRJ. TGA1, which bears moderate sequence similarity to TGA2, acts as a transcriptional activator in unchallenged Arabidopsis, however the significance of this activity is J unclear. Once SAR has been induced, TGAI and TGA2 interact with NPRI to form complexes that are capable of activating transcription. Curiously, although TGAI is capable of transactivating, the ability of the TGAI-NPRI complex to activate transcription results from a novel transactivation domain in NPRI. This transactivation domain, which depends on the oxidation of cysteines 521 and 529, is also responsible for the transactivation ability of the TGA2-NPRI complex. Although the exact mechanism preventing TGA2-NPRI interaction in unchallenged Arabidopsis is unclear, the regulation of TGAI-NPRI interaction is based on the redox status of cysteines 260 and 266 in TGAl. We determined that a glutaredoxin, which is an enzyme capable of regulating a protein's redox status, interacts with the reduced form of TGAI and this interaction results .in the glutathionylation of TGAI and a loss of interaction with NPRl. Taken together, these results expand our understanding of how TGA transcription factors and NPRI behave to regulate events and gene expression during SAR. Furthermore, the regulation of the behavior of both TGAI and NPRI by their redox status and the involvement of a glutaredoxin in modulating TGAI-NPRI interaction suggests the redox regulation of proteins is a general mechanism implemented in SAR.