Sterol transport by the human breast cancer resistance protein (ABCG2) expressed in Lactococcus lactis

The human breast cancer resistance protein (BCRP, also know as ABCG2, MXR, or ABCP) is one of the more recently discovered ATP-binding cassette (ABC) transporters that confer resistance on cancer cells by mediating multidrug efflux. In the present study, we have obtained functional expression of human BCRP in the Gram-positive bacterium Lactococcus lactis. BCRP expression conferred multidrug resistance on the lactococcal cells, which was based on ATP-dependent drug extrusion. BCRP-mediated ATPase and drug transport activities were inhibited by the BCRP-specific modulator fumitremorgin C. To our knowledge these data represent the first example of the functional expression of a mammalian ABC half-transporter in bacteria. Although members of the ABCG subfamily (such as ABCG1 and ABCG5/8) have been implicated in the transport of sterols, such a role has not yet been established for BCRP. Interestingly, the BCRP-associated ATPase activity in L. lactis was significantly stimulated by (i) sterols including cholesterol and estradiol, (ii) natural steroids such as progesterone and testosterone, and (iii) the anti-estrogen anticancer drug tamoxifen. In addition, BCRP mediated the efflux of [3H]estradiol from lactococcal cells. Our findings suggest that BCRP may play a role in the transport of sterols in human, in addition to its ability to transport multiple drugs and toxins.     

Can normal mode analysis reveal the geometry of the L550 chromophore of bacteriorhodopsin?

We discuss to what extent recent vibrational spectra of 14-²H, 15-²H and 14,15-²H isotopically labelled L₅₅₀ provide evidence for the occurrence of 13-cis, 14-s-trans or 13-cis, 14-s-cis chromophore structures in bacteriorhodopsin's photocycle. The discussion is based on a quantum chemical (MNDO) vibrational analysis of four molecular fragments as models for the retinal chromophore in bacteriorhodopsin.     

Self-organization of associative memory and pattern classification: recurrent signal processing on topological feature maps

We extend the neural concepts of topological feature maps towards self-organization of auto-associative memory and hierarchical pattern classification. As is well-known, topological maps for statistical data sets store information on the associated probability densities. To extract that information we introduce a recurrent dynamics of signal processing. We show that the dynamics converts a topological map into an auto-associative memory for real-valued feature vectors which is capable to perform a cluster analysis. The neural network scheme thus developed represents a generalization of non-linear matrix-type associative memories. The results naturally lead to the concept of a feature atlas and an associated scheme of self-organized, hierarchical pattern classification.