Randall Division of Cell and Molecular Biophysics

About: Randall Division of Cell and Molecular Biophysics is a based out in . It is known for research contribution in the topics: Actin cytoskeleton & Skeletal muscle. The organization has 576 authors who have published 1229 publications receiving 78279 citations.

AllergoOncology – the impact of allergy in oncology: EAACI position paper

Abstract: Th2 immunity and allergic immune surveillance play critical roles in host responses to pathogens, parasites and allergens. Numerous studies have reported significant links between Th2 responses and cancer, including insights into the functions of IgE antibodies and associated effector cells in both antitumour immune surveillance and therapy. The interdisciplinary field of AllergoOncology was given Task Force status by the European Academy of Allergy and Clinical Immunology in 2014. Affiliated expert groups focus on the interface between allergic responses and cancer, applied to immune surveillance, immunomodulation and the functions of IgE-mediated immune responses against cancer, to derive novel insights into more effective treatments. Coincident with rapid expansion in clinical application of cancer immunotherapies, here we review the current state-of-the-art and future translational opportunities, as well as challenges in this relatively new field. Recent developments include improved understanding of Th2 antibodies, intratumoral innate allergy effector cells and mediators, IgE-mediated tumour antigen cross-presentation by dendritic cells, as well as immunotherapeutic strategies such as vaccines and recombinant antibodies, and finally, the management of allergy in daily clinical oncology. Shedding light on the crosstalk between allergic response and cancer is paving the way for new avenues of treatment.

Comparative analysis of homology models of the AH receptor ligand binding domain: verification of structure-function predictions by site-directed mutagenesis of a nonfunctional receptor.

Abstract: The aryl hydrocarbon receptor (AHR) is a ligand-dependent transcription factor that mediates the biological and toxic effects of a wide variety of structurally diverse chemicals, including the toxic environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). While significant interspecies differences in AHR ligand binding specificity, selectivity and response have been observed, the structural determinants responsible have not been determined and homology models of the AHR ligand-binding domain (LBD) are available for only a few species. Here we describe the development and comparative analysis of homology models of the LBD of sixteen AHRs from twelve mammalian and nonmammalian species and identify the specific residues contained within their ligand binding cavities. The ligand-binding cavity of the fish AHR exhibits differences from mammalian and avian AHRs, suggesting a slightly different TCDD binding mode. Comparison of the internal cavity in the LBD model of zebrafish (zf) AHR2, which binds TCDD with high affinity, to that of zfAHR1a, which does not bind TCDD, revealed that the latter has a dramatically shortened binding cavity due to the side chains of three residues (Tyr296, Thr386, His388) that reduce the internal space available to TCDD. Mutagenesis of two of these residues in zfAhR1a to those present in zfAHR2 (Y296H, T386A) restored the ability of zfAHR1a to bind TCDD and to exhibit TCDD-dependent binding to DNA. These results demonstrate the importance of these two amino acids and highlight the predictive potential of comparative analysis of homology models from diverse species. The availability of these AHR LBD homology models will facilitate in depth comparative studies of AHR ligand binding and ligand-dependent AHR

Entropies of complex networks with hierarchically constrained topologies.

Abstract: The entropy of a hierarchical network topology in an ensemble of sparse random networks, with "hidden variables" associated with its nodes, is the log-likelihood that a given network topology is present in the chosen ensemble. We obtain a general formula for this entropy, which has a clear interpretation in some simple limiting cases. The results provide keys with which to solve the general problem of "fitting" a given network with an appropriate ensemble of random networks.