Mapping interaction sites within the N-terminus of the calcitonin gene-related peptide receptor; the role of residues 23-60 of the calcitonin receptor-like receptor

The calcitonin receptor-like receptor (CLR) acts as a receptor for the calcitonin gene-related peptide (CGRP) but in order to recognize CGRP, it must form a complex with an accessory protein, receptor activity modifying protein 1 (RAMP1). Identifying the protein/protein and protein/ligand interfaces in this unusual complex would aid drug design. The role of the extreme N-terminus of CLR (Glu23-Ala60) was examined by an alanine scan and the results were interpreted with the help of a molecular model. The potency of CGRP at stimulating cAMP production was reduced at Leu41Ala, Gln45Ala, Cys48Ala and Tyr49Ala; furthermore, CGRP-induced receptor internalization at all of these receptors was also impaired. Ile32Ala, Gly35Ala and Thr37Ala all increased CGRP potency. CGRP specific binding was abolished at Leu41Ala, Ala44Leu, Cys48Ala and Tyr49Ala. There was significant impairment of cell surface expression of Gln45Ala, Cys48Ala and Tyr49Ala. Cys48 takes part in a highly conserved disulfide bond and is probably needed for correct folding of CLR. The model suggests that Gln45 and Tyr49 mediate their effects by interacting with RAMP1 whereas Leu41 and Ala44 are likely to be involved in binding CGRP. Ile32, Gly35 and Thr37 form a separate cluster of residues which modulate CGRP binding. The results from this study may be applicable to other family B GPCRs which can associate with RAMPs.

Predictive gene lists for breast cancer prognosis: a topographic visualisation study

Background: The controversy surrounding the non-uniqueness of predictive gene lists (PGL) of small selected subsets of genes from very large potential candidates as available in DNA microarray experiments is now widely acknowledged 1. Many of these studies have focused on constructing discriminative semi-parametric models and as such are also subject to the issue of random correlations of sparse model selection in high dimensional spaces. In this work we outline a different approach based around an unsupervised patient-specific nonlinear topographic projection in predictive gene lists. Methods: We construct nonlinear topographic projection maps based on inter-patient gene-list relative dissimilarities. The Neuroscale, the Stochastic Neighbor Embedding(SNE) and the Locally Linear Embedding(LLE) techniques have been used to construct two-dimensional projective visualisation plots of 70 dimensional PGLs per patient, classifiers are also constructed to identify the prognosis indicator of each patient using the resulting projections from those visualisation techniques and investigate whether a-posteriori two prognosis groups are separable on the evidence of the gene lists. A literature-proposed predictive gene list for breast cancer is benchmarked against a separate gene list using the above methods. Generalisation ability is investigated by using the mapping capability of Neuroscale to visualise the follow-up study, but based on the projections derived from the original dataset. Results: The results indicate that small subsets of patient-specific PGLs have insufficient prognostic dissimilarity to permit a distinction between two prognosis patients. Uncertainty and diversity across multiple gene expressions prevents unambiguous or even confident patient grouping. Comparative projections across different PGLs provide similar results. Conclusion: The random correlation effect to an arbitrary outcome induced by small subset selection from very high dimensional interrelated gene expression profiles leads to an outcome with associated uncertainty. This continuum and uncertainty precludes any attempts at constructing discriminative classifiers. However a patient's gene expression profile could possibly be used in treatment planning, based on knowledge of other patients' responses. We conclude that many of the patients involved in such medical studies are intrinsically unclassifiable on the basis of provided PGL evidence. This additional category of 'unclassifiable' should be accommodated within medical decision support systems if serious errors and unnecessary adjuvant therapy are to be avoided.