Comparison of the molecular properties of retinitis pigmentosa P23H and N15S amino acid replacements in rhodopsin

Mutations in the RHO gene encoding for the visual pigment protein, rhodopsin, are among the most common cause of autosomal dominant retinitis pigmentosa (ADRP). Previous studies of ADRP mutations in different domains of rhodopsin have indicated that changes that lead to more instability in rhodopsin structure are responsible for more severe disease in patients. Here, we further test this hypothesis by comparing side-by-side and therefore quantitatively two RHO mutations, N15S and P23H, both located in the N-terminal intradiscal domain. The in vitro biochemical properties of these two rhodopsin proteins, expressed in stably transfected tetracycline-inducible HEK293S cells, their UV-visible absorption, their Fourier transform infrared, circular dichroism and Metarhodopsin II fluorescence spectroscopy properties were characterized. As compared to the severely impaired P23H molecular function, N15S is only slightly defective in structure and stability. We propose that the molecular basis for these structural differences lies in the greater distance of the N15 residue as compared to P23 with respect to the predicted rhodopsin folding core. As described previously for WT rhodopsin, addition of the cytoplasmic allosteric modulator chlorin e6 stabilizes especially the P23H protein, suggesting that chlorin e6 may be generally beneficial in the rescue of those ADRP rhodopsin proteins whose stability is affected by amino acid replacement.

The anti-promyelocytic leukemia mode of action of two endophytic secondary metabolites unveiled by a proteomic approach

As a result of a program to find antitumor compounds of endophytes from medicinal Asteraceae, the steroid (22E,24R)-8,14-epoxyergosta-4,22-diene-3,6-dione (a) and the diterpene aphidicolin (b) were isolated from the filamentous fungi Papulaspora immersa and Nigrospora sphaerica, respectively, and exhibited strong cytotoxicity against HL-60 cells. A proteomic approach was used in an attempt to identify the drugs' molecular targets and their respective antiproliferative mode of action. Results suggested that the (a) growth inhibition effect occurs by G2/M cell cycle arrest via reduction of tubulin alpha and beta isomers and 14-3-3 protein gamma expression, followed by a decrease of apoptotic and inflammatory proteins, culminating in mitochondrial oxidative damage that triggered autophagy-associated cell death. Moreover, the decrease observed in the expression levels of several types of histones indicated that (a) might be disarming oncogenic pathways via direct modulation of the epigenetic machinery. Effects on cell cycle progression and induction of apoptosis caused by (b) were confirmed. In addition, protein expression profiles also revealed that aphidicolin is able to influence microtubule dynamics, modulate proteasome activator complex expression, and control the inflammatory cascade through overexpression of thymosin beta 4, RhoGDI2, and 14-3-3 proteins. Transmission electron micrographs of (b)-treated cells unveiled dose-dependent morphological characteristics of autophagy- or oncosis-like cell death.

Protein complex identification by supervised graph local clustering

MOTIVATION

Protein complexes integrate multiple gene products to coordinate many biological functions. Given a graph representing pairwise protein interaction data one can search for subgraphs representing protein complexes. Previous methods for performing such search relied on the assumption that complexes form a clique in that graph. While this assumption is true for some complexes, it does not hold for many others. New algorithms are required in order to recover complexes with other types of topological structure.

RESULTS

We present an algorithm for inferring protein complexes from weighted interaction graphs. By using graph topological patterns and biological properties as features, we model each complex subgraph by a probabilistic Bayesian network (BN). We use a training set of known complexes to learn the parameters of this BN model. The log-likelihood ratio derived from the BN is then used to score subgraphs in the protein interaction graph and identify new complexes. We applied our method to protein interaction data in yeast. As we show our algorithm achieved a considerable improvement over clique based algorithms in terms of its ability to recover known complexes. We discuss some of the new complexes predicted by our algorithm and determine that they likely represent true complexes.

AVAILABILITY

Matlab implementation is available on the supporting website: www.cs.cmu.edu/~qyj/SuperComplex.