SOM230 inhibits insulin-like growth factor-I action in mammary gland development by pituitary independent mechanism: mediated through somatostatin subtype receptor 3?

Somatostatin analogs (SAs) treat acromegaly by lowering pituitary GH secretion, which, in turn, lowers systemic IGF-I. The profound systemic effect is often greater than expected in the face of only partial GH suppression. Here we report that the SA SOM230 can also act by a nonpituitary-mediated inhibition of IGF-I action. SOM230 inhibited mammary development in intact and hypophysectomized female rats, a process requiring IGF-I. IGF-I overcame this inhibition. SOM230 also inhibited other actions of IGF-I (inhibition of apoptosis, phosphorylation of insulin receptor substrate-1, and cell division). SOM230 did not reduce IGF-I mRNA abundance in mammary gland but did stimulate IGF binding protein 5 (IGFBP5). IGFBP5 was 3.75 times higher in mammary epithelium of SOM230 than in placebo animals (P < 0.001). Administration of IGFBP-5 also inhibited GH-induced mammary development (P < 0.001). Measurement of sstr(1-5) (somatostatin subtype receptor) by real-time RT-PCR revealed that the mammary glands had an abundance of sstr(3) and lower amounts of sstr(4) and sstr(5) but no sstr(1) or sstr(2.) That mammary development was also inhibited to a lesser degree than SOM230 by octreotide, whose main action is through sstr(2), strongly suggests that sstr(3) is at least in part mediating the effects of the SAs. We conclude that 1) SAs inhibit IGF-I action in the mammary gland through a novel nonpituitary mechanism; 2) IGFBP-5, here shown to inhibit pubertal mammary development, might mediate the effect; and 3) Measurement of available sstr receptors in the mammary gland suggests that sstr(3) mediates the SA activity, but sstr(5) is also a possible mediator.     

Evidence that complement protein C1q interacts with C-reactive protein through its globular head region

C1q acts as the recognition unit of the first complement component, C1, and binds to immunoglobulins IgG and IgM, as well as to non-Ig ligands, such as C-reactive protein (CRP). IgG and IgM are recognized via the globular head regions of C1q (C1qGR), whereas CRP has been postulated to interact with the collagen-like region (C1qCLR). In the present study, we used a series of nine mAbs to C1q, five directed against C1qGR and four against C1qCLR, to inhibit the interaction of C1q with CRP. The F(ab')(2) of each of the five mAbs directed against C1qGR inhibited binding of C1q to polymerized IgG. These five mAbs also successfully inhibited the interaction of C1q with CRP. Moreover, these five mAbs inhibited C1 activation by CRP as well as by polymerized IgG in vitro. In contrast, none of the four mAbs against C1qCLR inhibited C1q interaction with CRP or IgG, or could reduce activation of complement by CRP or polymerized IgG. These results provide the first evidence that the interaction of C1q with CRP or IgG involves sites located in the C1qGR, whereas sites in the CLR do not seem to be involved in the physiological interaction of C1q with CRP.     

Reliable gene signatures for microarray classification: assessment of stability and performance

MOTIVATION: Two important questions for the analysis of gene expression measurements from different sample classes are (1) how to classify samples and (2) how to identify meaningful gene signatures (ranked gene lists) exhibiting the differences between classes and sample subsets. Solutions to both questions have immediate biological and biomedical applications. To achieve optimal classification performance, a suitable combination of classifier and gene selection method needs to be specifically selected for a given dataset. The selected gene signatures can be unstable and the resulting classification accuracy unreliable, particularly when considering different subsets of samples. Both unstable gene signatures and overestimated classification accuracy can impair biological conclusions. METHODS: We address these two issues by repeatedly evaluating the classification performance of all models, i.e. pairwise combinations of various gene selection and classification methods, for random subsets of arrays (sampling). A model score is used to select the most appropriate model for the given dataset. Consensus gene signatures are constructed by extracting those genes frequently selected over many samplings. Sampling additionally permits measurement of the stability of the classification performance for each model, which serves as a measure of model reliability. RESULTS: We analyzed a large gene expression dataset with 78 measurements of four different cartilage sample classes. Classifiers trained on subsets of measurements frequently produce models with highly variable performance. Our approach provides reliable classification performance estimates via sampling. In addition to reliable classification performance, we determined stable consensus signatures (i.e. gene lists) for sample classes. Manual literature screening showed that these genes are highly relevant to our gene expression experiment with osteoarthritic cartilage. We compared our approach to others based on a publicly available dataset on breast cancer. AVAILABILITY: R package at http://www.bio.ifi.lmu.de/~davis/edaprakt     

A new representation for protein secondary structure prediction based on frequent patterns

MOTIVATION: A new representation for protein secondary structure prediction based on frequent amino acid patterns is described and evaluated. We discuss in detail how to identify frequent patterns in a protein sequence database using a level-wise search technique, how to define a set of features from those patterns and how to use those features in the prediction of the secondary structure of a protein sequence using support vector machines (SVMs). RESULTS: Three different sets of features based on frequent patterns are evaluated in a blind testing setup using 150 targets from the EVA contest and compared to predictions of PSI-PRED, PHD and PROFsec. Despite being trained on only 940 proteins, a simple SVM classifier based on this new representation yields results comparable to PSI-PRED and PROFsec. Finally, we show that the method contributes significant information to consensus predictions. AVAILABILITY: The method is available from the authors upon request.     

Anthrax in Western and Central African great apes

During the period of December 2004 to January 2005, Bacillus anthracis killed three wild chimpanzees (Pan troglodytes troglodytes) and one gorilla (Gorilla gorilla gorilla) in a tropical forest in Cameroon. While this is the second anthrax outbreak in wild chimpanzees, this is the first case of anthrax in gorillas ever reported. The number of great apes in Central Africa is dramatically declining and the populations are seriously threatened by diseases, mainly Ebola. Nevertheless, a considerable number of deaths cannot be attributed to Ebola virus and remained unexplained. Our results show that diseases other than Ebola may also threaten wild great apes, and indicate that the role of anthrax in great ape mortality may have been underestimated. These results suggest that risk identification, assessment, and management for the survival of the last great apes should be performed with an open mind, since various pathogens with distinct characteristics in epidemiology and pathogenicity may impact the populations. An animal mortality monitoring network covering the entire African tropical forest, with the dual aims of preventing both great ape extinction and human disease outbreaks, will create necessary baseline data for such risk assessments and management plans.     

Influence of reduction of heme a and Cu(A) on the oxidized catalytic center of cytochrome c oxidase: insight from organic solvents

Purified bovine heart cytochrome c oxidase (CcO) has been extracted from aqueous solution into hexane in the presence of phospholipids and calcium ions. In extracts, CcO is in the so-called "slow" form and probably situated in reverse micelles. At low water:phospholipid molar ratios, electron transfer from reduced heme a and Cu(A) to the catalytic center is inhibited and both heme a3 and Cu(B) remain in the oxidized state. The rate of binding of cyanide to heme a3 in this oxidized catalytic center is, however, dependent on the redox state of heme a and Cu(A). When heme a and Cu(A) are reduced, the rate is increased 20-fold compared to the rate when these two centers are oxidized. The enhanced rate of binding of cyanide to heme a3 is explained by the destabilization of an intrinsic ligand, located at the catalytic site, that is triggered by the reduction of heme a and Cu(A).     

Nuclear envelope localization of human UNC84A does not require nuclear lamins

The SUN proteins are a conserved family of proteins in eukaryotes. Human UNC84A (Sun1) is a homolog of Caenorhabditis elegans UNC-84, a protein involved in nuclear anchorage and migration. We have analyzed targeting of UNC84A to the nuclear envelope (NE) and show that the N-terminal 300 amino acids are crucial for efficient NE localization of UNC84A whereas the conserved C-terminal SUN domain is not required. Furthermore, we demonstrate by combining RNA interference with immunofluorescence and fluorescence recovery after photobleaching analysis that localization and anchoring of UNC84A is not dependent on the lamin proteins, in contrast to what had been observed for C. elegans UNC-84.     

Diagnosing benign and malignant lesions in breast tissue sections by using IR-microspectroscopy

The collection of IR spectra through microscope optics and the visualization of the IR data by IR imaging represent a visualization approach, which uses infrared spectral features as a native intrinsic contrast mechanism. To illustrate the potential of this spectroscopic methodology in breast cancer research, we have acquired IR-microspectroscopic data from benign and malignant lesions in breast tissue sections by point microscopy with spot sizes of 30-40 μm. Four classes of distinct breast tissue spectra were defined and stored in the data base: fibroadenoma (a total of 1175 spectra from 14 patients), ductal carcinoma in situ (a total of 1349 spectra from 8 patients), connective tissue (a total of 464 spectra), and adipose tissue (a total of 146 spectra). Artifical neural network analysis, a supervised pattern recognition method, was used to develop an automated classifier to separate the four classes. After training the artifical neural network classifier, infrared spectra of independent external validation data sets (“unknown spectra”) were analyzed. In this way, all spectra (a total of 386) taken from micro areas inside the epithelium of fibroadenomas from 4 patients were correctly classified. Out of the 421 spectra taken from micro areas of the in situ component of invasive ductal carcinomas of 3 patients, 93% were correctly identified. Based on these results, the potential of the IR-microspectroscopic approach for diagnosing breast tissue lesions is discussed.     

Predicting nucleic acid binding interfaces from structural models of proteins

The function of DNA‐ and RNA‐binding proteins can be inferred from the characterization and accurate prediction of their binding interfaces. However, the main pitfall of various structure‐based methods for predicting nucleic acid binding function is that they are all limited to a relatively small number of proteins for which high‐resolution three‐dimensional structures are available. In this study, we developed a pipeline for extracting functional electrostatic patches from surfaces of protein structural models, obtained using the I‐TASSER protein structure predictor. The largest positive patches are extracted from the protein surface using the patchfinder algorithm. We show that functional electrostatic patches extracted from an ensemble of structural models highly overlap the patches extracted from high‐resolution structures. Furthermore, by testing our pipeline on a set of 55 known nucleic acid binding proteins for which I‐TASSER produces high‐quality models, we show that the method accurately identifies the nucleic acids binding interface on structural models of proteins. Employing a combined patch approach we show that patches extracted from an ensemble of models better predicts the real nucleic acid binding interfaces compared with patches extracted from independent models. Overall, these results suggest that combining information from a collection of low‐resolution structural models could be a valuable approach for functional annotation. We suggest that our method will be further applicable for predicting other functional surfaces of proteins with unknown structure. Proteins 2012. © 2011 Wiley Periodicals, Inc.