Novel proteins regulated by mTOR in subependymal giant cell astrocytomas of patients with tuberous sclerosis complex and new therapeutic implications.

Subependymal giant cell astrocytomas (SEGAs) are rare brain tumors associated with tuberous sclerosis complex (TSC), a disease caused by mutations in TSC1 or TSC2, resulting in enhancement of mammalian target of rapamycin (mTOR) activity, dysregulation of cell growth, and tumorigenesis. Signaling via mTOR plays a role in multifaceted genomic responses, but its effectors in the brain are largely unknown. Therefore, gene expression profiling on four SEGAs was performed with Affymetrix Human Genome arrays. Of the genes differentially expressed in TSC, 11 were validated by real-time PCR on independent tumor samples and 3 SEGA-derived cultures. Expression of several proteins was confirmed by immunohistochemistry. The differentially-regulated proteins were mainly involved in tumorigenesis and nervous system development. ANXA1, GPNMB, LTF, RND3, S100A11, SFRP4, and NPTX1 genes were likely to be mTOR effector genes in SEGA, as their expression was modulated by an mTOR inhibitor, rapamycin, in SEGA-derived cells. Inhibition of mTOR signaling affected size of cultured SEGA cells but had no influence on their proliferation, morphology, or migration, whereas inhibition of both mTOR and extracellular signal-regulated kinase signaling pathways led to significant alterations of these processes. For the first time, we identified genes related to the occurrence of SEGA and regulated by mTOR and demonstrated an effective modulation of SEGA growth by pharmacological inhibition of both mTOR and extracellular signal-regulated kinase signaling pathways, which could represent a novel therapeutic approach.

Probe set filtering increases correlation between Affymetrix GeneChip and qRT-PCR expression measurements.

BACKGROUND: Affymetrix GeneChip microarrays are popular platforms for expression profiling in two types of studies: detection of differential expression computed by p-values of t-test and estimation of fold change between analyzed groups. There are many different preprocessing algorithms for summarizing Affymetrix data. The main goal of these methods is to remove effects of non-specific hybridization, and to optimally combine information from multiple probes annotated to the same transcript. The methods are benchmarked by comparison with reference methods, such as quantitative reverse-transcription PCR (qRT-PCR). RESULTS: We present a comprehensive analysis of agreement between Affymetrix GeneChip and qRT-PCR results. We analyzed the influence of filtering by fraction Present calls introduced by J.N. McClintick and H.J. Edenberg (2006) and 2 mapping procedures: updated probe sets definitions proposed by Dai et al. (2005) and our "naive mapping" method. Because of evolution of genome sequence annotations since the time when microarrays were designed, we also studied the effect of the annotation release date. These comparisons were prepared for 6 popular preprocessing algorithms (MAS5, PLIER, RMA, GC-RMA, MBEI, and MBEImm) in the 2 above-mentioned types of studies. We used data sets from 6 independent biological experiments. As a measure of reproducibility of microarray and qRT-PCR values, we used linear and rank correlation coefficients. CONCLUSIONS: We show that filtering by fraction Present calls increased correlations for all 6 preprocessing algorithms. We observed the difference in performance of PM-MM and PM-only methods: using MM probes increased correlations in fold change studies, but PM-only methods proved to perform better in detection of differential expression. We recommend using GC-RMA for detection of differential expression and PLIER for estimation of fold change. The use of the more recent annotation improves the results in both types of studies, encouraging re-analysis of old data.

AAindex: amino acid index database, progress report 2008.

AAindex is a database of numerical indices representing various physicochemical and biochemical properties of amino acids and pairs of amino acids. We have added a collection of protein contact potentials to the AAindex as a new section. Accordingly AAindex consists of three sections now: AAindex1 for the amino acid index of 20 numerical values, AAindex2 for the amino acid substitution matrix and AAindex3 for the statistical protein contact potentials. All data are derived from published literature. The database can be accessed through the DBGET/LinkDB system at GenomeNet (http://www.genome.jp/dbget-bin/www_bfind?aaindex) or downloaded by anonymous FTP (ftp://ftp.genome.jp/pub/db/community/aaindex/).

Plant response to N availability in permafrost-affected alpine wetlands in arid and semi-arid climate zones.

Nutrient cycling in alpine permafrost-affected wetlands remains insufficiently studied, as it is influenced by a complex network of interrelated climatic and environmental factors, at both regional and local scale. Therefore, we applied mathematical models to examine relationship between environmental factors and plant functional traits reflecting N availability in wetland communities developed under locally variable conditions in a geographic and climatic gradient of high-altitude habitats. Moreover, we assessed impact of local differences in soil chemistry on plant fractionation of N isotopes as a response to N availability. Based on environmental data and chemistry of biomass from 192 study sites from the Pamir Mountains (Tajikistan) and Khangai and Khentei Mountains (Mongolia), a matrix of rank correlations was prepared for regional and local factors and community level plant functional traits. For the traits that were highly correlated either with regional or with local drivers (that is plant N:P ratio and plant δ15N), linear models were built, with a limited set of predictors selected according to the Risk Inflation Criterion and the SOS algorithm. The models were fitted for each of the studied regions. Presented regional models indicated significant influence of soil NH4+ and/or PO43- content on plant N:P ratio, which showed increase with altitude and lowering precipitation. Thus, its values clearly distinguished between the Pamir Mountains (high N:P) and the Mongolian ranges (low N:P). Models for plant δ15N showed its strong positive correlations with soil δ15N and soil salinity. Average values of plant δ15N were comparable for both study areas. The studied plant functional traits showed different response to regional and local drivers. Plant N:P ratio was controlled by regional drivers via their influence on soil NH4+ content. Contrastingly, plant δ15N was significantly affected by local factors, namely soil δ15N and soil salinity expressed as Na:EC.

Distance matrix-based approach to protein structure prediction.

Much structural information is encoded in the internal distances; a distance matrix-based approach can be used to predict protein structure and dynamics, and for structural refinement. Our approach is based on the square distance matrix D = [r(ij)(2)] containing all square distances between residues in proteins. This distance matrix contains more information than the contact matrix C, that has elements of either 0 or 1 depending on whether the distance r (ij) is greater or less than a cutoff value r (cutoff). We have performed spectral decomposition of the distance matrices D = sigma lambda(k)V(k)V(kT), in terms of eigenvalues lambda kappa and the corresponding eigenvectors v kappa and found that it contains at most five nonzero terms. A dominant eigenvector is proportional to r (2)--the square distance of points from the center of mass, with the next three being the principal components of the system of points. By predicting r (2) from the sequence we can approximate a distance matrix of a protein with an expected RMSD value of about 7.3 A, and by combining it with the prediction of the first principal component we can improve this approximation to 4.0 A. We can also explain the role of hydrophobic interactions for the protein structure, because r is highly correlated with the hydrophobic profile of the sequence. Moreover, r is highly correlated with several sequence profiles which are useful in protein structure prediction, such as contact number, the residue-wise contact order (RWCO) or mean square fluctuations (i.e. crystallographic temperature factors). We have also shown that the next three components are related to spatial directionality of the secondary structure elements, and they may be also predicted from the sequence, improving overall structure prediction. We have also shown that the large number of available HIV-1 protease structures provides a remarkable sampling of conformations, which can be viewed as direct structural information about the dynamics. After structure matching, we apply principal component analysis (PCA) to obtain the important apparent motions for both bound and unbound structures. There are significant similarities between the first few key motions and the first few low-frequency normal modes calculated from a static representative structure with an elastic network model (ENM) that is based on the contact matrix C (related to D), strongly suggesting that the variations among the observed structures and the corresponding conformational changes are facilitated by the low-frequency, global motions intrinsic to the structure. Similarities are also found when the approach is applied to an NMR ensemble, as well as to atomic molecular dynamics (MD) trajectories. Thus, a sufficiently large number of experimental structures can directly provide important information about protein dynamics, but ENM can also provide a similar sampling of conformations. Finally, we use distance constraints from databases of known protein structures for structure refinement. We use the distributions of distances of various types in known protein structures to obtain the most probable ranges or the mean-force potentials for the distances. We then impose these constraints on structures to be refined or include the mean-force potentials directly in the energy minimization so that more plausible structural models can be built. This approach has been successfully used by us in 2006 in the CASPR structure refinement (http://predictioncenter.org/caspR).

Ideal amino acid exchange forms for approximating substitution matrices.

We have analyzed 29 published substitution matrices (SMs) and five statistical protein contact potentials (CPs) for comparison. We find that popular, 'classical' SMs obtained mainly from sequence alignments of globular proteins are mostly correlated by at least a value of 0.9. The BLOSUM62 is the central element of this group. A second group includes SMs derived from alignments of remote homologs or transmembrane proteins. These matrices correlate better with classical SMs (0.8) than among themselves (0.7). A third group consists of intermediate links between SMs and CPs - matrices and potentials that exhibit mutual correlations of at least 0.8. Next, we show that SMs can be approximated with a correlation of 0.9 by expressions c(0) + x(i)x(j) + y(i)y(j) + z(i)z(j), 1

Inferring ideal amino acid interaction forms from statistical protein contact potentials.

We have analyzed 29 different published matrices of protein pairwise contact potentials (CPs) between amino acids derived from different sets of proteins, either crystallographic structures taken from the Protein Data Bank (PDB) or computer-generated decoys. Each of the CPs is similar to 1 of the 2 matrices derived in the work of Miyazawa and Jernigan (Proteins 1999;34:49-68). The CP matrices of the first class can be approximated with a correlation of order 0.9 by the formula e(ij) = h(i) + h(j), 1

A minimal proteinlike lattice model: an alpha-helix motif.

A simple protein model of a four-helix bundle motif on a face-centered cubic lattice has been studied. Total energy of a conformation includes attractive interactions between hydrophobic residues, repulsive interactions between hydrophobic and polar residues, and a potential that favors helical turns. Using replica exchange Monte Carlo simulations we have estimated a set of parameters for which the native structure is a global minimum of conformational energy. Then we have shown that all the above types of interactions are necessary to guarantee the cooperativity of folding transition and to satisfy the thermodynamic hypothesis.

A minimal physically realistic protein-like lattice model: designing an energy landscape that ensures all-or-none folding to a unique native state.

A simple protein model restricted to the face-centered cubic lattice has been studied. The model interaction scheme includes attractive interactions between hydrophobic (H) residues, repulsive interactions between hydrophobic and polar (P) residues, and orientation-dependent P-P interactions. Additionally, there is a potential that favors extended beta-type conformations. A sequence has been designed that adopts a native structure, consisting of an antiparallel, six-member Greek-key beta-barrel with protein-like structural degeneracy. It has been shown that the proposed model is a minimal one, i.e., all the above listed types of interactions are necessary for cooperative (all-or-none) type folding to the native state. Simulations were performed via the Replica Exchange Monte Carlo method and the numerical data analyzed via a multihistogram method.

A simple lattice model that exhibits a protein-like cooperative all-or-none folding transition.

In a recent paper (D. Gront et al., Journal of Chemical Physics, Vol. 115, pp. 1569, 2001) we applied a simple combination of the Replica Exchange Monte Carlo and the Histogram methods in the computational studies of a simplified protein lattice model containing hydrophobic and polar units and sequence-dependent local stiffness. A well-defined, relatively complex Greek-key topology, ground (native) conformations was found; however, the cooperativity of the folding transition was very low. Here we describe a modified minimal model of the same Greek-key motif for which the folding transition is very cooperative and has all the features of the "all-or-none" transition typical of real globular proteins. It is demonstrated that the all-or-none transition arises from the interplay between local stiffness and properly defined tertiary interactions. The tertiary interactions are directional, mimicking the packing preferences seen in proteins. The model properties are compared with other minimal protein-like models, and we argue that the model presented here captures essential physics of protein folding (structurally well-defined protein-like native conformation and cooperative all-or-none folding transition).