Inferring stabilizing mutations from protein phylogenies : application to influenza hemagglutinin

One selection pressure shaping sequence evolution is the requirement that a protein fold with sufficient stability to perform its biological functions. We present a conceptual framework that explains how this requirement causes the probability that a particular amino acid mutation is fixed during evolution to depend on its effect on protein stability. We mathematically formalize this framework to develop a Bayesian approach for inferring the stability effects of individual mutations from homologous protein sequences of known phylogeny. This approach is able to predict published experimentally measured mutational stability effects (ΔΔG values) with an accuracy that exceeds both a state-of-the-art physicochemical modeling program and the sequence-based consensus approach. As a further test, we use our phylogenetic inference approach to predict stabilizing mutations to influenza hemagglutinin. We introduce these mutations into a temperature-sensitive influenza virus with a defect in its hemagglutinin gene and experimentally demonstrate that some of the mutations allow the virus to grow at higher temperatures. Our work therefore describes a powerful new approach for predicting stabilizing mutations that can be successfully applied even to large, complex proteins such as hemagglutinin. This approach also makes a mathematical link between phylogenetics and experimentally measurable protein properties, potentially paving the way for more accurate analyses of molecular evolution

Network-based models for social recommender systems

With the overwhelming online products available in recent years, there is an increasing need to filter and deliver relevant personalized advice for users. Recommender systems solve this problem by modeling and predicting individual preferences for a great variety of items such as movies, books or research articles. In this chapter, we explore rigorous network-based models that outperform leading approaches for recommendation. The network models we consider are based on the explicit assumption that there are groups of individuals and of items, and that the preferences of an individual for an item are determined only by their group memberships. The accurate prediction of individual user preferences over items can be accomplished by different methodologies, such as Monte Carlo sampling or Expectation-Maximization methods, the latter resulting in a scalable algorithm which is suitable for large datasets

Chemical Cues Influence Pupation Behavior of Drosophila simulans and Drosophila buzzatii in Nature and in the Laboratory

In the wild, larvae of several species of Drosophila develop in heterogeneous and rapidly changing environments sharing resources as food and space. In this scenario, sensory systems contribute to detect, localize and recognize congeners and heterospecifics, and provide information about the availability of food and chemical features of environments where animals live. We investigated the behavior of D. simulans and D. buzzatii larvae to chemicals emitted by conspecific and heterospecific larvae. Our goal was to understand the role of these substances in the selection of pupation sites in the two species that cohabit within decaying prickly pear fruits (Opuntia ficus-indica). In these breeding sites, larvae of D. simulans and D. buzzatii detect larvae of the other species changing their pupation site preferences. Larvae of the two species pupated in the part of the fruit containing no or few heterospecifics, and spent a longer time in/on spots marked by conspecifics rather than heterospecifics. In contrast, larvae of the two species reared in isolation from conspecifics pupated randomly over the substrate and spent a similar amount of time on spots marked by conspecifics and by heterospecifics. Our results indicate that early chemically-based experience with conspecific larvae is critical for the selection of the pupation sites in D. simulans and D. buzzatii, and that pupation site preferences of Drosophila larvae depend on species-specific chemical cues. These preferences can be modulate by the presence of larvae of the same or another species

Use of antibiotic spacers for knee endoprosthesis infections treatment

OBJCTIVE: The aim of this study is to evaluate the use of cement spacers impregnated with antibiotics for the treatment of infections in the nonconventional endoprostheses of the knee. METHODOLOGY: We have treated seven patients since 2004 (of which six were submitted to surgery in our service and one patient had been submitted to a primary tumor surgery in another removal service) with deep infection in knee tumor prosthesis. All patients were submitted to endoprosthesis removal and reconstructed with antibiotic cement spacer. All patients were monitored both clinically and by lab tests as for monitoring the evolution, being considered able for reviews after 6 (six) months without infections signs. RESULTS: We have noted a small predominance of infectious processes on the prosthesis inserted on proximal tibia as compared with distal femur (57.1% x 42.9%). The mean follow-up time of patients was 68.2 months. During the follow up, one patient died as a result of the root disease. Six patients out of seven were regarded as cured and one persisted with infection signs and symptoms. CONCLUSION: The results obtained up to date have motivated us to continue using this method of treatment.OBJETIVO: O objetivo do estudo é avaliar a utilização dos espaçadores de cimento acrílico com antibiótico no tratamento das infecções em endopróteses não convencionais de joelho. MÉTODO: Desde de 2004 foram tratados sete pacientes (seis pacientes operados no nosso serviço e um paciente que havia sido submetido a cirurgia primária do tumor em outro serviço) com infecção peri-endoprótese não convencional de joelho. Todos pacientes foram submetidos a retirada da endoprótese e reconstrução com espaçador com cimento acrílico com antibiótico. Todos os pacientes foram monitorados clínica e laboratorialmente quanto ao controle da evolução, sendo considerados aptos para a revisão e recolocação de endoprótese após 06 (seis) meses sem sinais infecciosos RESULTADOS: Notamos um discreto predomínio do do processo infeccioso nas próteses realizadas na tíbia proximal em comparação com o fêmur distal (57,1% x 42,9%). O seguimento médio dos pacientes foi 68,2 meses. Durante o seguimento, um paciente faleceu devido a doença de base. Dos sete pacientes, 6 foram considerados curados e um persistiu com sinais e sintomas de infecção. CONCLUSÃO: Os resultados obtidos até o momento tem motivado a continuidade deste método de tratamento.Universidade Federal de São Paulo (UNIFESP) Escola Paulista de Medicina Departamento de Ortopedia e TraumatologiaUNIFESP, EPM, Depto. de Ortopedia e TraumatologiaSciEL

AYUMS: an algorithm for completely automatic quantitation based on LC-MS/MS proteome data and its application to the analysis of signal transduction

BACKGROUND: Comprehensive description of the behavior of cellular components in a quantitative manner is essential for systematic understanding of biological events. Recent LC-MS/MS (tandem mass spectrometry coupled with liquid chromatography) technology, in combination with the SILAC (Stable Isotope Labeling by Amino acids in Cell culture) method, has enabled us to make relative quantitation at the proteome level. The recent report by Blagoev et al. (Nat. Biotechnol., 22, 1139–1145, 2004) indicated that this method was also applicable for the time-course analysis of cellular signaling events. Relative quatitation can easily be performed by calculating the ratio of peak intensities corresponding to differentially labeled peptides in the MS spectrum. As currently available software requires some GUI applications and is time-consuming, it is not suitable for processing large-scale proteome data. RESULTS: To resolve this difficulty, we developed an algorithm that automatically detects the peaks in each spectrum. Using this algorithm, we developed a software tool named AYUMS that automatically identifies the peaks corresponding to differentially labeled peptides, compares these peaks, calculates each of the peak ratios in mixed samples, and integrates them into one data sheet. This software has enabled us to dramatically save time for generation of the final report. CONCLUSION: AYUMS is a useful software tool for comprehensive quantitation of the proteome data generated by LC-MS/MS analysis. This software was developed using Java and runs on Linux, Windows, and Mac OS X. Please contact [email protected] if you are interested in the application. The project web page is

Primary Xenografts of Human Prostate Tissue as a Model to Study Angiogenesis Induced by Reactive Stroma

Characterization of the mechanism(s) of androgen-driven human angiogenesis could have significant implications for modeling new forms of anti-angiogenic therapies for CaP and for developing targeted adjuvant therapies to improve efficacy of androgen-deprivation therapy. However, models of angiogenesis by human endothelial cells localized within an intact human prostate tissue architecture are until now extremely limited. This report characterizes the burst of angiogenesis by endogenous human blood vessels in primary xenografts of fresh surgical specimens of benign prostate or prostate cancer (CaP) tissue that occurs between Days 6–14 after transplantation into SCID mice pre-implanted with testosterone pellets. The wave of human angiogenesis was preceded by androgen-mediated up-regulation of VEGF-A expression in the stromal compartment. The neo-vessel network anastomosed to the host mouse vascular system between Days 6–10 post-transplantation, the angiogenic response ceased by Day 15, and by Day 30 the vasculature had matured and stabilized, as indicated by a lack of leakage of serum components into the interstitial tissue space and by association of nascent endothelial cells with mural cells/pericytes. The angiogenic wave was concurrent with the appearance of a reactive stroma phenotype, as determined by staining for α-SMA, Vimentin, Tenascin, Calponin, Desmin and Masson's trichrome, but the reactive stroma phenotype appeared to be largely independent of androgen availability. Transplantation-induced angiogenesis by endogenous human endothelial cells present in primary xenografts of benign and malignant human prostate tissue was preceded by induction of androgen-driven expression of VEGF by the prostate stroma, and was concurrent with and the appearance of a reactive stroma phenotype. Androgen-modulated expression of VEGF-A appeared to be a causal regulator of angiogenesis, and possibly of stromal activation, in human prostate xenografts

IFN-γ signaling, with the synergistic contribution of TNF-α, mediates cell specific microglial and astroglial activation in experimental models of Parkinson's disease

To through light on the mechanisms underlying the stimulation and persistence of glial cell activation in Parkinsonism, we investigate the function of IFN-γ and TNF-α in experimental models of Parkinson's disease and analyze their relation with local glial cell activation. It was found that IFN-γ and TNF-α remained higher over the years in the serum and CNS of chronic Parkinsonian macaques than in untreated animals, accompanied by sustained glial activation (microglia and astroglia) in the substantia nigra pars compacta. Importantly, Parkinsonian monkeys showed persistent and increasing levels of IFN-γR signaling in both microglial and astroglial cells. In addition, experiments performed in IFN-γ and TNF-α KO mice treated with MPTP revealed that, even before dopaminergic cell death can be observed, the presence of IFN-γ and TNF-α is crucial for microglial and astroglial activation, and, together, they have an important synergistic role. Both cytokines were necessary for the full level of activation to be attained in both microglial and astroglial cells. These results demonstrate that IFN-γ signaling, together with the contribution of TNF-α, have a critical and cell-specific role in stimulating and maintaining glial cell activation in Parkinsonism

An Integrative -omics Approach to Identify Functional Sub-Networks in Human Colorectal Cancer

Emerging evidence indicates that gene products implicated in human cancers often cluster together in “hot spots” in protein-protein interaction (PPI) networks. Additionally, small sub-networks within PPI networks that demonstrate synergistic differential expression with respect to tumorigenic phenotypes were recently shown to be more accurate classifiers of disease progression when compared to single targets identified by traditional approaches. However, many of these studies rely exclusively on mRNA expression data, a useful but limited measure of cellular activity. Proteomic profiling experiments provide information at the post-translational level, yet they generally screen only a limited fraction of the proteome. Here, we demonstrate that integration of these complementary data sources with a “proteomics-first” approach can enhance the discovery of candidate sub-networks in cancer that are well-suited for mechanistic validation in disease. We propose that small changes in the mRNA expression of multiple genes in the neighborhood of a protein-hub can be synergistically associated with significant changes in the activity of that protein and its network neighbors. Further, we hypothesize that proteomic targets with significant fold change between phenotype and control may be used to “seed” a search for small PPI sub-networks that are functionally associated with these targets. To test this hypothesis, we select proteomic targets having significant expression changes in human colorectal cancer (CRC) from two independent 2-D gel-based screens. Then, we use random walk based models of network crosstalk and develop novel reference models to identify sub-networks that are statistically significant in terms of their functional association with these proteomic targets. Subsequently, using an information-theoretic measure, we evaluate synergistic changes in the activity of identified sub-networks based on genome-wide screens of mRNA expression in CRC. Cross-classification experiments to predict disease class show excellent performance using only a few sub-networks, underwriting the strength of the proposed approach in discovering relevant and reproducible sub-networks