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1.
BMC Plant Biol ; 17(1): 66, 2017 03 28.
Artigo em Inglês | MEDLINE | ID: mdl-28347287

RESUMO

BACKGROUND: The environment has a profound influence on the organoleptic quality of tomato (Solanum lycopersicum) fruit, the extent of which depends on a well-regulated and dynamic interplay among genes, metabolites and sensorial attributes. We used a systems biology approach to elucidate the complex interacting mechanisms regulating the plasticity of sensorial traits. To investigate environmentally challenged transcriptomic and metabolomic remodeling and evaluate the organoleptic consequences of such variations we grown three tomato varieties, Heinz 1706, whose genome was sequenced as reference and two "local" ones, San Marzano and Vesuviano in two different locations of Campania region (Italy). RESULTS: Responses to environment were more pronounced in the two "local" genotypes, rather than in the Heinz 1706. The overall genetic composition of each genotype, acting in trans, modulated the specific response to environment. Duplicated genes and transcription factors, establishing different number of network connections by gaining or losing links, play a dominant role in shaping organoleptic profile. The fundamental role of cell wall metabolism in tuning all the quality attributes, including the sensorial perception, was also highlighted. CONCLUSIONS: Although similar fruit-related quality processes are activated in the same environment, different tomato genotypes follow distinct transcriptomic, metabolomic and sensorial trajectories depending on their own genetic makeup.


Assuntos
Frutas/genética , Frutas/metabolismo , Lycopersicon esculentum/genética , Lycopersicon esculentum/metabolismo , Parede Celular/genética , Parede Celular/metabolismo , Qualidade dos Alimentos , Frutas/fisiologia , Dosagem de Genes , Regulação da Expressão Gênica de Plantas , Genoma de Planta , Genótipo , Itália , Metaboloma , Biologia de Sistemas/métodos , Fatores de Transcrição/genética , Fatores de Transcrição/metabolismo , Transcriptoma
2.
Psychometrika ; 82(2): 442-458, 2017 06.
Artigo em Inglês | MEDLINE | ID: mdl-27734294

RESUMO

The elicitation of an ordinal judgment on multiple alternatives is often required in many psychological and behavioral experiments to investigate preference/choice orientation of a specific population. The Plackett-Luce model is one of the most popular and frequently applied parametric distributions to analyze rankings of a finite set of items. The present work introduces a Bayesian finite mixture of Plackett-Luce models to account for unobserved sample heterogeneity of partially ranked data. We describe an efficient way to incorporate the latent group structure in the data augmentation approach and the derivation of existing maximum likelihood procedures as special instances of the proposed Bayesian method. Inference can be conducted with the combination of the Expectation-Maximization algorithm for maximum a posteriori estimation and the Gibbs sampling iterative procedure. We additionally investigate several Bayesian criteria for selecting the optimal mixture configuration and describe diagnostic tools for assessing the fitness of ranking distributions conditionally and unconditionally on the number of ranked items. The utility of the novel Bayesian parametric Plackett-Luce mixture for characterizing sample heterogeneity is illustrated with several applications to simulated and real preference ranked data. We compare our method with the frequentist approach and a Bayesian nonparametric mixture model both assuming the Plackett-Luce model as a mixture component. Our analysis on real datasets reveals the importance of an accurate diagnostic check for an appropriate in-depth understanding of the heterogenous nature of the partial ranking data.


Assuntos
Algoritmos , Teorema de Bayes , Psicometria , Humanos , Probabilidade
3.
BMC Plant Biol ; 16: 53, 2016 Feb 27.
Artigo em Inglês | MEDLINE | ID: mdl-26920134

RESUMO

BACKGROUND: Fusarium oxysporum f.sp. radicis-lycopersici (FORL) is one of the most destructive necrotrophic pathogens affecting tomato crops, causing considerable field and greenhouse yield losses. Despite such major economic impact, little is known about the molecular mechanisms regulating Fusarium oxysporum f.sp. radicis-lycopersici resistance in tomato. RESULTS: A transcriptomic experiment was carried out in order to investigate the main mechanisms of FORL response in resistant and susceptible isogenic tomato lines. Microarray analysis at 15 DPI (days post inoculum) revealed a distinct gene expression pattern between the two genotypes in the inoculated vs non-inoculated conditions. A model of plant response both for compatible and incompatible reactions was proposed. In particular, in the incompatible interaction an activation of defense genes related to secondary metabolite production and tryptophan metabolism was observed. Moreover, maintenance of the cell osmotic potential after the FORL challenging was mediated by a dehydration-induced protein. As for the compatible interaction, activation of an oxidative burst mediated by peroxidases and a cytochrome monooxygenase induced cell degeneration and necrosis. CONCLUSIONS: Our work allowed comprehensive understanding of the molecular basis of the tomato-FORL interaction. The result obtained emphasizes a different transcriptional reaction between the resistant and the susceptible genotype to the FORL challenge. Our findings could lead to the improvement in disease control strategies.


Assuntos
Fusarium/fisiologia , Lycopersicon esculentum/genética , Lycopersicon esculentum/microbiologia , Doenças das Plantas/genética , Perfilação da Expressão Gênica , Genoma de Planta , Doenças das Plantas/imunologia , Doenças das Plantas/microbiologia , Transcriptoma
4.
Biometrics ; 72(1): 125-35, 2016 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-26444297

RESUMO

We develop alternative strategies for building and fitting parametric capture-recapture models for closed populations which can be used to address a better understanding of behavioral patterns. In the perspective of transition models, we first rely on a conditional probability parameterization. A large subset of standard capture-recapture models can be regarded as a suitable partitioning in equivalence classes of the full set of conditional probability parameters. We exploit a regression approach combined with the use of new suitable summaries of the conditioning binary partial capture histories as a device for enlarging the scope of behavioral models and also exploring the range of all possible partitions. We show how one can easily find unconditional MLE of such models within a generalized linear model framework. We illustrate the potential of our approach with the analysis of some known datasets and a simulation study.


Assuntos
Algoritmos , Comportamento Animal/fisiologia , Censos , Interpretação Estatística de Dados , Modificador do Efeito Epidemiológico , Modelos Estatísticos , Animais , Biometria/métodos , Simulação por Computador , Humanos , Densidade Demográfica , Reprodutibilidade dos Testes , Tamanho da Amostra , Sensibilidade e Especificidade
5.
Stat Med ; 33(21): 3738-58, 2014 Sep 20.
Artigo em Inglês | MEDLINE | ID: mdl-24903256

RESUMO

We propose the use of probability models for ranked data as a useful alternative to a quantitative data analysis to investigate the outcome of bioassay experiments when the preliminary choice of an appropriate normalization method for the raw numerical responses is difficult or subject to criticism. We review standard distance-based and multistage ranking models and propose an original generalization of the Plackett-Luce model to account for the order of the ranking elicitation process. The usefulness of the novel model is illustrated with its maximum likelihood estimation for a real data set. Specifically, we address the heterogeneous nature of the experimental units via model-based clustering and detail the necessary steps for a successful likelihood maximization through a hybrid version of the expectation-maximization algorithm. The performance of the mixture model using the new distribution as mixture components is then compared with alternative mixture models for random rankings. A discussion on the interpretation of the identified clusters and a comparison with more standard quantitative approaches are finally provided.


Assuntos
Algoritmos , Análise por Conglomerados , Interpretação Estatística de Dados , Funções Verossimilhança , Modelos Estatísticos , Simulação por Computador , Humanos
6.
PLoS One ; 9(5): e94963, 2014.
Artigo em Inglês | MEDLINE | ID: mdl-24804963

RESUMO

Since gene expression approaches constitute a starting point for investigating plant-pathogen systems, we performed a transcriptional analysis to identify a set of genes of interest in tomato plants infected with F. oxysporum f. sp. lycopersici (Fol) and Tomato Mosaic Virus (ToMV). Differentially expressed tomato genes upon inoculation with Fol and ToMV were identified at two days post-inoculation. A large overlap was found in differentially expressed genes throughout the two incompatible interactions. However, Gene Ontology enrichment analysis evidenced specific categories in both interactions. Response to ToMV seems more multifaceted, since more than 70 specific categories were enriched versus the 30 detected in Fol interaction. In particular, the virus stimulated the production of an invertase enzyme that is able to redirect the flux of carbohydrates, whereas Fol induced a homeostatic response to prevent the fungus from killing cells. Genomic mapping of transcripts suggested that specific genomic regions are involved in resistance response to pathogen. Coordinated machinery could play an important role in prompting the response, since 60% of pathogen receptor genes (NB-ARC-LRR, RLP, RLK) were differentially regulated during both interactions. Assessment of genomic gene expression patterns could help in building up models of mediated resistance responses.


Assuntos
Fusarium/patogenicidade , Lycopersicon esculentum/microbiologia , Doenças das Plantas/microbiologia , Tobamovirus/patogenicidade , Regulação da Expressão Gênica de Plantas , Proteínas de Plantas/genética , Proteínas de Plantas/metabolismo
7.
PLoS One ; 8(3): e58358, 2013.
Artigo em Inglês | MEDLINE | ID: mdl-23555577

RESUMO

We developed a new phage-display based approach, the Large Fragment Phage Display (LFPD), that can be used for mapping conformational epitopes on target molecules of immunological interest. LFPD uses a simplified and more effective phage-display approach in which only a limited set of larger fragments (about 100 aa in length) are expressed on the phage surface. Using the human HER2 oncoprotein as a target, we identified novel B-cell conformational epitopes. The same homologous epitopes were also detected in rat HER2 and all corresponded to the epitopes predicted by computational analysis (PEPITO software), showing that LFPD gives reproducible and accurate results. Interestingly, these newly identified HER2 epitopes seem to be crucial for an effective immune response against HER2-overexpressing breast cancers and might help discriminating between metastatic breast cancer and early breast cancer patients. Overall, the results obtained in this study demonstrated the utility of LFPD and its potential application to the detection of conformational epitopes on many other molecules of interest, as well as, the development of new and potentially more effective B-cell conformational epitopes based vaccines.


Assuntos
Epitopos de Linfócito B , Biblioteca de Peptídeos , Receptor ErbB-2 , Animais , Células 3T3 BALB , Neoplasias da Mama/diagnóstico , Neoplasias da Mama/genética , Neoplasias da Mama/imunologia , Neoplasias da Mama/patologia , Epitopos de Linfócito B/química , Epitopos de Linfócito B/genética , Epitopos de Linfócito B/imunologia , Feminino , Humanos , Camundongos , Metástase Neoplásica , Estrutura Terciária de Proteína , Ratos , Receptor ErbB-2/química , Receptor ErbB-2/genética , Receptor ErbB-2/imunologia
8.
J Comput Biol ; 19(4): 418-38, 2012 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-22414153

RESUMO

Bayesian phylogenetic methods are generating noticeable enthusiasm in the field of molecular systematics. Many phylogenetic models are often at stake, and different approaches are used to compare them within a Bayesian framework. The Bayes factor, defined as the ratio of the marginal likelihoods of two competing models, plays a key role in Bayesian model selection. We focus on an alternative estimator of the marginal likelihood whose computation is still a challenging problem. Several computational solutions have been proposed, none of which can be considered outperforming the others simultaneously in terms of simplicity of implementation, computational burden and precision of the estimates. Practitioners and researchers, often led by available software, have privileged so far the simplicity of the harmonic mean (HM) estimator. However, it is known that the resulting estimates of the Bayesian evidence in favor of one model are biased and often inaccurate, up to having an infinite variance so that the reliability of the corresponding conclusions is doubtful. We consider possible improvements of the generalized harmonic mean (GHM) idea that recycle Markov Chain Monte Carlo (MCMC) simulations from the posterior, share the computational simplicity of the original HM estimator, but, unlike it, overcome the infinite variance issue. We show reliability and comparative performance of the improved harmonic mean estimators comparing them to approximation techniques relying on improved variants of the thermodynamic integration.


Assuntos
Modelos Genéticos , Filogenia , Teorema de Bayes , Simulação por Computador , Cadeias de Markov , Plantas/genética , Termodinâmica
9.
Biostatistics ; 13(1): 101-12, 2012 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-21856651

RESUMO

Peptide Microarray Immunoassay (PMI for brevity) is a novel technology that enables researchers to map a large number of proteomic measurements at a peptide level, providing information regarding the relationship between antibody response and clinical sensitivity. PMI studies aim at recognizing antigen-specific antibodies from serum samples and at detecting epitope regions of the protein antigen. PMI data present new challenges for statistical analysis mainly due to the structural dependence among peptides. A PMI is made of a complete library of consecutive peptides. They are synthesized by systematically shifting a window of a fixed number of amino acids through the finite sequence of amino acids of the antigen protein as ordered in the primary structure of the protein. This implies that consecutive peptides have a certain number of amino acids in common and hence are structurally dependent. We propose a new flexible Bayesian hierarchical model framework, which allows one to detect recognized peptides and bound epitope regions in a single framework, taking into account the structural dependence between peptides through a suitable latent Markov structure. The proposed model is illustrated using PMI data from a recent study about egg allergy. A simulation study shows that the proposed model is more powerful and robust in terms of epitope detection than simpler models overlooking some of the dependence structure.


Assuntos
Epitopos , Modelos Estatísticos , Análise Serial de Proteínas/estatística & dados numéricos , Teorema de Bayes , Bioestatística , Dessensibilização Imunológica , Hipersensibilidade a Ovo/imunologia , Hipersensibilidade a Ovo/terapia , Proteínas Dietéticas do Ovo/imunologia , Epitopos/genética , Humanos , Cadeias de Markov , Ovalbumina/imunologia , Peptídeos/genética , Peptídeos/imunologia , Proteômica/estatística & dados numéricos , Razão Sinal-Ruído
10.
Stat Appl Genet Mol Biol ; 10: Article 7, 2011.
Artigo em Inglês | MEDLINE | ID: mdl-21291417

RESUMO

We propose a robust model for discovering differentially expressed genes which directly incorporates biological significance, i.e., effect dimension. Using the so-called c-fold rule, we transform the expressions into a nominal observed random variable with three categories: below a fixed lower threshold, above a fixed upper threshold or within the two thresholds. Gene expression data is then transformed into a nominal variable with three levels possibly originated by three different distributions corresponding to under expressed, not differential, and over expressed genes. This leads to a statistical model for a 3-component mixture of trinomial distributions with suitable constraints on the parameter space. In order to obtain the MLE estimates, we show how to implement a constrained EM algorithm with a latent label for the corresponding component of each gene. Different strategies for a statistically significant gene discovery are discussed and compared. We illustrate the method on a little simulation study and a real dataset on multiple sclerosis.


Assuntos
Perfilação da Expressão Gênica/estatística & dados numéricos , Estudos de Associação Genética/estatística & dados numéricos , Modelos Estatísticos , Análise de Sequência com Séries de Oligonucleotídeos/estatística & dados numéricos , Algoritmos , Simulação por Computador , Interpretação Estatística de Dados , Mineração de Dados/estatística & dados numéricos , Probabilidade
11.
J Exp Bot ; 60(12): 3379-86, 2009.
Artigo em Inglês | MEDLINE | ID: mdl-19516072

RESUMO

The long-term objective of tomato breeders is to identify metabolites that contribute to defining the target flavour and to design strategies to enhance it. This paper reports the results of network analysis, based on metabolic phenotypic and sensory data, to highlight important relationships among such traits. This tool allowed a reduction in data set complexity, building a network consisting of 35 nodes and 74 links corresponding to the 74 significant (positive or negative) correlations among the variables studied. A number of links among traits contributing to fruit organoleptic quality and to the perception of sensory attributes were identified. Modular partitioning of the characteristics involved in fruit organoleptic perception captured the essential fruit parameters that regulate interactions among different class traits. The main feature of the network was the presence of three nodes interconnected among themselves (dry matter, pH, and degrees Brix) and with other traits, and nodes with widely different linkage degrees. Identification of strong associations between some metabolic and sensory traits, such as citric acid with tomato smell, glycine with tomato smell, and granulosity with dry matter, suggests a basis for more targeted investigations in the future.


Assuntos
Redes Reguladoras de Genes , Lycopersicon esculentum/genética , Característica Quantitativa Herdável , Aminoácidos/análise , Frutas/química , Frutas/genética , Frutas/metabolismo , Lycopersicon esculentum/química , Lycopersicon esculentum/metabolismo , Compostos Orgânicos/análise
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