Detection of influent virulence and resistance genes in microarray data through quasi likelihood modeling.

Publicly available genomic data are a great source of biological knowledge that can be extracted when appropriate data analysis is used. Predicting the biological function of genes is of interest to understand molecular mechanisms of virulence and resistance in pathogens and hosts and is important for drug discovery and disease control. This is commonly done by searching for similar gene expression behavior. Here, we used publicly available Streptococcus pyogenes microarray data obtained during primate infection to identify genes that have a potential influence on virulence and Phytophtora infestance inoculated tomato microarray data to identify genes potentially implicated in resistance processes. This approach goes beyond co-expression analysis. We employed a quasi-likelihood model separated by primate gender/inoculation condition to model median gene expression of known virulence/resistance factors. Based on this model, an influence analysis considering time course measurement was performed to detect genes with atypical expression. This procedure allowed for the detection of genes potentially implicated in the infection process. Finally, we discuss the biological meaning of these results, showing that influence analysis is an efficient and useful alternative for functional gene prediction.

[Association of the incidence of type 1 diabetes mellitus with environmental factors in Chile during the period 2000-2007]. / Estudio temporal de diabetes mellitus tipo 1 en Chile: asociación con factores ambientales durante el período 2000-2007.

BACKGROUND: Pollution and viral infections could be associated with the incidence of type 1 diabetes mellitus. AIM: To look for associations between the temporal patterns of Type 1 Diabetes Mellitus (T1D) in infants younger than the age of 15 years, and environmental factors, such as air pollution and viruses. MATERIAL AND METHODS: Data registries from hospitals, emergency services, and the Infantile Diabetes Foundation were reviewed, corresponding to children aged less than 15 years, who received their first insulin injection between 2000 and 2007. The incidence of type 1 diabetes was computed for each epidemiological week. Environmental ozone and particulate matter rates for each week were obtained from Environmental services. Rates of influenza and respiratory syncytial virus infections were obtained from the epidemiological department of the Ministry of Health. An ecological Bayesian Poisson regression model was fitted, introducing the covariates, lagged covariates and errors, to estimate the incidence by epidemiological week. RESULTS: Three factors were significant by the proposed model: particulate matter PPM 2.5 (relative risk (RR): 1.003) lagged by two weeks, influenza (RR: 0.1808) and RSV (RR: 1.021). Trends and seasonality were clearly controlled by these covariates, considering the epidemiological week as a counting period. CONCLUSIONS: These results show that environmental factors could be related to peaks of type 1 diabetes incidence.

Virulence factor prediction in Streptococcus pyogenes using classification and clustering based on microarray data.

Interesting biological information as, for example, gene expression data (microarrays), can be extracted from publicly available genomic data. As a starting point in order to narrow down the great possibilities of wet lab experiments, global high throughput data and available knowledge should be used to infer biological knowledge and emit biological hypothesis. Here, based on microarray data, we propose the use of cluster and classification methods that have become very popular and are implemented in freely available software in order to predict the participation in virulence mechanisms of different proteins coded by genes of the pathogen Streptococcus pyogenes. Confidence of predictions is based on classification errors of known genes and repetitive prediction by more than three methods. A special emphasis is done on the nonlinear kernel classification methods used. We propose a list of interesting candidates that could be virulence factors or that participate in the virulence process of S. pyogenes. Biological validations should start using this list of candidates as they show similar behavior to known virulence factors.

Classification and Clustering on Microarray Data for Gene Functional Prediction Using R.

Gene expression data (microarrays and RNA-sequencing data) as well as other kinds of genomic data can be extracted from publicly available genomic data. Here, we explain how to apply multivariate cluster and classification methods on gene expression data. These methods have become very popular and are implemented in freely available software in order to predict the participation of gene products in a specific functional category of interest. Taking into account the availability of data and of these methods, every biological study should apply them in order to obtain knowledge on the organism studied and functional category of interest. A special emphasis is made on the nonlinear kernel classification methods.

Lung cancer mortality trends in Chile and six-year projections using Bayesian dynamic linear models.

The objectives were to analyze lung cancer mortality trends in Chile from 1990 to 2009, and to project the rates six years forward. Lung cancer mortality data were obtained from the Chilean Ministry of Health. To obtain mortality rates, population projections were used, based on the 2002 National Census. Rates were adjusted using the world standard population as reference. Bayesian dynamic linear models were fitted to estimate trends from 1990 to 2009 and to obtain projections for 2010-2015. During the period under study, there was a 19.9% reduction in the lung cancer mortality rate in men. In women, there was increase of 28.4%. The second-order model showed a better fit for men, and the first-order model a better fit for women. Between 2010 and 2015 the downward trend continued in men, while a trend to stabilization was projected for lung cancer mortality in women in Chile. This analytical approach could be useful implement surveillance systems for chronic non-communicable disease and to evaluate preventive strategies.

Mortalidad por cáncer pulmonar en Chile: tendencia y proyección a seis años con modelos lineales dinámicos bayesianos / Mortalidade por câncer de pulmão no Chile: tendência e projeção de seis anos com modelos lineares dinâmicos bayesianos / Lung cancer mortality trends in Chile and six-year projections using Bayesian dynamic linear models

El objetivo fue analizar la tendencia de la tasa de mortalidad por cáncer de pulmón en Chile, durante el periodo 1990-2009 y proyectar estas tasas a seis años. La información de mortalidad fue obtenida del Ministerio de Salud de Chile. Para calcular las tasas se utilizaron las proyecciones de población según el Censo de 2002. Las tasas se estandarizaron usando la población mundial como referencia. Se ajustaron modelos lineales dinámicos bayesianos para estimar la tendencia entre 1990-2009 y proyectar el periodo 2010-2015. Durante el periodo se observa una reducción del 19,9% de la tasa de mortalidad en hombres, mientras que en mujeres, la tendencia es creciente con aumento de 28,4%. El modelo de segundo orden entregó un mejor ajuste en hombres y el de primer orden en mujeres. Entre 2010 y 2015, se mantiene la tendencia decreciente en hombres, en cambio se proyecta una estabilización en la tendencia de mortalidad por cáncer pulmonar en mujeres en Chile. Este tipo de análisis es útil para implementar sistemas de vigilancia epidemiológica y evaluar estrategias.

O objetivo foi analisar a tendência da taxa de mortalidade por câncer de pulmão no Chile, durante 1990-2009 e projetar estas taxas em seis anos. As informações sobre a mortalidade foram obtidas no Ministério da Saúde do Chile. Para calcular as taxas foram utilizadas projeções da população de acordo com o Censo de 2002. As taxas foram padronizadas utilizando-se a população mundial como referência. Modelos lineares dinâmicos bayesianos foram ajustados para estimar a tendência entre 1990-2009 e projetar o período 2010-2015. Durante o período, observa-se uma redução da taxa de mortalidade de 19,9% nos homens, entretanto nas mulheres a tendência é de crescimento, com o aumento de 28,4%. O modelo de segunda ordem deu um melhor ajuste para os homens e mulheres de primeira ordem. Entre 2010 e 2015, a tendência descendente é mantida entre os homens, no entanto, projetando uma tendência de estabilização da mortalidade por câncer de pulmão em mulheres no Chile. Este tipo de análise pode ser útil para implementar sistemas de vigilância epidemiológica e avaliar estratégias.

The objectives were to analyze lung cancer mortality trends in Chile from 1990 to 2009, and to project the rates six years forward. Lung cancer mortality data were obtained from the Chilean Ministry of Health. To obtain mortality rates, population projections were used, based on the 2002 National Census. Rates were adjusted using the world standard population as reference. Bayesian dynamic linear models were fitted to estimate trends from 1990 to 2009 and to obtain projections for 2010-2015. During the period under study, there was a 19.9% reduction in the lung cancer mortality rate in men. In women, there was increase of 28.4%. The second-order model showed a better fit for men, and the first-order model a better fit for women. Between 2010 and 2015 the downward trend continued in men, while a trend to stabilization was projected for lung cancer mortality in women in Chile. This analytical approach could be useful implement surveillance systems for chronic non-communicable disease and to evaluate preventive strategies.

Mortalidad por cáncer pulmonar en Chile: tendencia y proyección a seis años con modelos lineales dinámicos bayesianos / /Mortalidade por câncer de pulmão no Chile: tendência e projeção de seis anos com modelos lineares dinâmicos bayesianos / /Lung cancer mortality trends in Chile and six-year projections using Bayesian dynamic linear models

El objetivo fue analizar la tendencia de la tasa de mortalidad por cáncer de pulmón en Chile, durante el periodo 1990-2009 y proyectar estas tasas a seis años. La información de mortalidad fue obtenida del Ministerio de Salud de Chile. Para calcular las tasas se utilizaron las proyecciones de población según el Censo de 2002. Las tasas se estandarizaron usando la población mundial como referencia. Se ajustaron modelos lineales dinámicos bayesianos para estimar la tendencia entre 1990-2009 y proyectar el periodo 2010-2015. Durante el periodo se observa una reducción del 19,9% de la tasa de mortalidad en hombres, mientras que en mujeres, la tendencia es creciente con aumento de 28,4%. El modelo de segundo orden entregó un mejor ajuste en hombres y el de primer orden en mujeres. Entre 2010 y 2015, se mantiene la tendencia decreciente en hombres, en cambio se proyecta una estabilización en la tendencia de mortalidad por cáncer pulmonar en mujeres en Chile. Este tipo de análisis es útil para implementar sistemas de vigilancia epidemiológica y evaluar estrategias.(AU)

O objetivo foi analisar a tendência da taxa de mortalidade por câncer de pulmão no Chile, durante 1990-2009 e projetar estas taxas em seis anos. As informações sobre a mortalidade foram obtidas no Ministério da Saúde do Chile. Para calcular as taxas foram utilizadas projeções da população de acordo com o Censo de 2002. As taxas foram padronizadas utilizando-se a população mundial como referência. Modelos lineares dinâmicos bayesianos foram ajustados para estimar a tendência entre 1990-2009 e projetar o período 2010-2015. Durante o período, observa-se uma redução da taxa de mortalidade de 19,9% nos homens, entretanto nas mulheres a tendência é de crescimento, com o aumento de 28,4%. O modelo de segunda ordem deu um melhor ajuste para os homens e mulheres de primeira ordem. Entre 2010 e 2015, a tendência descendente é mantida entre os homens, no entanto, projetando uma tendência de estabilização da mortalidade por câncer de pulmão em mulheres no Chile. Este tipo de análise pode ser útil para implementar sistemas de vigilância epidemiológica e avaliar estratégias.(AU)

The objectives were to analyze lung cancer mortality trends in Chile from 1990 to 2009, and to project the rates six years forward. Lung cancer mortality data were obtained from the Chilean Ministry of Health. To obtain mortality rates, population projections were used, based on the 2002 National Census. Rates were adjusted using the world standard population as reference. Bayesian dynamic linear models were fitted to estimate trends from 1990 to 2009 and to obtain projections for 2010-2015. During the period under study, there was a 19.9% reduction in the lung cancer mortality rate in men. In women, there was increase of 28.4%. The second-order model showed a better fit for men, and the first-order model a better fit for women. Between 2010 and 2015 the downward trend continued in men, while a trend to stabilization was projected for lung cancer mortality in women in Chile. This analytical approach could be useful implement surveillance systems for chronic non-communicable disease and to evaluate preventive strategies.(AU)

Clustering of cases of type 1 diabetes in high socioeconomic communes in Santiago de Chile: spatio-temporal and geographical analysis.

The objective of this study was to describe spatial and space-time patterns of type 1 diabetes in children less than 15 years old, diagnosed between 2000 and 2005 with residence in the Metropolitan Region of Chile. Knox and Mantel tests were used to detect space-time interaction between cases. An ecological Bayesian model adjusted by socioeconomic factor and year was proposed to estimate the incidence by communes. Initially, there was no space-time interaction between cases, but there is evidence of clustering effect in urban areas of the region. The incidence rate for the overall study period was estimated by 6.18/100,000 (95% CI: 5.69-6.70), with a significant annual trend of 8.2% (P < 0.01). The geographical incidence could be explained by the human development index, as a socioeconomic factor. These results suggest that children living in communes with higher socioeconomic levels may be at higher risk of developing type 1 diabetes. Our findings support the hypothesis of an aetiological role of environmental factors in the onset of type 1 diabetes.

Data mining and influential analysis of gene expression data for plant resistance gene identification in tomato (Solanum lycopersicum)

Background Molecular mechanisms of plant-pathogen interactions have been studied thoroughly but much about them is still unknown. A better understanding of these mechanisms and the detection of new resistance genes can improve crop production and food supply. Extracting this knowledge from available genomic data is a challenging task. Results Here, we evaluate the usefulness of clustering, data-mining and regression to identify potential new resistance genes. Three types of analyses were conducted separately over two conditions, tomatoes inoculated with Phytophthora infestans and not inoculated tomatoes. Predictions for 10 new resistance genes obtained by all applied methods were selected as being the most reliable and are therefore reported as potential resistance genes. Conclusion Application of different statistical analyses to detect potential resistance genes reliably has shown to conduct interesting results that improve knowledge on molecular mechanisms of plant resistance to pathogens.

Estudio temporal de diabetes mellitus tipo 1 en Chile: asociación con factores ambientales durante el período 2000-2007 / Association of the incidence of type 1 diabetes mellitus with environmental factors in Chile during the period 2000-2007

Background: Pollution and viral infections could be associated with the incidence of type 1 diabetes mellitus. Aim: To look for associations between the temporal patterns of Type 1 Diabetes Mellitus (T1D) in infants younger than the age of 15years, and environmental factors, such as air pollution and viruses. Material and Methods: Data registries from hospitals, emergency services, and the Infantile Diabetes Foundation were reviewed, corresponding to children aged less than 15years, who received their first insulin injection between 2000 and 2007. The incidence of type 1 diabetes was computed for each epidemiological week. Environmental ozone and particulate matter rates for each week were obtained from Environmental services. Rates of influenza and respiratory syncytial virus infections were obtained from the epidemiological department of the Ministry of Health. An ecological Bayesian Poisson regression model was fitted, introducing the covariates, lagged covariates and errors, to estimate the incidence by epidemiological week. Results: Three factors were significant by the proposed model: particulate matter PPM 2.5 (relative risk (RR): 1.003) lagged by two weeks, influenza (RR: 0.1808) and RSV (RR: 1.021). Trends and seasonality were clearly controlled by these covariates, considering the epidemiological week as a counting period. Conclusions: These results show that environmental factors could be related to peaks of type 1 diabetes incidence.