Systematic determination of transcription factor DNA-binding specificities in yeast

International audienceUnderstanding how genes are regulated, decoding their "regulome", is one of the main challenges of the post-genomic era. Here, we describe the in vitro method we used to associate cis-regulatory sites with cognate trans-regulators by characterizing the DNA-binding specificity of the vast majority of yeast transcription factors using Protein Binding Microarrays. This approach can be implemented to any given organism

Prioritizing bona fide bacterial small RNAs with machine learning classifiers

Bacterial small (sRNAs) are involved in the control of several cellular processes. Hundreds of putative sRNAs have been identified in many bacterial species through RNA sequencing. The existence of putative sRNAs is usually validated by Northern blot analysis. However, the large amount of novel putative sRNAs reported in the literature makes it impractical to validate each of them in the wet lab. In this work, we applied five machine learning approaches to construct twenty models to discriminate bona fide sRNAs from random genomic sequences in five bacterial species. Sequences were represented using seven features including free energy of their predicted secondary structure, their distances to the closest predicted promoter site and Rho-independent terminator, and their distance to the closest open reading frames (ORFs). To automatically calculate these features, we developed an sRNA Characterization Pipeline (sRNACharP). All seven features used in the classification task contributed positively to the performance of the predictive models. The best performing model obtained a median precision of 100% at 10% recall and of 64% at 40% recall across all five bacterial species, and it outperformed previous published approaches on two benchmark datasets in terms of precision and recall. Our results indicate that even though there is limited sRNA sequence conservation across different bacterial species, there are intrinsic features in the genomic context of sRNAs that are conserved across taxa. We show that these features are utilized by machine learning approaches to learn a species-independent model to prioritize bona fide bacterial sRNAs

Detecting ulcerative colitis from colon samples using efficient feature selection and machine learning

Ulcerative colitis (UC) is one of the most common forms of inflammatory bowel disease (IBD) characterized by inflammation of the mucosal layer of the colon. Diagnosis of UC is based on clinical symptoms, and then confirmed based on endoscopic, histologic and laboratory findings. Feature selection and machine learning have been previously used for creating models to facilitate the diagnosis of certain diseases. In this work, we used a recently developed feature selection algorithm (DRPT) combined with a support vector machine (SVM) classifier to generate a model to discriminate between healthy subjects and subjects with UC based on the expression values of 32 genes in colon samples. We validated our model with an independent gene expression dataset of colonic samples from subjects in active and inactive periods of UC. Our model perfectly detected all active cases and had an average precision of 0.62 in the inactive cases. Compared with results reported in previous studies and a model generated by a recently published software for biomarker discovery using machine learning (BioDiscML), our final model for detecting UC shows better performance in terms of average precision

Simultaneous host and parasite expression profiling identifies tissue-specific transcriptional programs associated with susceptibility or resistance to experimental cerebral malaria

BACKGROUND: The development and outcome of cerebral malaria (CM) reflects a complex interplay between parasite-expressed virulence factors and host response to infection. The murine CM model, Plasmodium berghei ANKA (PbA), which simulates many of the features of human CM, provides an excellent system to study this host/parasite interface. We designed "combination" microarrays that concurrently detect genome-wide transcripts of both PbA and mouse, and examined parasite and host transcriptional programs during infection of CM-susceptible (C57BL/6) and CM-resistant (BALB/c) mice. RESULTS: Analysis of expression data from brain, lung, liver, and spleen of PbA infected mice showed that both host and parasite gene expression can be examined using a single microarray, and parasite transcripts can be detected within whole organs at a time when peripheral blood parasitemia is low. Parasites display a unique transcriptional signature in each tissue, and lung appears to be a large reservoir for metabolically active parasites. In comparisons of susceptible versus resistant animals, both host and parasite display distinct, organ-specific transcriptional profiles. Differentially expressed mouse genes were related to humoral immune response, complement activation, or cell-cell interactions. PbA displayed differential expression of genes related to biosynthetic activities. CONCLUSION: These data show that host and parasite gene expression profiles can be simultaneously analysed using a single "combination" microarray, and that both the mouse and malaria parasite display distinct tissue- and strain-specific responses during infection. This technology facilitates the dissection of host-pathogen interactions in experimental cerebral malaria and could be extended to other disease models

Application of edible nanolaminate coatings with antimicrobial extract of Flourensia cernua to extend the shelf-life of tomato (Solanum lycopersicum L.) fruit

Supplementarymaterialrelatedtothisarticlecanbefound,inthe online version, at doi:https://doi.org/10.1016/j.postharvbio.2018.12. 008.Edible coatings have potential to reduce postharvest losses of fruit such as tomato. In this study, the effects of nanolaminate coatings incorporated with extracts of Flourensia cernua, an endemic plant of the arid and semi-arid regions of Mexico, has been investigated. Ethanol extracts of F. cernua (FcE) were prepared and incorporated into polyelectrolyte solutions of alginate and chitosan. The nanolaminates were characterized by determining the zeta potential, contact angle and water vapor and oxygen permeabilities. Shelf-life analyses (20°C for 15 d) were carried out with uncoated fruit (UCF), nanolaminate coating (NL) and nanolaminate coating with FcE (NL+FcE). Physicochemical analyses, gas exchange rates of O2 and CO2 and ethylene production, as well as microbiological analyses of treated fruit were measured. Zeta potential and contact angle measurements confirmed the successful assembly of successive nanolayers of alginate and chitosan, as well as those with F. cernua. The nanolaminate coatings resulted in decreased permeabilities to water and O2. The best treatment of NL+FcE, extended the shelf-life of fruit by reducing weight loss and microbial growth, reducing gas exchange and ethylene production, and maintaining firmness and color. The NL+FcE treatment are an alternative to extend the shelf-life of tomato fruit.Author E. de J. Salas-Méndez thanks Mexican Science and Technology Council (CONACYT, Mexico) for PhD fellowship support. Authors want to thank PhD Zlatina Genisheva for the proof reading of the manuscript and suggestions to the same; also, to:MaríaGuadalupe Moreno Esquivel, Edith E. Chaires Colunga, Olga L. Solís Hernández and M. Leticia Rodríguez González of the Phytochemistry Laboratory from Universidad Autónoma Agraria Antonio Narro, for their assistance in obtaining extracts and chemical composition.info:eu-repo/semantics/publishedVersio

Utilidad del score SOFA en la predicción de muerte materna en la UCI materna del INMP

Objective:  To compare the SOFA score at admission and 24 hours and evaluate its usefulness in predicting maternal death. Materials and methods: A cross-sectional descriptive study of patients admitted to the Maternal ICU of the INMP was carried out from August 2014 to July 2019. Parametric and non-parametric tests were applied according to a previous evaluation of normality and a statistical significance p<0.05. Statistical analysis was performed using the statistical package STATA version 13. Results: The mean age corresponds to 28.32 years. The mean gestational age corresponded to 31.33 weeks; The mean hospital stay was 4.52 days. The area under the curve for SOFA at entry was 0.8818 and the area under the curve for SOFA at 24 hours was 0.9755.     P=0.1225. No significant differences were found between them. It was determined that the best cut-off point corresponds to a Score of 6 with a sensitivity that corresponds to 89.29% and a specificity of 79.13%. Conclusions: The SOFA Score adapts well to obstetric patients admitted to Maternal Intensive Care settings, maintaining a cut-off of 6 for admission SOFA with good sensitivity and specificity. (89.29%, 79.13%). The SOFA score is useful in maternal ICU environments and it should be used in our country.Objetivo: Comparar el score SOFA al ingreso y a las 24horas, y evaluar su utilidad en la predicción de la muerte materna. Materiales y métodos: Se realizó un estudio descriptivo de tipo transversal de las pacientes admitidas a la UCI Materna del INMP desde agosto del 2014 a Julio del 2019. Se aplicaron pruebas paramétricas o no paramétricas según evaluación previa de normalidad y se usó una significación estadística p<0.05. El análisis estadístico se realizó utilizando el paquete estadístico STATA versión 13. Resultados: La media de la edad corresponde a 28.32 años. La media de la edad gestacional correspondió a 31.33 semanas; La media de la estancia hospitalaria fue de 4.52 días. El area bajo la curva para el SOFA al ingreso fue de 0.8818 y el area bajo la curva del SOFA a las 24horas fue de 0.9755. P=0.1225. No se encontraron diferencias significativas entre ellas. Se determinó que el mejor punto de corte corresponde a un Score de 6 con una sensibilidad que corresponde a 89.29% y una especificidad de 79.13%. Conclusiones: El Score SOFA se adapta bien a las pacientes obstétricas admitidas en los ambientes de Cuidados Intensivos Materno, manteniendo un corte de 6 para el SOFA de ingreso con una buena sensibilidad y especificidad. (89.29%, 79.13%). El Score SOFA es útil para ser usado en ambientes de UCI y además debe sugerirse su uso en toda UCI obstétrica de nuestro país

Características clínicas, manejo y mortalidad de pacientes hospitalizados con COVID-19 en un hospital de referencia en Lima, Perú

The objective of this study was to describe the clinical characteristics, management and mortality of adult patients hospitalized by COVID-19 during the first fortnight of May 2020 at the Edgardo Rebagliati Martins National Hospital (Lima, Peru). A retrospective cohort was performed reviewing electronic medical records. Data from 152 patients was collected (68.4% male, average age: 58.7 years). It was found that 27.0% had close contact with a person with COVID-19, 64.2% came to the hospital with a critical illness, 91.4% received hydroxychloroquine and 96.1% received azithromycin, 23.7% entered the intensive care unit, and mortality was 18.8%. In conclusion, more than half of the patients came to this hospital with a critical illness, almost all received drugs that were initially seen as potentially useful but are not currently recommended for the management of COVID-19 in hospitalized, and mortality was similar to that reported in other countries.El presente estudio tuvo por objetivo describir las características clínicas, manejo y mortalidad de pacientes adultos hospitalizados por COVID-19 durante la primera quincena de mayo del 2020 en el Hospital Nacional Edgardo Rebagliati Martins (Lima, Perú). Se realizó una cohorte retrospectiva revisando historias clínicas electrónicas. Se recolectaron datos de 152 pacientes (68,4% varones, edad promedio: 58,7 años). Se encontró que 27,0% tuvo contacto cercano con una persona con COVID-19, 64.2% llegó al hospital con una enfermedad crítica, 91,4% recibió hidroxicloroquina y 96,1% recibió azitromicina, 23,7% ingresó a la unidad de cuidados intensivos, y la mortalidad fue de 18,8%. En conclusión, más de la mitad de los pacientes acudieron a este hospital con una enfermedad crítica, casi todos recibieron fármacos que inicialmente fueron vistos como potencialmente útiles pero que actualmente no son recomendados para el manejo de COVID-19 en hospitalizados, y la mortalidad fue similar a lo reportado en otros países

Nosotros somos nos y somos otros. Estudios dedicados a Félix San Vicente. Tomo II

Estos dos volúmenes de Nosotros somos nos y somos otros reúnen el homenaje que muchos discípulos, compañeros de equipos de investigación, exalumnos y colaboradores, de universidades italianas, españolas y de otros países, han querido rendir al profesor Félix San Vicente Santiago con ocasión de su jubilación. Breves semblanzas, gratos y afectuosos recuerdos, estudios inéditos a él dedicados, junto con una tabula gratulatoria final, describen mejor que otras celebraciones formales e institucionales cuánto de la investigación en el ámbito de la lingüística aplicada a la lengua española y con un enfoque contrastivo sobre la relación histórica y actual entre el español y la lengua italiana se debe a Félix San Vicente

A critical assessment of Mus musculus gene function prediction using integrated genomic evidence

Background: Several years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help focus limited experimental resources on the most likely hypotheses. Several algorithms using diverse genomic data have been applied to this task in model organisms; however, the performance of such approaches in mammals has not yet been evaluated. Results: In this study, a standardized collection of mouse functional genomic data was assembled; nine bioinformatics teams used this data set to independently train classifiers and generate predictions of function, as defined by Gene Ontology (GO) terms, for 21,603 mouse genes; and the best performing submissions were combined in a single set of predictions. We identified strengths and weaknesses of current functional genomic data sets and compared the performance of function prediction algorithms. This analysis inferred functions for 76% of mouse genes, including 5,000 currently uncharacterized genes. At a recall rate of 20%, a unified set of predictions averaged 41% precision, with 26% of GO terms achieving a precision better than 90%. Conclusion: We performed a systematic evaluation of diverse, independently developed computational approaches for predicting gene function from heterogeneous data sources in mammals. The results show that currently available data for mammals allows predictions with both breadth and accuracy. Importantly, many highly novel predictions emerge for the 38% of mouse genes that remain uncharacterized