Development of SSR primers from EST sequences and their application in germplasm identification of Porphyra lines (Rhodophyta)

This paper reports the development of SSR markers from EST data and their utilization in germplasm identification of Porphyra. The publicly available EST (expressed sequence tag) sequences of Porphyra were searched from the Internet (www.kazura.or.jp/en/plant/porphyra/EST/). From a total of 20,779 obtained EST sequences, 391 SSRs (simple sequence repeats) were analysed with SSRIT software (www.gramene.org/db/searches/ssrtool). From those, 48 SSR primer-pairs were designed and tested by commonly used SSR reaction conditions using 22 Porphyra DNA samples as templates. Results showed that 41 SSR primer-pairs gave good amplification patterns. These were used to conduct SSR analyses of genetic diversity and variety identification of the 22 Porphyra lines. A dendrogram and the DNA fingerprints of the Porphyra lines were developed based on the obtained SSR data.

Identification of Porphyra lines (Rhodophyta) by AFLP DNA fingerprinting and molecular markers

Twenty-seven Porphyra lines from 5 classes, including lines widely used in China, wild lines, and lines introduced to China from abroad in recent years, were screened by means of amplified fragment length polymorphism (AFLP) with 24 primer pairs. From the generated AFLP products, 13 bands that showed stable and repeatable AFLP patterns amplified by primer pairs M-CGA/E-AA and M-CGA/E-TA were scored and used to develop the DNA fingerprints of the 27 Porphyra lines. Moreover, the DNA fingerprinting patterns were converted into computer language expressed with digitals 1 and 0, which represented the presence (numbered as 1) or absence (numbered as 0) of the corresponding band. On the basis of these results, computerized AFLP DNA fingerprints were constructed in which each of the 27 Porphyra lines has its unique AFLP,fingerprinting pattern and can be easily distinguished from others. Software called PGI-AFLP (Porphyra germplasm identification-AFLP) was designed for identification of the 27 Porphyra lines. In addition, 21 specific AFLP markers from 15 Porphyra lines were identified; 6 AFLP markers from 4 Porphyra lines were sequenced, and 2 of them were successfully converted into SCAR (sequence characterized amplification region) markers. The developed AFLP DNA fingerprinting and specific molecular markers provide useful ways for the identification, classification, and resource protection of the Porphyra lines.

Identification of 27 Porphyra lines (Rhodophyta) by DNA fingerprinting and molecular markers

Twenty-seven Porphyra lines, including lines widely used in China, wild lines and lines introduced to China from abroad in recent years, were screened by random amplified polymorphic DNA (RAPD) technique with 120 operon primers. From the generated RAPD products, 11 bands that showed stable and repeatable RAPD patterns amplified by OPC-04, OPJ-18 and OPX-06, respectively were scored and used to develop the DNA fingerprints of the 27 Porphyra lines. Moreover, the DNA fingerprinting patterns were converted into computer language expressed with two digitals, 1 and 0, which represented the presence (numbered as 1) or absence (numbered as 0) of the corresponding band, respectively. Based on the above results, computerized DNA fingerprints were constructed in which each of the 27 Porphyra lines has its unique fingerprinting pattern and can be easily distinguished from others. Software named PGI (Porphyra germplasm identification) was designed for identification of the 27 Porphyra lines. In addition, seven specific RAPD markers from seven Porphyra lines were identified and two of them were successfully converted into SCAR (sequence characterized amplification region) markers. The developed DNA fingerprinting and specific molecular markers provide useful ways for the identification, classification and resource protection of the Porphyra lines.

Molecular identification of 16 Porphyra lines using sequence-related amplified polymorphism markers

Sequence-related amplified polymorphism (SRAP) is a novel molecular marker technique designed to amplify open reading frames (ORFs). The SRAP analytic system was set up and applied to Porphyra germplasm identification in this study for the first time. Sixteen Porphyra lines were screened by SRAP technique with 30 primer combinations. In the analysis, 14 primer combinations produced stable and reproducible amplification patterns in three repetitive experiments. Among the total 533 amplified fragments, 522 (98%) were polymorphic, with an average of 38 fragments for each primer combination, ranging in size from 50 to 500 bp. The 533 fragments were visually scored one by one and then used to develop a dendrogram with Unweighted Pair-Group Method Arithmetic Average (UPGMA), and the 16 Porphyra lines were divided into two major groups at the 0.68 similarity level. From the total 533 fragments, I I amplified by two primer combinations, ME1/EM1 and ME4/EM6, were used to develop the DNA fingerprints of the 16 Porphyra lines. The DNA fingerprints were then converted into binary codes, with I and 0 representing presence and absence of the corresponding amplified fragment, respectively. In the DNA fingerprints, each of the 16 Porphyra lines has its unique binary code and can be easily distinguished from the others. This is the first report on the development of SRAP technique and its utilization in germplasm identification of seaweeds. The results demonstrated that SRAP is a simple, stable, polymorphic and reproducible molecular marker technique for the classification and identification of Porphyra lines. (c) 2007 Elsevier B.V. All rights reserved.

The rapid assignment of ruminal fungi to presumptive genera using ITS1 and ITS2 RNA secondary structures to produce group-specific fingerprints

Danny S. Tuckwell, Matthew J. Nicholson, Christopher S. McSweeney, Michael K. Theodorou and Jayne L. Brookman (2005). The rapid assignment of ruminal fungi to presumptive genera using ITS1 and ITS2 RNA secondary structures to produce group-specific fingerprints. Microbiology, 151 (5) pp.1557-1567 Sponsorship: BBSRC / Stapledon Memorial Trust RAE2008

A novel toxicity fingerprinting method for pollutant identification with lux-marked biosensors

A novel technique is described for the identification and quantification of environmental pollutants based on toxicity fingerprinting with a metabolic lux-marked bacterial biosensor. This method involved characterizing the toxicity-based responses of the biosensor to seven calibration pollutants as acute temporal-dose response fingerprints. An algorithm is described to allow comparisons of responses of an unknown pollutant to be made against the calibration data. This is based on predicting pollutant concentration at each of six different time points over the course of a 5-min assay. If the prediction is consistent between the unknown pollutant and a calibration pollutant at the 95% test level, this is considered to be a positive identification. All seven calibration pollutants could be successfully distinguished from each other with this technique. Environmental samples, individually spiked with single concentrations of pollutants, were compared in this way against the calibration pollutants. An 83% identification success was achieved, with no false positives at the 95% test level. This is a simple and rapid technique that potentially can be applied to monitoring of industrial wastewater or as a screening tool for regulators.

Neuro-Fuzzy based Identification of Meat Spoilage using an Electronic Nose

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). An adaptive fuzzy logic system model that utilizes a prototype defuzzification scheme has been developed to classify beef samples in their respective quality class and to predict their associated microbiological population directly from volatile compounds fingerprints. Results confirmed the superiority of the adopted methodology and indicated that volatile information in combination with an efficient choice of a modeling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage

Identification of pyridine analogs as new predator-derived kairomones.

In the wild, animals have developed survival strategies relying on their senses. The individual ability to identify threatening situations is crucial and leads to increase in the overall fitness of the species. Rodents, for example have developed in their nasal cavities specialized olfactory neurons implicated in the detection of volatile cues encoding for impending danger such as predator scents or alarm pheromones. In particular, the neurons of the Grueneberg ganglion (GG), an olfactory subsystem, are implicated in the detection of danger cues sharing a similar chemical signature, a heterocyclic sulfur- or nitrogen-containing motif. Here we used a "from the wild to the lab" approach to identify new molecules that are involuntarily emitted by predators and that initiate fear-related responses in the recipient animal, the putative prey. We collected urines from carnivores as sources of predator scents and first verified their impact on the blood pressure of the mice. With this approach, the urine of the mountain lion emerged as the most potent source of chemical stress. We then identified in this biological fluid, new volatile cues with characteristic GG-related fingerprints, in particular the methylated pyridine structures, 2,4-lutidine and its analogs. We finally verified their encoded danger quality and demonstrated their ability to mimic the effects of the predator urine on GG neurons, on mice blood pressure and in behavioral experiments. In summary, we were able to identify here, with the use of an integrative approach, new relevant molecules, the pyridine analogs, implicated in interspecies danger communication.

Development of molecular-based techniques for the detection, identification and quantification of food-borne pathogens

La presencia de microorganismos patógenos en alimentos es uno de los problemas esenciales en salud pública, y las enfermedades producidas por los mismos es una de las causas más importantes de enfermedad. Por tanto, la aplicación de controles microbiológicos dentro de los programas de aseguramiento de la calidad es una premisa para minimizar el riesgo de infección de los consumidores. Los métodos microbiológicos clásicos requieren, en general, el uso de pre-enriquecimientos no-selectivos, enriquecimientos selectivos, aislamiento en medios selectivos y la confirmación posterior usando pruebas basadas en la morfología, bioquímica y serología propias de cada uno de los microorganismos objeto de estudio. Por lo tanto, estos métodos son laboriosos, requieren un largo proceso para obtener resultados definitivos y, además, no siempre pueden realizarse. Para solucionar estos inconvenientes se han desarrollado diversas metodologías alternativas para la detección identificación y cuantificación de microorganismos patógenos de origen alimentario, entre las que destacan los métodos inmunológicos y moleculares. En esta última categoría, la técnica basada en la reacción en cadena de la polimerasa (PCR) se ha convertido en la técnica diagnóstica más popular en microbiología, y recientemente, la introducción de una mejora de ésta, la PCR a tiempo real, ha producido una segunda revolución en la metodología diagnóstica molecular, como pude observarse por el número creciente de publicaciones científicas y la aparición continua de nuevos kits comerciales. La PCR a tiempo real es una técnica altamente sensible -detección de hasta una molécula- que permite la cuantificación exacta de secuencias de ADN específicas de microorganismos patógenos de origen alimentario. Además, otras ventajas que favorecen su implantación potencial en laboratorios de análisis de alimentos son su rapidez, sencillez y el formato en tubo cerrado que puede evitar contaminaciones post-PCR y favorece la automatización y un alto rendimiento. En este trabajo se han desarrollado técnicas moleculares (PCR y NASBA) sensibles y fiables para la detección, identificación y cuantificación de bacterias patogénicas de origen alimentario (Listeria spp., Mycobacterium avium subsp. paratuberculosis y Salmonella spp.). En concreto, se han diseñado y optimizado métodos basados en la técnica de PCR a tiempo real para cada uno de estos agentes: L. monocytogenes, L. innocua, Listeria spp. M. avium subsp. paratuberculosis, y también se ha optimizado y evaluado en diferentes centros un método previamente desarrollado para Salmonella spp. Además, se ha diseñado y optimizado un método basado en la técnica NASBA para la detección específica de M. avium subsp. paratuberculosis. También se evaluó la aplicación potencial de la técnica NASBA para la detección específica de formas viables de este microorganismo. Todos los métodos presentaron una especificidad del 100 % con una sensibilidad adecuada para su aplicación potencial a muestras reales de alimentos. Además, se han desarrollado y evaluado procedimientos de preparación de las muestras en productos cárnicos, productos pesqueros, leche y agua. De esta manera se han desarrollado métodos basados en la PCR a tiempo real totalmente específicos y altamente sensibles para la determinación cuantitativa de L. monocytogenes en productos cárnicos y en salmón y productos derivados como el salmón ahumado y de M. avium subsp. paratuberculosis en muestras de agua y leche. Además este último método ha sido también aplicado para evaluar la presencia de este microorganismo en el intestino de pacientes con la enfermedad de Crohn's, a partir de biopsias obtenidas de colonoscopia de voluntarios afectados. En conclusión, este estudio presenta ensayos moleculares selectivos y sensibles para la detección de patógenos en alimentos (Listeria spp., Mycobacterium avium subsp. paratuberculosis) y para una rápida e inambigua identificación de Salmonella spp. La exactitud relativa de los ensayos ha sido excelente, si se comparan con los métodos microbiológicos de referencia y pueden serusados para la cuantificación de tanto ADN genómico como de suspensiones celulares. Por otro lado, la combinación con tratamientos de preamplificación ha resultado ser de gran eficiencia para el análisis de las bacterias objeto de estudio. Por tanto, pueden constituir una estrategia útil para la detección rápida y sensible de patógenos en alimentos y deberían ser una herramienta adicional al rango de herramientas diagnósticas disponibles para el estudio de patógenos de origen alimentario.

Retention time prediction and protein identification

Proteins are commonly identified through enzymatic digestion and generation of short sequence tags or fingerprints of peptide masses by mass spectrometry. Separation methods, such as liquid chromatography and electrophoresis, are often used to fractionate complex protein or peptide mixtures and these separations also provide information on the different species, such as molecular weight and isoelectric point from electrophoresis and hydrophobicity in reversed-phase chromatography. These are also properties that can be predicted from amino acid sequences derived from genomic sequences and used in protein identification. This chapter reviews recently introduced methods based on retention time prediction to extract information from chromatographic separations and the applications to protein identification in organisms with small and large genomes. Novel data on retention time prediction of posttranslationally modified peptides is also presented.

Proteome analysis for identification of target proteins of genistein in primary human endothelial cells stressed with oxidized LDL or homocysteine

Background Epidemiological studies suggest that soy consumption contributes to the prevention of coronary heart disease. The proposed anti-atherogenic effects of soy appear to be carried by the soy isoflavones with genistein as the most abundant compound. Aim of the study To identify proteins or pathways by which genistein might exert its protective activities on atherosclerosis, we analyzed the proteomic response of primary human umbilical vein endothelial cells ( HUVEC) that were exposed to the pro-atherosclerotic stressors homocysteine or oxidized low-density lipoprotein (ox-LDL). Methods HUVEC were incubated with physiological concentrations of homocysteine or ox-LDL in the absence and presence of genistein at concentrations that can be reached in human plasma by a diet rich in soy products (2.5 muM) or by pharmacological intervention ( 25 muM). Proteins from HUVEC were separated by two-dimensional polyacrylamide gel electrophoresis and those that showed altered expression level upon genistein treatment were identified by peptide mass fingerprints derived from tryptic digests of the protein spots. Results Several proteins were found to be differentially affected by genistein. The most interesting proteins that were potently decreased by homocysteine treatment were annexin V and lamin A. Annexin V is an antithrombotic molecule and mutations in nuclear lamin A have been found to result in perturbations of plasma lipids associated with hypertension. Genistein at low and high concentrations reversed the stressor-induced decrease of these anti-atherogenic proteins. Ox-LDL treatment of HUVEC resulted in an increase in ubiquitin conjugating enzyme 12, a protein involved in foam cell formation. Treatment with genistein at both doses reversed this effect. Conclusions Proteome analysis allows the identification of potential interactions of dietary components in the molecular process of atherosclerosis and consequently provides a powerful tool to define biomarkers of response.

An approach based on neural networks for identification of fault sections in radial distribution systems

The main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way, several approaches that can contribute to the successful detection process of faults, aiming that it is carried out in a reliable and safe way. The compilations of the results obtained from practical experiments accomplished in a pilot radial distribution feeder have demonstrated that the developed techniques provide accurate results, identifying and classifying efficiently the several occurrences of faults observed in the feeder.

What is the importance of selecting features for non-technical losses identification?

Although non-technical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy has not attracted much attention in this context. In this paper, we focus on this problem applying a novel feature selection algorithm based on Particle Swarm Optimization and Optimum-Path Forest. The results demonstrated that this method can improve the classification accuracy of possible frauds up to 49% in some datasets composed by industrial and commercial profiles. © 2011 IEEE.

Improving Parkinson's disease identification through evolutionary-based feature selection

Parkinson's disease (PD) automatic identification has been actively pursued over several works in the literature. In this paper, we deal with this problem by applying evolutionary-based techniques in order to find the subset of features that maximize the accuracy of the Optimum-Path Forest (OPF) classifier. The reason for the choice of this classifier relies on its fast training phase, given that each possible solution to be optimized is guided by the OPF accuracy. We also show results that improved other ones recently obtained in the context of PD automatic identification. © 2011 IEEE.