Information extraction from bibliography for Marker Assisted Selection in wheat

Improvement of most animal and plant species of agronomical interest in the near future has become an international stake because of the increasing demand for feeding a growing world population and to mitigate the reduction of the industrial resources. The recent advent of genomic tools contributed to improve the discovery of linkage between molecular markers and genes that are involved in the control of traits of agronomical interest such as grain number or disease resistance. This information is mostly published as scientific papers but rarely available in databases. Here, we present a method aiming at automatically extract this information from the scientific literature and relying on a knowledge model of the target information and on the WheatPhenotype ontology that we developed for this purpose. The information extraction results were evaluated and integrated into the on-line semantic search engine [i]AlvisIR WheatMarker.[/i

Combining and mapping QTL for fusarium head blight (FHB) resistance in wheat

Fusarium head blight (FHB) has become one of the most damaging wheat diseases in humid and semi-humid regions around the world. Single gene resistance to FHB in wheat provides only partial resistance and also the disease severity is highly influenced by environment. Consequently multiple genes are required for effective resistance. Our hypothesis is that identifying DNA markers for type I resistance will be very beneficial for selection, and combining type I and type II FHB resistance will be more effective than either type alone. The objectives of this project are to 1) combine type I resistance from cultivars Goldfield, INW0412, Bess, 99751, and Truman; and type II resistance of Fhb1 and Qfhs.pur-7EL backcrossed into adapted soft winter wheat lines and quantify augmentation of FHB resistance and 2) characterize a RIL population from the cross INW0412 (type I resistance)/992060G1 (susceptible) for frequency of initial infection and map QTLs for type I resistance. For objective 1, QTL from Sumai3 on chromosome 3B (Fhb1), from tall wheatgrass on 7EL (Qfhs.pur-7EL), and from Goldfield together provided high resistance, whereas epistatic interactions among those three QTL resulted in lower resistance than expected. QTL from Sumai3 and from tall wheatgrass on 7EL (Qfhs.pur-7EL) each significantly improved type II FHB resistance. No effect on increasing type I FHB resistance was detected in the presence of the QTL on 2B in these lines, which may be overshadowed by other potential genes controlling type I resistance that presented. Combining cultivars with type I and type II FHB resistance provided lines with high FHB resistance that will be beneficial to improve wheat cultivars. For objective 2, a population of 198 RILs and the two parents were characterized for FHB incidence at Lafayette, IN in 2011 and 2013 and in the greenhouse 2012 and 2013. A two-enzyme genotyping-by-sequencing (GBS) approach was applied to construct a 1,883 cM linkage map. Composite interval mapping analysis detected a QTL on chromosome 1AS under greenhouse conditions, and three other QTL on chromosomes 1BL, 2BL, and 3AS under field environments. Each QTL explained between 7.44% and 12.20% of the total phenotypic variation. RILs with all three QTL on chromosomes 1BL, 2BL, and 3AS significantly improved type I resistance by 33.06% in the field experiments. Our results also confirmed that type I and type II FHB resistance were controlled by different loci in wheat

FUSARIUM HEAD BLIGHT RESISTANCE AND AGRONOMIC PERFORMANCE IN SOFT RED WINTER WHEAT POPULATIONS

Fusarium head blight (FHB), caused by Fusarium graminearum Schwabe [telomorph: Gibberella zeae Schwein.(Petch)], is recognized as one of the most destructive diseases of wheat (Triticum aestivum L. and T. durum L.) and barley (Hordeum vulgare L.) worldwide. Breeding for FHB resistance must be accompanied by selection for desirable agronomic traits. Donor parents with two FHB resistance quantitative trait loci (QTL) Fhb1 (chromosome 3BS) and QFhs.nau-2DL (chromosome 2DL) were crossed to four adapted SRW wheat lines to generate backcross and forward cross progeny. F2 individuals were genotyped and assigned to 4 different groups according to presence/ absence of one or both QTL. The effectiveness of these QTL in reducing FHB in F2 derived lines was assessed in a misted, inoculated scab nursery. Resistance alleles and the interaction among FHB resistance QTL have distinct behavior in different genetic backgrounds in wheat. Fhb1 showed an average disease reduction of 12%, however it did not result in significant improvement of FHB resistance in all populations. In general, for the four backgrounds studied, the QFhs.nau-2DL QTL as more effective reducing FHB (19% average reduction). The combination of Fhb1 and QFhs.nau-2DL is not necessary, but recommended and it improved resistance in all populations. Backcross derived (BC) progeny from four genetic backgrounds were planted in replicated plots (2011 and 2012) and in the scab nursery in 2012. Population 2 had its progeny characterized by 961 DArT markers distributed throughout the genome. Several high-quality polymorphic markers were identified and listed as good predictors of phenotypic traits like disease resistance, and improved agronomic and quality characteristics. Backcross and forward cross derived progenies were tested for FHB resistance and agronomic and baking quality performance for 4 different populations sharing the same donor parent for resistance QTL. The results confirmed that F2 populations were effective indicators of expression levels of QTL prior to extensive backcrossing. The QTL Fhb1 and QFhs.nau-2DL increased FHB resistance without detriments on agronomic and quality traits on wheat populations investigated. BC populations were assessed as breeding populations and established as being rewarding tools for derivation of inbred lines in a breeding program, being BC2 the most recommended from our results

The use of genetic information to predict the relative maturity of soybeans

Research suggests that in North America, soybean Relative Maturity (RM) is controlled by a minimum of eight genetic loci labeled E loci. The amount of variation explained by these genes would suggest that accurate predictions for RM could be obtained using prediction models that only include allele effects for markers located near the major E genes. Having the ability to accurately predict the RM of a segregating breeding line using genetic information has the potential to positively impact both the rate of genetic gain and cost per unit of genetic gain within a breeding program by enabling; 1) prediction of RM in segregating progeny from crosses between parents with large differences in RM; 2) selection of segregating lines with appropriate RMs in non-adapted off season nurseries; 3) increased selection intensities of segregating lines assigned to field trials; and 4) cost reduction of replicated field trials. The objectives of this research then was to; 1) compare the accuracy of RM prediction using genome wide markers versus using prediction models containing only molecular markers significantly associated with RM; 2) validate that prediction accuracies were maintained when predictions were made for segregating lines not only having distant relationships to those in the original training dataset, but also developed and grown outside of the years of the segregating lines in the original training dataset; and 3) evaluate if the prediction accuracies and associated genotyping costs support wide scale RM prediction within a soybean cultivar development program. In effort to determine if the RM of a segregating soybean breeding line could be predicted using genetic information, we developed a training dataset that consisted of 1,244 F4 derived advanced stage segregating soybean lines having known RMs ranging from RM 1.3 to 8.0 that were genotyped with 1,817 genome wide single nucleotide polymorphism (SNP) markers. The segregating lines were selected from multiple families that were the result of hundreds of breeding crosses made over multiple years in a soybean cultivar development program. The data were utilized to determine allele effects for four prediction models, two models that represented traditional Marker Assisted Selection (MAS) approaches using only markers associated with known E genes or within regions of the genome thought to influence RM (specific E-gene and expanded E-gene) and two Genomic Prediction (GP) models with distinct marker densities (full GP model and reduced GP model). The GP and expanded E-gene prediction models evaluated in the study produced an average across RM prediction accuracy from 0.93 to 0.94 while the E-gene specific model prediction accuracy was 0.81. The results indicated that the E genes identified in the literature were highly predictive of RM, the greatest prediction accuracies however were obtained through the use of whole genome marker panels. While the results from the initial research were promising, additional research was required to determine if the prediction accuracies could be maintained when predictions were made on segregating lines outside of the years of those contained within the original training dataset. In an attempt to strengthen the prediction accuracies obtained for the early and late maturities, the original training dataset was expanded to include a total of 2,194 segregating lines that were selected from replicated field trials in 2009-2013 having validated RM phenotypes that ranged from RM 0.0 to 8.0. All of the 2,194 segregating lines within the updated training dataset had previously been genotyped using 1,118 genome wide SNP markers. Since it was identified in the preliminary research that prediction accuracies were highest when whole genome marker panels were used, only a full GP model using allele effect estimates for all 1,118 SNP markers was evaluated in this study. The 1,118 SNP marker GP model successfully predicted the RM’s of 1,854 segregating lines in 2014 and 1,465 segregating lines in 2015. The estimated correlation between predicted RM (RMp) and validated RM (RMv) for all segregating lines was 0.95 with an average difference between RMp and RMv of 4 days. Prediction accuracies were again the lowest for segregating lines with RMv earlier than 1.0 and later than 5.0 which we feel was still likely the result of a small number of segregating lines in the training set for those RM groups. Alternative metrics including the frequency of RMp within 0.5 of RMv, f(|RMp-RMv|≤ 0.5) and the frequency of RMp within 0.25 of RMv, f(|RMp-RMv|≤0.25) were developed that indicated that across years, 66% of the segregating lines had RMp that were within 5 days of their RMv and 39% of the segregating lines had RMp that were within 2.5 days or their RMv. The f(|RMp-RMv|≤ 0.5) and f(|RMp-RMv|≤0.25) improved to 73% and 46% respectively when only segregating lines with RMv that ranged from 1.0 – 5.9 were evaluated. While the results from this second round of research proved that genetic information could be used to predict the RM of segregating lines with relatively high accuracy across the maturity groups grown within NA, additional analysis was required to determine if wide scale implementation could be justified within a breeding program. In effort to determine if the prediction accuracies and genotyping costs associated with predicting the RM of segregating lines using genetic information could be justified for wide scale implementation within a breeding program, we evaluated the program wide implementation of RM prediction using basic principles of Operations Research (OR). A simple Microsoft Excel based tool termed the Genomic Prediction Evaluation Tool (GPE tool) was built that allowed all possible cultivar development scenarios that exist within the Iowa State University soybean breeding program to be evaluated to determine both Total Program Cost (TPC) and Relative Breeding Design Efficiency (RBDE). Optimal breeding designs were those designs that both maximized RBDE while minimizing TPC. Two analysis were conducted using the GPE tool. The first analysis (analysis 1) determined the total number of years to reach the final year of replicated field trials as the number of years from the initiation of crossing to the final year of field trials. The second analysis (analysis 2) added a year to the total number of years from crossing to the final year of field trials for those designs that utilized a North American summer crossing block, thus decreasing associated RBDE. Of the optimal breeding designs identified from both analysis, no design was identified that recommended the use of RM prediction to support the cultivar development process, the associated cost of implementation was simply too high. Slight modifications to the current version of the GPE tool should allow the ISU breeding program to identify more efficient breeding designs as compared to the current design that has been implemented to date. The GPE in its current version sets the foundation to build a tool that will provide soybean breeders the ability to appropriately evaluate the potential wide scale implementation of GS to predict complex phenotypes in support of soybean variety development

Analytical strategies for the characterization, identification, and quantification of peptides and proteins of interest in the prevention and understanding of hypertension

Premio Extraordinario de Doctorado de la UAH en el año académico 2013-2014La hipertensión constituye un serio problema de salud y está considerada como una de las causas de las enfermedades cardiovasculares y renales. A pesar de la prevalencia de la hipertensión, la mitad de las personas afectadas desconocen que la padecen. Además, los estudios realizados acerca de las causas más importantes de muerte en el mundo predicen un aumento de la contribución de las enfermedades cardiovasculares. Debido a que los síntomas de la hipertensión pueden permanecer inadvertidos y raramente se ponen de manifiesto, la prevención y tratamiento de la hipertensión se consideran de gran importancia en la sociedad moderna. El tratamiento de la hipertensión ha disminuido la incidencia de accidentes cardiovasculares. Sin embargo, aunque la utilización de fármacos sintéticos ha sido decisiva en este descenso, estos fármacos poseen habitualmente efectos secundarios. Una alternativa interesante a estos fármacos la constituyen algunos péptidos que se encuentran de forma natural en determinados alimentos. De hecho, una estrategia básica para mejorar la salud cardiovascular es modificar la dieta y el estilo de vida ya que la dieta es uno de los factores que más influencia tienen en la salud humana. Entre los compuestos bioactivos que se encuentran en los alimentos, los péptidos bioactivos están recibiendo una gran atención en los últimos años. En particular, los péptidos antihipertensivos y antioxidantes son los más estudiados y han demostrado su efecto positivo sobre la salud cardiovascular. En efecto, los péptidos antihipertensivos pueden inhibir la actividad de la ACE y disminuir la presión arterial. Por su parte, los péptidos antioxidantes previenen el estrés oxidativo que puede iniciar y promover la aparición de la hipertensión. Los péptidos bioactivos pueden ser ingredientes naturales de los alimentos o bien originarse a partir de proteínas alimentarias de las que forman parte ya sea por procedimientos in vivo o in vitro. La digestión gastrointestinal constituye en sí misma un procedimiento in vivo mientras que el procedimiento in vitro implica la hidrólisis de las proteínas por la acción de enzimas o microorganismos adicionados a los alimentos. En el caso de productos procesados, los péptidos bioactivos se liberan a partir de las proteínas durante el procesado del alimento (queso, yogurt, kéfir, etc.). Hasta la fecha, la investigación relacionada con péptidos antihipertensivos y antioxidantes se ha centrado principalmente en alimentos de origen animal como la leche, los productos lácteos y la carne. Sin embargo, los péptidos bioactivos de origen vegetal, aunque menos estudiados, a menudo poseen actividades más altas. El maíz y la soja, son ejemplos de fuentes de péptidos bioactivos de elevada actividad. Las fórmulas infantiles de soja (SBIFs) constituyen una alternativa muy interesante a la leche y los productos lácteos para niños con intolerancia o alergia a algunos constituyentes de la leche, con problemas de alimentación o pertenecientes a familias vegetarianas. Sin embargo, en comparación con la leche y los productos lácteos, las SBIFs han sido poco investigadas en lo que al contenido de péptidos bioactivos se refiere. Estas fórmulas infantiles se elaboran a partir de aislados de proteína de soja que contienen alrededor de un 90% de proteínas. Durante su preparación, se someten a calor intenso o a hidrólisis proteica. Así, las SBIFs pueden contener de forma natural péptidos potencialmente bioactivos con efectos beneficiosos sobre la salud a parte de sus beneficios nutricionales. En este trabajo, las SBIFs se han elegido como una fuente potencial de péptidos bioactivos. Se han propuesto cuatro métodos diferentes para extraer péptidos de las SBIFs. En este trabajo, se ha desarrollado una metodología analítica para la determinación simultánea de los péptidos LRP, LSP y LQP de las [alfa]-zeínas presentes en granos de maíz. Adicionalmente, se desarrolló una metodología analítica por HPLC-Q-ToF-MS para la determinación de los tres péptidos mencionados en maíz. Con fines a llevar a cabo la determinación del péptido VLIVP en habas de soja, en este trabajo se desarrolló también una metodología analítica utilizando HPLC capilar acoplada a un sistema de espectrometría de masas de trampa de iones (HPLC capilar-IT-MS). Finalmente, en este trabajo se ha desarrollado un método SRM (selected reaction monitoring) utilizando detección por espectrometría de masas de triple cuadrupolo (QqQ) para evaluar el contenido de las isoformas de las proteínas kinasas PKA, PKG y CaMKII en distintos tejidos de rata. A modo de resumen, en este trabajo se ha investigado por primera vez la presencia de péptidos bioactivos nativos presentes en SBIFs. Estos estudios han permitido obtener una visión amplia del potencial de los péptidos bioactivos presentes en estos alimentos para bebés y al mismo tiempo observar grandes diferencias entre ellos contribuyendo a incrementar el conocimiento del valor nutricional real de estos alimentos así como de sus efectos fisiológicos y biológicos. Por otra parte, también se han desarrollado metodologías analíticas para la determinación de péptidos inhibidores de la ACE altamente potentes en cultivos de maíz y soja. Estos métodos se han caracterizado y se han aplicado al análisis de diferentes variedades de estos cultivos. Los resultados obtenidos tienen un importante potencial en el campo de la ciencia de los alimentos íntimamente relacionada con el área de la biomedicina. Finalmente, otro aspecto investigado en este trabajo ha sido la determinación de isoformas de proteínas de alto interés cardiovascular en diferentes tejidos de rata. Aunque se desarrolló un método SRM apropiado, la complejidad de la muestra no permitió la cuantificación fiable de las isoformas de PKA, PKG y CaMKII por lo que son necesarios más estudios para superar esta dificultad. Una vez superada, esta estrategia podría tener un impacto enorme en la investigación de los mecanismos moleculares que intervienen en el sistema cardiovascular

Leaf Rubisco turnover variation in a perennial ryegrass (Lolium perenne L.) population : analysis of quantitative trait loci, implications for productivity, and potential for manipulation : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Plant Science, Massey University, Institute of Natural Resources, College of Sciences, Palmerston North, New Zealand

The Grasslands II perennial ryegrass (Lolium perenne L.) mapping population comprising F1 progeny with the two parents (one plant each from the cultivars Samson and Impact) (Crush et al., 2007) was used to detect putative quantitative trait loci (QTL) for leaf Rubisco turnover and herbage yield traits. Rubisco turnover was described by three mathematical parameters: d (maximum Rubisco content), g (time of d) and f (a measure of curve width). All three parameters exhibited continuous variation among the F1 progeny. Sixteen QTL were detected, seven for Rubisco turnover and nine for herbage yield traits. Support interval overlap on linkage group (LG) 1 and close location on LG2 for plant dry weight (DW) QTL in this study and in a previous analysis (Sartie, 2007) of the same mapping population suggests DW QTL stability across environments. Some QTL identified by Sartie (2007) were not re-confirmed in this study, but new QTL were identified. This suggests genotype x environment interaction generated by variable expression of genes in different environments. Clusters of QTL with overlapping support intervals were found on LG2 and LG5. The cluster on LG2 included QTL for herbage yield traits leaf lamina length (LL), tiller number (TN), productivity index (PI) and DW. The cluster on LG5 included QTL for DW, PI, TN, and d. These two regions offer potential for plant breeding applications. Apart from the QTL for d on LG5, there was no co-location of Rubisco turnover and herbage yield QTL. However, principal component analysis indicated plants with lower d tended to have higher DW; thus Rubisco turnover effects on plant productivity may relate to energy cost of Rubisco synthesis rather than photosynthetic capacity. DW was generally unrelated to f and g; therefore, hypothesised nitrogen use inefficiencies arising from premature Rubisco degradation, or retention of Rubisco at leaf senescence, were not confirmed. LG5 and LG7 on which QTL for d were located have conserved syntenic regions with rice chromosomes 8 and 9 where QTL for Rubisco content at different stages during heading were mapped by Ishimaru et al (2001a)