Identificazione di un QTL principale per resistenza a ruggine bruna sul cromosoma 7B di frumento duro

Leaf rust caused by Puccinia triticina is a serious disease of durum wheat (Triticum durum) worldwide. However, genetic and molecular mapping studies aimed at characterizing leaf rust resistance genes in durum wheat have been only recently undertaken. The Italian durum wheat cv. Creso shows a high level of resistance to P. triticina that has been considered durable and that appears to be due to a combination of a single dominant gene and one or more additional factors conferring partial resistance. In this study, the genetic basis of leaf rust resistance carried by Creso was investigated using 176 recombinant inbred lines (RILs) from the cross between the cv. Colosseo (C, leaf rust resistance donor) and Lloyd (L, susceptible parent). Colosseo is a cv. directly related to Creso with the leaf rust resistance phenotype inherited from Creso, and was considered as resistance donor because of its better adaptation to local (Emilia Romagna, Italy) cultivation environment. RILs have been artificially inoculated with a mixture of 16 Italian P. triticina isolates that were characterized for virulence to seedlings of 22 common wheat cv. Thatcher isolines each carrying a different leaf rust resistance gene, and for molecular genotypes at 15 simple sequence repeat (SSR) loci, in order to determine their specialization with regard to the host species. The characterization of the leaf rust isolates was conducted at the Cereal Disease Laboratory of the University of Minnesota (St. Paul, USA) (Chapter 2). A genetic linkage map was constructed using segregation data from the population of 176 RILs from the cross CL. A total of 662 loci, including 162 simple sequence repeats (SSRs) and 500 Diversity Arrays Technology markers (DArTs), were analyzed by means of the package EasyMap 0.1. The integrated SSR-DArT linkage map consisted of 554 loci (162 SSR and 392 DArT markers) grouped into 19 linkage blocks with an average marker density of 5.7 cM/marker. The final map spanned a total of 2022 cM, which correspond to a tetraploid genome (AABB) coverage of ca. 77% (Chapter 3). The RIL population was phenotyped for their resistance to leaf rust under artificial inoculation in 2006; the percentage of infected leaf area (LRS, leaf rust susceptibility) was evaluated at three stages through the disease developmental cycle and the area under disease progress curve (AUDPC) was then calculated. The response at the seedling stage (infection type, IT) was also investigated. QTL analysis was carried out by means of the Composite Interval Mapping method based on a selection of markers from the CL map. A major QTL (QLr.ubo-7B.2) for leaf rust resistance controlling both the seedling and the adult plant response, was mapped on the distal region of chromosome arm 7BL (deletion bin 7BL10-0.78-1.00), in a gene-dense region known to carry several genes/QTLs for resistance to rusts and other major cereal fungal diseases in wheat and barley. QLr.ubo-7B.2 was identified within a supporting interval of ca. 5 cM tightly associated with three SSR markers (Xbarc340.2, Xgwm146 e Xgwm344.2), and showed an R2 and an LOD peak value for the AUDPC equal to 72.9% an 44.5, respectively. Three additional minor QTLs were also detected (QLr.ubo-7B.1 on chr. 7BS; QLr.ubo-2A on chr. 2AL and QLr.ubo-3A on chr. 3AS) (Chapter 4). The presence of the major QTL (QLr.ubo-7B.2) was validated by a linkage disequilibrium (LD)-based test using field data from two different plant materials: i) a set of 62 advanced lines from multiple crosses involving Creso and his directly related resistance derivates Colosseo and Plinio, and ii) a panel of 164 elite durum wheat accessions representative of the major durum breeding program of the Mediterranean basin. Lines and accessions were phenotyped for leaf rust resistance under artificial inoculation in two different field trials carried out at Argelato (BO, Italy) in 2006 and 2007; the durum elite accessions were also evaluated in two additional field experiments in Obregon (Messico; 2007 and 2008) and in a green-house experiment (seedling resistance) at the Cereal Disease Laboratory (St. Paul, USA, 2008). The molecular characterization involved 14 SSR markers mapping on the 7BL chromosome region found to harbour the major QTL. Association analysis was then performed with a mixed-linear-model approach. Results confirmed the presence of a major QTL for leaf rust resistance, both at adult plant and at seedling stage, located between markers Xbarc340.2, Xgwm146 and Xgwm344.2, in an interval that coincides with the supporting interval (LOD-2) of QLr.ubo-7B.2 as resulted from the RIL QTL analysis. (Chapter 5). The identification and mapping of the major QTL associated to the durable leaf rust resistance carried by Creso, together with the identification of the associated SSR markers, will enhance the selection efficiency in durum wheat breeding programs (MAS, Marker Assisted Selection) and will accelerate the release of cvs. with durable resistance through marker-assisted pyramiding of the tagged resistance genes/QTLs most effective against wheat fungal pathogens.

Characterization of DNA sequence properties through network and statistical approaches

In this thesis we will see that the DNA sequence is constantly shaped by the interactions with its environment at multiple levels, showing footprints of DNA methylation, of its 3D organization and, in the case of bacteria, of the interaction with the host organisms. In the first chapter, we will see that analyzing the distribution of distances between consecutive dinucleotides of the same type along the sequence, we can detect epigenetic and structural footprints. In particular, we will see that CG distance distribution allows to distinguish among organisms of different biological complexity, depending on how much CG sites are involved in DNA methylation. Moreover, we will see that CG and TA can be described by the same fitting function, suggesting a relationship between the two. We will also provide an interpretation of the observed trend, simulating a positioning process guided by the presence and absence of memory. In the end, we will focus on TA distance distribution, characterizing deviations from the trend predicted by the best fitting function, and identifying specific patterns that might be related to peculiar mechanical properties of the DNA and also to epigenetic and structural processes. In the second chapter, we will see how we can map the 3D structure of the DNA onto its sequence. In particular, we devised a network-based algorithm that produces a genome assembly starting from its 3D configuration, using as inputs Hi-C contact maps. Specifically, we will see how we can identify the different chromosomes and reconstruct their sequences by exploiting the spectral properties of the Laplacian operator of a network. In the third chapter, we will see a novel method for source clustering and source attribution, based on a network approach, that allows to identify host-bacteria interaction starting from the detection of Single-Nucleotide Polymorphisms along the sequence of bacterial genomes.

The three-dimensional single-bin-size bin packing problem: combining metaheuristic and machine learning approaches

The Three-Dimensional Single-Bin-Size Bin Packing Problem is one of the most studied problem in the Cutting & Packing category. From a strictly mathematical point of view, it consists of packing a finite set of strongly heterogeneous “small” boxes, called items, into a finite set of identical “large” rectangles, called bins, minimizing the unused volume and requiring that the items are packed without overlapping. The great interest is mainly due to the number of real-world applications in which it arises, such as pallet and container loading, cutting objects out of a piece of material and packaging design. Depending on these real-world applications, more objective functions and more practical constraints could be needed. After a brief discussion about the real-world applications of the problem and a exhaustive literature review, the design of a two-stage algorithm to solve the aforementioned problem is presented. The algorithm must be able to provide the spatial coordinates of the placed boxes vertices and also the optimal boxes input sequence, while guaranteeing geometric, stability, fragility constraints and a reduced computational time. Due to NP-hard complexity of this type of combinatorial problems, a fusion of metaheuristic and machine learning techniques is adopted. In particular, a hybrid genetic algorithm coupled with a feedforward neural network is used. In the first stage, a rich dataset is created starting from a set of real input instances provided by an industrial company and the feedforward neural network is trained on it. After its training, given a new input instance, the hybrid genetic algorithm is able to run using the neural network output as input parameter vector, providing as output the optimal solution. The effectiveness of the proposed works is confirmed via several experimental tests.