ddRADseq-mediated detection of genetic variants in sugarcane.

KEY MESSAGE: The article presents an optimization of the key parameters for the identification of SNPs in sugarcane using a GBS protocol based on two Illumina NextSeq and NovaSeq platforms. Sugarcane (Saccharum sp.), a world-wide known feedstock for sugar production, bioethanol, and energy, has an extremely complex genome, being highly polyploid and aneuploid. A double-digestion restriction site-associated DNA sequencing protocol (ddRADseq) was tested in four commercial sugarcane hybrids and one high-fibre biotype for the detection of single nucleotide polymorphisms (SNPs). In this work we tested two Illumina sequencing platforms, read size (70 vs. 150 bp), different sequencing coverage per individual (medium and high coverage), and single-reads versus paired-end reads. We also explored different variant calling strategies (with and without reference genome) and filtering schemes [combining two minor allele frequencies (MAFs) with three depth of coverage thresholds]. For the discovery of a large number of novel SNPs in sugarcane, we recommend longer size and paired-end reads, medium sequencing coverage per individual and Illumina platform NovaSeq6000 for a cost-effective approach, and filter parameters of lower MAF and higher depth coverages thresholds. Although the de novo analysis retrieved more SNPs, the reference-based method allows downstream characterization of variants. For the two best performing matrices, the number of SNPs per chromosome correlated positively with chromosome length, demonstrating the presence of variants throughout the genome. Multivariate comparisons, with both matrices, showed closer relationships among commercial hybrids than with the high-fibre biotype. Functional analysis of the SNPs demonstrated that more than half of them landed within regulatory regions, whereas the other half affected coding, intergenic and intronic regions. Allelic distances values were lower than 0.07 when analysing two replicated genotypes, confirming the protocol robustness.

Plastome genomics in South American maize landraces: chloroplast lineages parallel the geographical structuring of nuclear gene pools.

BACKGROUND AND AIMS: The number of plastome sequences has increased exponentially during the last decade. However, there is still little knowledge of the levels and distribution of intraspecific variation. The aims of this study were to estimate plastome diversity within Zea mays and analyse the distribution of haplotypes in connection with the landrace groups previously delimited for South American maize based on nuclear markers. METHODS: We obtained the complete plastomes of 30 South American maize landraces and three teosintes by means of next-generation sequencing (NGS) and used them in combination with data from public repositories. After quality filtering, the curated data were employed to search for single-nucleotide polymorphisms, indels and chloroplast simple sequence repeats. Exact permutational contingency tests were performed to assess associations between plastome and nuclear variation. Network and Bayesian phylogenetic analyses were used to infer evolutionary relationships among haplotypes. KEY RESULTS: Our analyses identified a total of 124 polymorphic plastome loci, with the intergenic regions psbE-rps18, petN-rpoB, trnL_UAG-ndhF and rpoC2-atpI exhibiting the highest marker densities. Although restricted in number, these markers allowed the discrimination of 27 haplotypes in a total of 51 Zea mays individuals. Andean and lowland South American landraces differed significantly in haplotype distribution. However, overall differentiation patterns were not informative with respect to subspecies diversification, as evidenced by the scattered distribution of maize and teosinte plastomes in both the network and Bayesian phylogenetic reconstructions. CONCLUSIONS: Knowledge of intraspecific plastome variation provides the framework for a more comprehensive understanding of evolutionary processes at low taxonomic levels and may become increasingly important for future plant barcoding efforts. Whole-plastome sequencing provided useful variability to contribute to maize phylogeographic studies. The structuring of haplotype diversity in the maize landraces examined here clearly reflects the distinction between the Andean and South American lowland gene pools previously inferred based on nuclear markers.

Exploring gene networks in two sunflower lines with contrasting leaf senescence phenotype using a system biology approach.

BACKGROUND: Leaf senescence is a complex process, controlled by multiple genetic and environmental variables. In sunflower, leaf senescence is triggered abruptly following anthesis thereby limiting the capacity of plants to keep their green leaf area during grain filling, which subsequently has a strong impact on crop yield. Recently, we performed a selection of contrasting sunflower inbred lines for the progress of leaf senescence through a physiological, cytological and molecular approach. Here we present a large scale transcriptomic analysis using RNA-seq and its integration with metabolic profiles for two contrasting sunflower inbred lines, R453 and B481-6 (early and delayed senescence respectively), with the aim of identifying metabolic pathways associated to leaf senescence. RESULTS: Gene expression profiles revealed a higher number of differentially expressed genes, as well as, higher expression levels in R453, providing evidence for early activation of the senescence program in this line. Metabolic pathways associated with sugars and nutrient recycling were differentially regulated between the lines. Additionally, we identified transcription factors acting as hubs in the co-expression networks; some previously reported as senescence-associated genes in model species but many are novel candidate genes. CONCLUSIONS: Understanding the onset and the progress of the senescence process in crops and the identification of these new candidate genes will likely prove highly useful for different management strategies to mitigate the impact of senescence on crop yield. Functional characterization of candidate genes will help to develop molecular tools for biotechnological applications in breeding crop yield.

Transcript and metabolic adjustments triggered by drought in Ilex paraguariensis leaves.

MAIN CONCLUSION: Abscisic acid is involved in the drought response of Ilex paraguariensis. Acclimation includes root growth stimulation, stomatal closure, osmotic adjustment, photoprotection, and regulation of nonstructural carbohydrates and amino acid metabolisms. Ilex paraguariensis (yerba mate) is cultivated in the subtropical region of South America, where the occurrence of drought episodes limit yield. To explore the mechanisms that allow I. paraguariensis to overcome dehydration, we investigated (1) how gene expression varied between water-stressed and non-stressed plants and (2) in what way the modulation of gene expression was linked to physiological status and metabolite composition. A total of 4920 differentially expressed transcripts were obtained through RNA-Seq after water deprivation. Drought induced the expression of several transcripts involved in the ABA-signalling pathway. Stomatal closure and leaf osmotic adjustments were promoted to minimize water loss, and these responses were accompanied by a high transcriptional remodeling of stress perception, signalling and transcriptional regulation, the photoprotective and antioxidant systems, and other stress-responsive genes. Simultaneously, significant changes in metabolite contents were detected. Glutamine, phenylalanine, isomaltose, fucose, and malate levels were shown to be positively correlated with dehydration. Principal component analysis showed differences in the metabolic profiles of control and stressed leaves. These results provide a comprehensive overview of how I. paraguariensis responds to dehydration at transcriptional and metabolomic levels and provide further characterization of the molecular mechanisms associated with drought response in perennial subtropical species.

ATGC transcriptomics: a web-based application to integrate, explore and analyze de novo transcriptomic data.

BACKGROUND: In the last years, applications based on massively parallelized RNA sequencing (RNA-seq) have become valuable approaches for studying non-model species, e.g., without a fully sequenced genome. RNA-seq is a useful tool for detecting novel transcripts and genetic variations and for evaluating differential gene expression by digital measurements. The large and complex datasets resulting from functional genomic experiments represent a challenge in data processing, management, and analysis. This problem is especially significant for small research groups working with non-model species. RESULTS: We developed a web-based application, called ATGC transcriptomics, with a flexible and adaptable interface that allows users to work with new generation sequencing (NGS) transcriptomic analysis results using an ontology-driven database. This new application simplifies data exploration, visualization, and integration for a better comprehension of the results. CONCLUSIONS: ATGC transcriptomics provides access to non-expert computer users and small research groups to a scalable storage option and simple data integration, including database administration and management. The software is freely available under the terms of GNU public license at http://atgcinta.sourceforge.net .

Integration of transcriptomic and metabolic data reveals hub transcription factors involved in drought stress response in sunflower (Helianthus annuus L.).

KEY MESSAGE: By integration of transcriptional and metabolic profiles we identified pathways and hubs transcription factors regulated during drought conditions in sunflower, useful for applications in molecular and/or biotechnological breeding. Drought is one of the most important environmental stresses that effects crop productivity in many agricultural regions. Sunflower is tolerant to drought conditions but the mechanisms involved in this tolerance remain unclear at the molecular level. The aim of this study was to characterize and integrate transcriptional and metabolic pathways related to drought stress in sunflower plants, by using a system biology approach. Our results showed a delay in plant senescence with an increase in the expression level of photosynthesis related genes as well as higher levels of sugars, osmoprotectant amino acids and ionic nutrients under drought conditions. In addition, we identified transcription factors that were upregulated during drought conditions and that may act as hubs in the transcriptional network. Many of these transcription factors belong to families implicated in the drought response in model species. The integration of transcriptomic and metabolomic data in this study, together with physiological measurements, has improved our understanding of the biological responses during droughts and contributes to elucidate the molecular mechanisms involved under this environmental condition. These findings will provide useful biotechnological tools to improve stress tolerance while maintaining crop yield under restricted water availability.

Network and biosignature analysis for the integration of transcriptomic and metabolomic data to characterize leaf senescence process in sunflower.

BACKGROUND: In recent years, high throughput technologies have led to an increase of datasets from omics disciplines allowing the understanding of the complex regulatory networks associated with biological processes. Leaf senescence is a complex mechanism controlled by multiple genetic and environmental variables, which has a strong impact on crop yield. Transcription factors (TFs) are key proteins in the regulation of gene expression, regulating different signaling pathways; their function is crucial for triggering and/or regulating different aspects of the leaf senescence process. The study of TF interactions and their integration with metabolic profiles under different developmental conditions, especially for a non-model organism such as sunflower, will open new insights into the details of gene regulation of leaf senescence. RESULTS: Weighted Gene Correlation Network Analysis (WGCNA) and BioSignature Discoverer (BioSD, Gnosis Data Analysis, Heraklion, Greece) were used to integrate transcriptomic and metabolomic data. WGCNA allowed the detection of 10 metabolites and 13 TFs whereas BioSD allowed the detection of 1 metabolite and 6 TFs as potential biomarkers. The comparative analysis demonstrated that three transcription factors were detected through both methodologies, highlighting them as potentially robust biomarkers associated with leaf senescence in sunflower. CONCLUSIONS: The complementary use of network and BioSignature Discoverer analysis of transcriptomic and metabolomic data provided a useful tool for identifying candidate genes and metabolites which may have a role during the triggering and development of the leaf senescence process. The WGCNA tool allowed us to design and test a hypothetical network in order to infer relationships across selected transcription factor and metabolite candidate biomarkers involved in leaf senescence, whereas BioSignature Discoverer selected transcripts and metabolites which discriminate between different ages of sunflower plants. The methodology presented here would help to elucidate and predict novel networks and potential biomarkers of leaf senescence in sunflower.

Integrating transcriptomic and metabolomic analysis to understand natural leaf senescence in sunflower.

Leaf senescence is a complex process, which has dramatic consequences on crop yield. In sunflower, gap between potential and actual yields reveals the economic impact of senescence. Indeed, sunflower plants are incapable of maintaining their green leaf area over sustained periods. This study characterizes the leaf senescence process in sunflower through a systems biology approach integrating transcriptomic and metabolomic analyses: plants being grown under both glasshouse and field conditions. Our results revealed a correspondence between profile changes detected at the molecular, biochemical and physiological level throughout the progression of leaf senescence measured at different plant developmental stages. Early metabolic changes were detected prior to anthesis and before the onset of the first senescence symptoms, with more pronounced changes observed when physiological and molecular variables were assessed under field conditions. During leaf development, photosynthetic activity and cell growth processes decreased, whereas sucrose, fatty acid, nucleotide and amino acid metabolisms increased. Pathways related to nutrient recycling processes were also up-regulated. Members of the NAC, AP2-EREBP, HB, bZIP and MYB transcription factor families showed high expression levels, and their expression level was highly correlated, suggesting their involvement in sunflower senescence. The results of this study thus contribute to the elucidation of the molecular mechanisms involved in the onset and progression of leaf senescence in sunflower leaves as well as to the identification of candidate genes involved in this process.

Population structure and genetic diversity characterization of a sunflower association mapping population using SSR and SNP markers.

BACKGROUND: Argentina has a long tradition of sunflower breeding, and its germplasm is a valuable genetic resource worldwide. However, knowledge of the genetic constitution and variability levels of the Argentinean germplasm is still scarce, rendering the global map of cultivated sunflower diversity incomplete. In this study, 42 microsatellite loci and 384 single nucleotide polymorphisms (SNPs) were used to characterize the first association mapping population used for quantitative trait loci mapping in sunflower, along with a selection of allied open-pollinated and composite populations from the germplasm bank of the National Institute of Agricultural Technology of Argentina. The ability of different kinds of markers to assess genetic diversity and population structure was also evaluated. RESULTS: The analysis of polymorphism in the set of sunflower accessions studied here showed that both the microsatellites and SNP markers were informative for germplasm characterization, although to different extents. In general, the estimates of genetic variability were moderate. The average genetic diversity, as quantified by the expected heterozygosity, was 0.52 for SSR loci and 0.29 for SNPs. Within SSR markers, those derived from non-coding regions were able to capture higher levels of diversity than EST-SSR. A significant correlation was found between SSR and SNP- based genetic distances among accessions. Bayesian and multivariate methods were used to infer population structure. Evidence for the existence of three different genetic groups was found consistently across data sets (i.e., SSR, SNP and SSR + SNP), with the maintainer/restorer status being the most prevalent characteristic associated with group delimitation. CONCLUSION: The present study constitutes the first report comparing the performance of SSR and SNP markers for population genetics analysis in cultivated sunflower. We show that the SSR and SNP panels examined here, either used separately or in conjunction, allowed consistent estimations of genetic diversity and population structure in sunflower breeding materials. The generated knowledge about the levels of diversity and population structure of sunflower germplasm is an important contribution to this crop breeding and conservation.

Transcriptomic survey of the midgut of Anthonomus grandis (Coleoptera: Curculionidae).

Anthonomus grandis Boheman is a key pest in cotton crops in the New World. Its larval stage develops within the flower bud using it as food and as protection against its predators. This behavior limits the effectiveness of its control using conventional insecticide applications and biocontrol techniques. In spite of its importance, little is known about its genome sequence and, more important, its specific expression in key organs like the midgut. Total mRNA isolated from larval midguts was used for pyrosequencing. Sequence reads were assembled and annotated to generate a unigene data set. In total, 400,000 reads from A. grandis midgut with an average length of 237 bp were assembled and combined into 20,915 contigs. The assembled reads fell into 6,621 genes models. BlastX search using the NCBI-NR database showed that 3,006 unigenes had significant matches to known sequences. Gene Ontology (GO) mapping analysis evidenced that A. grandis is able to transcripts coding for proteins involved in catalytic processing of macromolecules that allows its adaptation to very different feeding source scenarios. Furthermore, transcripts encoding for proteins involved in detoxification mechanisms such as p450 genes, glutathione-S-transferase, and carboxylesterases are also expressed. This is the first report of a transcriptomic study in A. grandis and the largest set of sequence data reported for this species. These data are valuable resources to expand the knowledge of this insect group and could be used in the design of new control strategies based in molecular information.

Comparison of ddRADseq and EUChip60K SNP genotyping systems for population genetics and genomic selection in Eucalyptus dunnii (Maiden).

Eucalyptus dunnii is one of the most important Eucalyptus species for short-fiber pulp production in regions where other species of the genus are affected by poor soil and climatic conditions. In this context, E. dunnii holds promise as a resource to address and adapt to the challenges of climate change. Despite its rapid growth and favorable wood properties for solid wood products, the advancement of its improvement remains in its early stages. In this work, we evaluated the performance of two single nucleotide polymorphism, (SNP), genotyping methods for population genetics analysis and Genomic Selection in E. dunnii. Double digest restriction-site associated DNA sequencing (ddRADseq) was compared with the EUChip60K array in 308 individuals from a provenance-progeny trial. The compared SNP set included 8,011 and 19,008 informative SNPs distributed along the 11 chromosomes, respectively. Although the two datasets differed in the percentage of missing data, genome coverage, minor allele frequency and estimated genetic diversity parameters, they revealed a similar genetic structure, showing two subpopulations with little differentiation between them, and low linkage disequilibrium. GS analyses were performed for eleven traits using Genomic Best Linear Unbiased Prediction (GBLUP) and a conventional pedigree-based model (ABLUP). Regardless of the SNP dataset, the predictive ability (PA) of GBLUP was better than that of ABLUP for six traits (Cellulose content, Total and Ethanolic extractives, Total and Klason lignin content and Syringyl and Guaiacyl lignin monomer ratio). When contrasting the SNP datasets used to estimate PAs, the GBLUP-EUChip60K model gave higher and significant PA values for six traits, meanwhile, the values estimated using ddRADseq gave higher values for three other traits. The PAs correlated positively with narrow sense heritabilities, with the highest correlations shown by the ABLUP and GBLUP-EUChip60K. The two genotyping methods, ddRADseq and EUChip60K, are generally comparable for population genetics and genomic prediction, demonstrating the utility of the former when subjected to rigorous SNP filtering. The results of this study provide a basis for future whole-genome studies using ddRADseq in non-model forest species for which SNP arrays have not yet been developed.

De novo assembly and characterization of leaf transcriptome for the development of functional molecular markers of the extremophile multipurpose tree species Prosopis alba.

BACKGROUND: Prosopis alba (Fabaceae) is an important native tree adapted to arid and semiarid regions of north-western Argentina which is of great value as multipurpose species. Despite its importance, the genomic resources currently available for the entire Prosopis genus are still limited. Here we describe the development of a leaf transcriptome and the identification of new molecular markers that could support functional genetic studies in natural and domesticated populations of this genus. RESULTS: Next generation DNA pyrosequencing technology applied to P. alba transcripts produced a total of 1,103,231 raw reads with an average length of 421 bp. De novo assembling generated a set of 15,814 isotigs and 71,101 non-assembled sequences (singletons) with an average of 991 bp and 288 bp respectively. A total of 39,000 unique singletons were identified after clustering natural and artificial duplicates from pyrosequencing reads.Regarding the non-redundant sequences or unigenes, 22,095 out of 54,814 were successfully annotated with Gene Ontology terms. Moreover, simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs) were searched, resulting in 5,992 and 6,236 markers, respectively, throughout the genome. For the validation of the the predicted SSR markers, a subset of 87 SSRs selected through functional annotation evidence was successfully amplified from six DNA samples of seedlings. From this analysis, 11 of these 87 SSRs were identified as polymorphic. Additionally, another set of 123 nuclear polymorphic SSRs were determined in silico, of which 50% have the probability of being effectively polymorphic. CONCLUSIONS: This study generated a successful global analysis of the P. alba leaf transcriptome after bioinformatic and wet laboratory validations of RNA-Seq data.The limited set of molecular markers currently available will be significantly increased with the thousands of new markers that were identified in this study. This information will strongly contribute to genomics resources for P. alba functional analysis and genetics. Finally, it will also potentially contribute to the development of population-based genome studies in the genera.

Co-Expression Networks in Sunflower: Harnessing the Power of Multi-Study Transcriptomic Public Data to Identify and Categorize Candidate Genes for Fungal Resistance.

Fungal plant diseases are a major threat to food security worldwide. Current efforts to identify and list loci involved in different biological processes are more complicated than originally thought, even when complete genome assemblies are available. Despite numerous experimental and computational efforts to characterize gene functions in plants, about ~40% of protein-coding genes in the model plant Arabidopsis thaliana L. are still not categorized in the Gene Ontology (GO) Biological Process (BP) annotation. In non-model organisms, such as sunflower (Helianthus annuus L.), the number of BP term annotations is far fewer, ~22%. In the current study, we performed gene co-expression network analysis using eight terabytes of public transcriptome datasets and expression-based functional prediction to categorize and identify loci involved in the response to fungal pathogens. We were able to construct a reference gene network of healthy green tissue (GreenGCN) and a gene network of healthy and stressed root tissues (RootGCN). Both networks achieved robust, high-quality scores on the metrics of guilt-by-association and selective constraints versus gene connectivity. We were able to identify eight modules enriched in defense functions, of which two out of the three modules in the RootGCN were also conserved in the GreenGCN, suggesting similar defense-related expression patterns. We identified 16 WRKY genes involved in defense related functions and 65 previously uncharacterized loci now linked to defense response. In addition, we identified and classified 122 loci previously identified within QTLs or near candidate loci reported in GWAS studies of disease resistance in sunflower linked to defense response. All in all, we have implemented a valuable strategy to better describe genes within specific biological processes.

Double Digest Restriction-Site Associated DNA Sequencing (ddRADseq) Technology.

Double digest restriction-site associated DNA sequencing (ddRADseq) technology combines genome reduced representation by digestion with two restriction enzymes and next generation sequencing (NGS) to obtain thousands of markers (SNP, SSR, and InDels) and genotype tens to hundreds of samples simultaneously. In this chapter, we describe a 96-plex derived ddRADseq protocol that can be set up to obtain different depth of coverage per locus and can be exploited to model and non-model plant species.

Transcriptome survey of Patagonian southern beech Nothofagus nervosa (= N. Alpina): assembly, annotation and molecular marker discovery.

BACKGROUND: Nothofagus nervosa is one of the most emblematic native tree species of Patagonian temperate forests. Here, the shotgun RNA-sequencing (RNA-Seq) of the transcriptome of N. nervosa, including de novo assembly, functional annotation, and in silico discovery of potential molecular markers to support population and associations genetic studies, are described. RESULTS: Pyrosequencing of a young leaf cDNA library generated a total of 111,814 high quality reads, with an average length of 447 bp. De novo assembly using Newbler resulted into 3,005 tentative isotigs (including alternative transcripts). The non-assembled sequences (singletons) were clustered with CD-HIT-454 to identify natural and artificial duplicates from pyrosequencing reads, leading to 21,881 unique singletons. 15,497 out of 24,886 non-redundant sequences or unigenes, were successfully annotated against a plant protein database. A substantial number of simple sequence repeat markers (SSRs) were discovered in the assembled and annotated sequences. More than 40% of the SSR sequences were inside ORF sequences. To confirm the validity of these predicted markers, a subset of 73 SSRs selected through functional annotation evidences were successfully amplified from six seedlings DNA samples, being 14 polymorphic. CONCLUSIONS: This paper is the first report that shows a highly precise representation of the mRNAs diversity present in young leaves of a native South American tree, N. nervosa, as well as its in silico deduced putative functionality. The reported Nothofagus transcriptome sequences represent a unique resource for genetic studies and provide a tool to discover genes of interest and genetic markers that will greatly aid questions involving evolution, ecology, and conservation using genetic and genomic approaches in the genus.

Association mapping in sunflower for Sclerotinia Head Rot resistance.

BACKGROUND: Sclerotinia Head Rot (SHR) is one of the most damaging diseases of sunflower in Europe, Argentina, and USA, causing average yield reductions of 10 to 20 %, but leading to total production loss under favorable environmental conditions for the pathogen. Association Mapping (AM) is a promising choice for Quantitative Trait Locus (QTL) mapping, as it detects relationships between phenotypic variation and gene polymorphisms in existing germplasm without development of mapping populations. This article reports the identification of QTL for resistance to SHR based on candidate gene AM. RESULTS: A collection of 94 sunflower inbred lines were tested for SHR under field conditions using assisted inoculation with the fungal pathogen Sclerotinia sclerotiorum. Given that no biological mechanisms or biochemical pathways have been clearly identified for SHR, 43 candidate genes were selected based on previous transcript profiling studies in sunflower and Brassica napus infected with S. sclerotiorum. Associations among SHR incidence and haplotype polymorphisms in 16 candidate genes were tested using Mixed Linear Models (MLM) that account for population structure and kinship relationships. This approach allowed detection of a significant association between the candidate gene HaRIC_B and SHR incidence (P < 0.01), accounting for a SHR incidence reduction of about 20 %. CONCLUSIONS: These results suggest that AM will be useful in dissecting other complex traits in sunflower, thus providing a valuable tool to assist in crop breeding.

Morphological and genetic diversity of maize landraces along an altitudinal gradient in the Southern Andes.

Maize (Zea mays ssp. mays) is a major cereal crop worldwide and is traditionally or commercially cultivated almost all over the Americas. The North-Western Argentina (NWA) region constitutes one of the main diversity hotspots of the Southern Andes, with contrasting landscapes and a large number of landraces. Despite the extensive collections performed by the "Banco Activo de Germoplasma INTA Pergamino, Argentina" (BAP), most of them have not been characterized yet. Here we report the morphological and molecular evaluation of 30 accessions collected from NWA, along an altitudinal gradient between 1120 and 2950 meters above sea level (masl). Assessment of morphological variation in a common garden allowed the discrimination of two groups, which differed mainly in endosperm type and overall plant size. Although the groups retrieved by the molecular analyses were not consistent with morphological clusters, they showed a clear pattern of altitudinal structuring. Affinities among accessions were not in accordance with racial assignments. Overall, our results revealed that there are two maize gene pools co-existing in NWA, probably resulting from various waves of maize introduction in pre-Columbian times as well as from the adoption of modern varieties by local farmers. In conclusion, the NWA maize landraces preserved at the BAP possess high morphological and molecular variability. Our results highlight their potential as a source of diversity for increasing the genetic basis of breeding programs and provide useful information to guide future sampling and conservation efforts.

Genome-Wide Association Studies in Sunflower: Towards Sclerotinia sclerotiorum and Diaporthe/Phomopsis Resistance Breeding.

Diseases caused by necrotrophic fungi, such as the cosmopolitan Sclerotinia sclerotiorum and the Diaporthe/Phomopsis complex, are among the most destructive diseases of sunflower worldwide. The lack of complete resistance combined with the inefficiency of chemical control makes assisted breeding the best strategy for disease control. In this work, we present an integrated genome-wide association (GWA) study investigating the response of a diverse panel of sunflower inbred lines to both pathogens. Phenotypic data for Sclerotinia head rot (SHR) consisted of five disease descriptors (disease incidence, DI; disease severity, DS; area under the disease progress curve for DI, AUDPCI, and DS, AUDPCS; and incubation period, IP). Two disease descriptors (DI and DS) were evaluated for two manifestations of Diaporthe/Phomopsis: Phomopsis stem canker (PSC) and Phomopsis head rot (PHR). In addition, a principal component (PC) analysis was used to derive transformed phenotypes as inputs to a univariate GWA (PC-GWA). Genotypic data comprised a panel of 4269 single nucleotide polymorphisms (SNP), generated via genotyping-by-sequencing. The GWA analysis revealed 24 unique marker-trait associations for SHR, 19 unique marker-trait associations for Diaporthe/Phomopsis diseases, and 7 markers associated with PC1 and PC2. No common markers were found for the response to the two pathogens. Nevertheless, epistatic interactions were identified between markers significantly associated with the response to S. sclerotiorum and Diaporthe/Phomopsis. This suggests that, while the main determinants of resistance may differ for the two pathogens, there could be an underlying common genetic basis. The exploration of regions physically close to the associated markers yielded 364 genes, of which 19 were predicted as putative disease resistance genes. This work presents the first simultaneous evaluation of two manifestations of Diaporthe/Phomopsis in sunflower, and undertakes a comprehensive GWA study by integrating PSC, PHR, and SHR data. The multiple regions identified, and their exploration to identify candidate genes, contribute not only to the understanding of the genetic basis of resistance, but also to the development of tools for assisted breeding.

Comparison of predictive methods and biological validation for qPCR reference genes in sunflower leaf senescence transcript analysis.

The selection and validation of reference genes constitute a key point for gene expression analysis based on qPCR, requiring efficient normalization approaches. In this work, the expression profiles of eight genes were evaluated to identify novel reference genes for transcriptional studies associated to the senescence process in sunflower. Three alternative strategies were applied for the evaluation of gene expression stability in leaves of different ages and exposed to different treatments affecting the senescence process: algorithms implemented in geNorm, BestKeeper software, and the fitting of a statistical linear mixed model (LMModel). The results show that geNorm suggested the use of all combined genes, although identifying α-TUB1 as the most stable expressing gene. BestKeeper revealed α-TUB and ß-TUB as stable genes, scoring ß-TUB as the most stable one. The statistical LMModel identified α-TUB, actin, PEP, and EF-1α as stable genes in this order. The model-based approximation allows not only the estimation of systematic changes in gene expression, but also the identification of sources of random variation through the estimation of variance components, considering the experimental design applied. Validation of α-TUB and EF-1α as reference genes for expression studies of three sunflower senescence associated genes showed that the first one was more stable for the assayed conditions. We conclude that, when biological replicates are available, LMModel allows a more reliable selection under the assayed conditions. This study represents the first analysis of identification and validation of genuine reference genes for use as internal control in qPCR expression studies in sunflower, experimentally validated throughout six different controlled leaf senescence conditions.

Detection of single copy sequences using BAC-FISH and C-PRINS techniques in sunflower chromosomes.

Bacterial artificial chromosome-fluorescence in situ hybridization (BAC-FISH) and cycling-primed in situ labeling (C-PRINS) techniques were evaluated for integration of physical and genetic maps of sunflower (Helianthus annuus L.). Single-site SSR markers were selected from three linkage groups of a high-density sunflower genetic map. This selection was based on previously identified QTL associated to S. sclerotiorum. These markers were used to select BACs contaning single copy sequences for BAC-FISH aplication. Blocking of highly dispersed repetitive sunflower sequences reduced unspecific hybridization, and allowed the detection of specific signals for BACs containing SSR markers HA4222 and HA2600, anchored to LG 16 and LG 10, respectively. Single-site FISH signal detection was optimized by adjusting the relative quantity and quality of unlabelled repetitive sequences present in the blocking DNA. The SSR marker ORS1247 anchored to the LG 17 was detected by C-PRINS, which yielded fluorescence signals that were specific and intense. This progress in localizing single-copy sequences using BAC-FISH and indirect C-PRINS strategies in sunflower will facilitate the integration of genetic and physical maps, allowing the identification of chromosomes containing key genes and/or QTL associated to agronomic important traits in sunflower.