Unlimited Thirst for Genome Sequencing, Data Interpretation, and Database Usage in Genomic Era: The Road towards Fast-Track Crop Plant Improvement

SSR markers and seed quality traits revealed genetic diversity in durum wheat (Triticum durum Desf.)

Genetic diversity and differences among durum-wheat cultivars evolved in various regions of the world are important for sustainable production in the current climate change scenario. Information regarding genetic differences was also important for the correct choice of parental material for the selection of high quality cultivars. Two elite and six obsolete cultivars of durum-wheat were characterized with 25-simple sequence repeats (SSR) markers. All accessions were evaluated for 2-agronomic-traits (Yield (Y) and Thousand-Kernel-Weight (TKW)) and 11 grain quality-traits (grain protein content (GPC), grain moisture contents (H), carotene content (CT), sedimentation test (SDS), gluten content (GC), gluten index (GI), semolina color index (L*, a*, b*) and alveographic parameters (W and P/L)) under randomized complete block design with three replication for two crop seasons (2015-2017). Genetic characterization through SSR markers revealed 126 alleles with an average of 5.04 alleles locus-1 and had average 0.79 polymorphism information content (PIC). The comparisons revealed that elite accessions were more productive in terms of grain yield and TKW, whereas obsolete accessions showed high GPC and end-use quality-traits. The generated dendrogram based on SSR markers, agronomic, seed quality-traits clearly differentiate the genotypes in two main groups obsolete and elite accessions. Analysis of correlation revealed a significant association between the traits TKW, Y, b*, a*, GPC, GC, SDS and H. High genetic diversity found between elite and obsolete cultivars for parameters such as yield, end-use quality and their correlation with SSR markers could help breeders for an eventual breeding program on durum-wheat.

Assessment of genetic diversity of pumpkins (Cucurbita spp.) from western Kenya using SSR molecular markers

Pumpkins (Cucurbita spp.) are among most neglected and underutilized crops cultivated for food and medicine. The major constraint to pumpkin production is lack of genetically improved seeds. The current study was aimed at evaluating the genetic diversity of pumpkins from eight counties in western Kenya using five SSR markers. Seeds were extracted from pumpkin fruits, dried and planted on plastic trays for 4 weeks. DNA was isolated from young leaves using CTAB method and amplified. The samples were genotyped using an ABI 3730 genetic analyzer and the allelic data analyzed using Power Marker V 3.25, DARwin V 6.0.12 and GenAIEx V 6.41software. The five SSR loci were polymorphic with a total of 33 alleles and a mean PIC value of 0.534. The gene diversity and observed heterozygosity was 0.796-0.329 and 0.967-0.164, respectively. Most of genetic variations were found within and among individual samples rather than among counties, with samples of some counties having private alleles. Based on the inbreeding coefficient (F), there was outbreeding in pumpkins from Kakamega county (F = - 0.282) and inbreeding in pumpkins from Kisii, Bungoma and Nyamira counties (F = 0.500, 0.409 and 0.286 respectively). The findings of this study suggest that genetic variation and distribution of pumpkins in western Kenya was due to monocropping and intercropping farming systems, trading of pumpkins in markets and exchange of seeds among local farmers rather than geographical and climatic differences.

Marker-assisted selection strategy to pyramid two or more QTLs for quantitative trait-grain yield under drought

BACKGROUND

Marker-assisted breeding will move forward from introgressing single/multiple genes governing a single trait to multiple genes governing multiple traits to combat emerging biotic and abiotic stresses related to climate change and to enhance rice productivity. MAS will need to address concerns about the population size needed to introgress together more than two genes/QTLs. In the present study, grain yield and genotypic data from different generations (F3 to F8) for five marker-assisted breeding programs were analyzed to understand the effectiveness of synergistic effect of phenotyping and genotyping in early generations on selection of better progenies.

RESULTS

Based on class analysis of the QTL combinations, the identified superior QTL classes in F3/BC1F3/BC2F3 generations with positive QTL x QTL and QTL x background interactions that were captured through phenotyping maintained its superiority in yield under non-stress (NS) and reproductive-stage drought stress (RS) across advanced generations in all five studies. The marker-assisted selection breeding strategy combining both genotyping and phenotyping in early generation significantly reduced the number of genotypes to be carried forward. The strategy presented in this study providing genotyping and phenotyping cost savings of 25-68% compared with the traditional marker-assisted selection approach. The QTL classes, Sub1 + qDTY 1.1  + qDTY 2.1  + qDTY 3.1 and Sub1 + qDTY 2.1  + qDTY 3.1 in Swarna-Sub1, Sub1 + qDTY 1.1  + qDTY 1.2 , Sub1 + qDTY 1.1  + qDTY 2.2 and Sub1 + qDTY 2.2  + qDTY 12.1 in IR64-Sub1, qDTY 2.2  + qDTY 4.1 in Samba Mahsuri, Sub1 + qDTY 3.1  + qDTY 6.1  + qDTY 6.2 and Sub1 + qDTY 6.1  + qDTY 6.2 in TDK1-Sub1 and qDTY 12.1  + qDTY 3.1 and qDTY 2.2  + qDTY 3.1 in MR219 had shown better and consistent performance under NS and RS across generations over other QTL classes.

CONCLUSION

"Deployment of this procedure will save time and resources and will allow breeders to focus and advance only germplasm with high probability of improved performance. The identification of superior QTL classes and capture of positive QTL x QTL and QTL x background interactions in early generation and their consistent performance in subsequent generations across five backgrounds supports the efficacy of a combined MAS breeding strategy".

Growing and cultivating the forest genomics database, TreeGenes

Forest trees are valued sources of pulp, timber and biofuels, and serve a role in carbon sequestration, biodiversity maintenance and watershed stability. Examining the relationships among genetic, phenotypic and environmental factors for these species provides insight on the areas of concern for breeders and researchers alike. The TreeGenes database is a web-based repository that is home to 1790 tree species and over 1500 registered users. The database provides a curated archive for high-throughput genomics, including reference genomes, transcriptomes, genetic maps and variant data. These resources are paired with extensive phenotypic information and environmental layers. TreeGenes recently migrated to Tripal, an integrated and open-source database schema and content management system. This migration enabled developments focused on data exchange, data transfer and improved analytical capacity, as well as providing TreeGenes the opportunity to communicate with the following partner databases: Hardwood Genomics Web, Genome Database for Rosaceae, and the Citrus Genome Database. Recent development in TreeGenes has focused on coordinating information for georeferenced accessions, including metadata acquisition and ontological frameworks, to improve integration across studies combining genetic, phenotypic and environmental data. This focus was paired with the development of tools to enable comparative genomics and data visualization. By combining advanced data importers, relevant metadata standards and integrated analytical frameworks, TreeGenes provides a platform for researchers to store, submit and analyze forest tree data.

Plant Promoter Analysis: Identification and Characterization of Root Nodule Specific Promoter in the Common Bean

The upstream sequences of gene coding sequences are termed as promoter sequences. Studying the expression patterns of promoters are very significant in understanding the gene regulation and spatiotemporal expression patterns of target genes. On the other hand, it is also critical to establish promoter evaluation tools and genetic transformation techniques that are fast, efficient, and reproducible. In this study, we investigated the spatiotemporal expression pattern of the rhizobial symbiosis-specific nodule inception (NIN) promoter of Phaseolus vulgaris in the transgenic hairy roots. Using plant genome databases and analysis tools we identified, isolated, and cloned the P. vulgaris NIN promoter in a transcriptional fusion to the chimeric reporter β-glucuronidase (GUS) GUS-enhanced::GFP. Further, this protocol describes a rapid and versatile system of genetic transformation in the P. vulgaris using Agrobacterium rhizogenes induced hairy roots. This system generates ≥2 cm hairy roots at 10 to 12 days after transformation. Next, we assessed the spatiotemporal expression of NIN promoter in Rhizobium inoculated hairy roots at periodic intervals of post-inoculation. Our results depicted by GUS activity show that the NIN promoter was active during the process of nodulation. Together, the present protocol demonstrates how to identify, isolate, clone, and characterize a plant promoter in the common bean hairy roots. Moreover, this protocol is easy to use in non-specialized laboratories.

Computational Tools for Genomic Studies in Plants

In recent years, the genomic sequence of numerous plant species including the main crop species has been determined. Computational tools have been developed to deal with the issue of which plant has been sequenced and where is the sequence hosted. In this mini-review, the databases for genome projects, the databases created to host species/clade projects and the databases developed to perform plant comparative genomics are revised. Because of their importance in modern research, an in-depth analysis of the plant comparative genomics databases has been performed. This comparative analysis is focused in the common and specific computational tools developed to achieve the particular objectives of each database. Besides, emerging high-performance bioinformatics tools specific for plant research are commented. What kind of computational approaches should be implemented in next years to efficiently analyze plant genomes is discussed.

Bioinformatics Approach in Plant Genomic Research

The advance in genomics technology leads to the dramatic change in plant biology research. Plant biologists now easily access to enormous genomic data to deeply study plant high-density genetic variation at molecular level. Therefore, fully understanding and well manipulating bioinformatics tools to manage and analyze these data are essential in current plant genome research. Many plant genome databases have been established and continued expanding recently. Meanwhile, analytical methods based on bioinformatics are also well developed in many aspects of plant genomic research including comparative genomic analysis, phylogenomics and evolutionary analysis, and genome-wide association study. However, constantly upgrading in computational infrastructures, such as high capacity data storage and high performing analysis software, is the real challenge for plant genome research. This review paper focuses on challenges and opportunities which knowledge and skills in bioinformatics can bring to plant scientists in present plant genomics era as well as future aspects in critical need for effective tools to facilitate the translation of knowledge from new sequencing data to enhancement of plant productivity.