Genetic basis for lentil adaptation to summer cropping in northern temperate environments

Genome-wide transcript expression analysis reveals major chickpea and lentil genes associated with plant branching

The search for elite cultivars with better architecture has been a demand by farmers of the chickpea and lentil crops, which aims to systematize their mechanized planting and harvesting on a large scale. Therefore, the identification of genes associated with the regulation of the branching and architecture of these plants has currently gained great importance. Herein, this work aimed to gain insight into transcriptomic changes of two contrasting chickpea and lentil cultivars in terms of branching pattern (little versus highly branched cultivars). In addition, we aimed to identify candidate genes involved in the regulation of shoot branching that could be used as future targets for molecular breeding. The axillary and apical buds of chickpea cultivars Blanco lechoso and FLIP07-318C, and lentil cultivars Castellana and Campisi, considered as little and highly branched, respectively, were harvested. A total of 1,624 and 2,512 transcripts were identified as differentially expressed among different tissues and contrasting cultivars of chickpea and lentil, respectively. Several gene categories were significantly modulated such as cell cycle, DNA transcription, energy metabolism, hormonal biosynthesis and signaling, proteolysis, and vegetative development between apical and axillary tissues and contrasting cultivars of chickpea and lentil. Based on differential expression and branching-associated biological function, ten chickpea genes and seven lentil genes were considered the main players involved in differentially regulating the plant branching between contrasting cultivars. These collective data putatively revealed the general mechanism and high-effect genes associated with the regulation of branching in chickpea and lentil, which are potential targets for manipulation through genome editing and transgenesis aiming to improve plant architecture.

Development of a knowledge graph framework to ease and empower translational approaches in plant research: a use-case on grain legumes

While the continuing decline in genotyping and sequencing costs has largely benefited plant research, some key species for meeting the challenges of agriculture remain mostly understudied. As a result, heterogeneous datasets for different traits are available for a significant number of these species. As gene structures and functions are to some extent conserved through evolution, comparative genomics can be used to transfer available knowledge from one species to another. However, such a translational research approach is complex due to the multiplicity of data sources and the non-harmonized description of the data. Here, we provide two pipelines, referred to as structural and functional pipelines, to create a framework for a NoSQL graph-database (Neo4j) to integrate and query heterogeneous data from multiple species. We call this framework Orthology-driven knowledge base framework for translational research (Ortho_KB). The structural pipeline builds bridges across species based on orthology. The functional pipeline integrates biological information, including QTL, and RNA-sequencing datasets, and uses the backbone from the structural pipeline to connect orthologs in the database. Queries can be written using the Neo4j Cypher language and can, for instance, lead to identify genes controlling a common trait across species. To explore the possibilities offered by such a framework, we populated Ortho_KB to obtain OrthoLegKB, an instance dedicated to legumes. The proposed model was evaluated by studying the conservation of a flowering-promoting gene. Through a series of queries, we have demonstrated that our knowledge graph base provides an intuitive and powerful platform to support research and development programmes.

Next-Generation-Sequencing-Based Simple Sequence Repeat (SSR) Marker Development and Linkage Mapping in Lentil (Lens culinaris L.)

Simple sequence repeats (SSRs) are highly versatile markers in genetic diversity analysis and plant breeding, making them widely applicable. They hold potential in lentil (Lens culinaris) breeding for genetic diversity analysis, marker-assisted selection (MAS), and linkage mapping. However, the availability and diversity of SSR markers in lentil is limited. We used next-generation sequencing (NGS) technology to develop SSR markers in lentil. NGS allowed us to identify regions of the lentil genome that contained SSRs. Illumina Hiseq-2000 sequencing of the lentil genotype "Karacadağ" resulted in 1,727,734 sequence reads comprising more than 48,390 Mb, and contigs were mined for SSRs, resulting in the identification of a total of 8697 SSR motifs. Among these, dinucleotide repeats were the most abundant (53.38%), followed by trinucleotides (30.38%), hexanucleotides (6.96%), tetranucleotides (6.59%), and pentanucleotides (3.19%). The most frequent repeat in dinucleotides was the TC (21.80%), followed by the GA (17.60%). A total of 2000 primer pairs were designed from these motifs, and 458 SSR markers were validated following their amplified PCR products. A linkage map was constructed using these new SSRs with high linkage disequilibrium (209) and previously known SSRs (11). The highest number of SSR markers (43) was obtained in LG2, while the lowest number of SSR markers (19) was obtained in LG7. The longest linkage group (LG) was LG2 (86.84 cM), whereas the shortest linkage group was LG7 (53.46 cM). The average length between markers ranged from 1.86 cM in LG1 to 2.81 cM in LG7, and the map density was 2.16 cM. The developed SSRs and created linkage map may provide useful information and offer a new library for genetic diversity analyses, linkage mapping studies, and lentil breeding programs.

Focusing the GWAS Lens on days to flower using latent variable phenotypes derived from global multienvironment trials

Adaptation constraints within crop species have resulted in limited genetic diversity in some breeding programs and areas where new crops have been introduced, for example, for lentil (Lens culinaris Medik.) in North America. An improved understanding of the underlying genetics involved in phenology-related traits is valuable knowledge to aid breeders in overcoming limitations associated with unadapted germplasm and expanding their genetic diversity by introducing new, exotic material. We used a large, 18 site-year, multienvironment dataset phenotyped for phenology-related traits across nine locations and over 3 yr along with accompanying latent variable phenotypes derived from a photothermal model and principal component analysis (PCA) of days from sowing to flower (DTF) data for a lentil diversity panel (324 accessions), which has also been genotyped with an exome capture array. Genome-wide association studies (GWAS) on DTF across multiple environments helped confirm associations with known flowering-time genes and identify new quantitative trait loci (QTL), which may contain previously unknown flowering time genes. Additionally, the use of latent variable phenotypes, which can incorporate environmental data such as temperature and photoperiod as both GWAS traits and as covariates, strengthened associations, revealed additional hidden associations, and alluded to potential roles of the associated QTL. Our approach can be replicated with other crop species, and the results from our GWAS serve as a resource for further exploration into the complex nature of phenology-related traits across the major growing environments for cultivated lentil.