In this study, we provide a comparative genomic analysis of Pantoea agglomerans strain P5 and 10 closely related strains based on phylogenetic analyses. A next-generation shotgun strategy was implemented using the Illumina HiSeq 2500 technology followed by core- and pan-genome analysis. The genome of P. agglomerans strain P5 contains an assembly size of 5082485 bp with 55.4% G + C content. P. agglomerans consists of 2981 core and 3159 accessory genes for Coding DNA Sequences (CDSs) based on the pan-genome analysis. Strain P5 can be grouped closely with strains PG734 and 299 R using pan and core genes, respectively. All the predicted and annotated gene sequences were allocated to KEGG pathways. Accordingly, genes involved in plant growth-promoting (PGP) ability, including phosphate solubilization, IAA and siderophore production, acetoin and 2,3-butanediol synthesis and bacterial secretion, were assigned. This study provides an in-depth view of the PGP characteristics of strain P5, highlighting its potential use in agriculture as a biofertilizer.

Tillering, or the production of lateral branches (i.e., culms), is an important agronomic trait that determines shoot architecture and grain production in grasses. Shoot architecture is based on the actions of the apical and axillary meristems (AXMs). The shoot apical meristem (SAM) produces all aboveground organs, including AXMs, leaves, stems, and inflorescences. In grasses like rice ( Oryza sativa L.) and barley ( Hordeum vulgare L.), vegetative AXMs form in the leaf axil of lower leaves of the plant and produce tillers (branches). Tiller development is characterized by three stages, including (i) AXM initiation, (ii) bud development, and (iii) outgrowth of the axillary bud into a tiller. Each tiller has the potential to produce a seed‐bearing inflorescence and, hence, increase yield. However, a balance between number and vigor of tillers is required, as unproductive tillers consume nutrients and can lead to a decreased grain production. Because of its agronomic and biological importance, tillering has been widely studied, and numerous works demonstrate that the control of AXM initiation, bud development, and tillering in the grasses is via a suite of genes, hormones, and environmental conditions. In this review, we describe the genes and hormones that control tillering in two key cereal crops, rice and barley. In addition, we discuss how the development of new genomics tools and approaches, coupled with the synteny between the rice and barley genomes, are accelerating the isolation of barley genes underlying tillering phenotypes.

Tillers are vegetative branches that develop from axillary buds located in the leaf axils at the base of many grasses. Genetic manipulation of tillering is a major objective in breeding for improved cereal yields and competition with weeds. Despite this, very little is known about the molecular genetic bases of tiller development in important Triticeae crops such as barley (Hordeum vulgare) and wheat (Triticum aestivum). Recessive mutations at the barley Uniculme4 (Cul4) locus cause reduced tillering, deregulation of the number of axillary buds in an axil, and alterations in leaf proximal-distal patterning. We isolated the Cul4 gene by positional cloning and showed that it encodes a BROAD-COMPLEX, TRAMTRACK, BRIC-À-BRAC-ankyrin protein closely related to Arabidopsis (Arabidopsis thaliana) BLADE-ON-PETIOLE1 (BOP1) and BOP2. Morphological, histological, and in situ RNA expression analyses indicate that Cul4 acts at axil and leaf boundary regions to control axillary bud differentiation as well as the development of the ligule, which separates the distal blade and proximal sheath of the leaf. As, to our knowledge, the first functionally characterized BOP gene in monocots, Cul4 suggests the partial conservation of BOP gene function between dicots and monocots, while phylogenetic analyses highlight distinct evolutionary patterns in the two lineages.

Photoperiod-H1 controls leaf size by influencing the duration of leaf growth in barley.

Meta-QTL (MQTL) analysis is a robust approach for genetic dissection of complex quantitative traits. Rice varieties adapted to non-flooded cultivation are highly desirable in breeding programs due to the water deficit global problem. In order to identify stable QTLs for major agronomic traits under water deficit conditions, we performed a comprehensive MQTL analysis on 563 QTLs from 67 rice populations published from 2001 to 2019. Yield and yield-related traits including grain weight, heading date, plant height, tiller number as well as root architecture-related traits including root dry weight, root length, root number, root thickness, the ratio of deep rooting and plant water content under water deficit condition were investigated. A total of 61 stable MQTLs over different genetic backgrounds and environments were identified. The average confidence interval of MQTLs was considerably refined compared to the initial QTLs, resulted in the identification of some well-known functionally characterized genes and several putative novel CGs for investigated traits. Ortho-MQTL mining based on genomic collinearity between rice and maize allowed identification of five ortho-MQTLs between these two cereals. The results can help breeders to improve yield under water deficit conditions.

Background: Improving yield and yield-related traits is the crucial goal in breeding programmes of cereals. Meta-QTL (MQTL) analysis discovers the most stable QTLs regardless of populations genetic background and field trial conditions and effectively narrows down the confidence interval (CI) for identification of candidate genes (CG) and markers development.Results: A comprehensive MQTL analysis was implemented on 1052 QTLs reported for yield (YLD), grain weight (GW), heading date (HD), plant height (PH) and tiller number (TN) in 122 rice populations evaluated under normal condition from 1996 to 2019. Consequently, these QTLs were confined into 114 MQTLs and the average CI was reduced up to 3.5 folds in compare to the mean CI of the original QTLs with an average of 4.85 cM CI in the resulted MQTLs. Among them, 27 MQTLs with at least five initial QTLs from independent studies were considered as the most stable QTLs over different field trials and genetic backgrounds. Furthermore, several known and novel CGs were detected in the high confident MQTLs intervals. The genomic distribution of MQTLs indicated the highest density at subtelomeric chromosomal regions. Using the advantage of synteny and comparative genomics analysis, 11 and 15 ortho-MQTLs were identified at co-linear regions between rice with barley and maize, respectively. In addition, comparing resulted MQTLs with GWAS studies led to identification of eighteen common significant chromosomal regions controlling the evaluated traits.Conclusion: This comprehensive analysis defines a genome wide landscape on the most stable loci associated with reliable genetic markers and CGs for yield and yield-related traits in rice. Our findings showed that some of these information are transferable to other cereals that lead to improvement of their breeding programs.