SummaryLegumes played central roles in the development of agriculture and civilization, and today account for approximately one-third of the world's primary crop production. Unfortunately, most cultivated legumes are poor model systems for genomic research. Therefore, Medicago truncatula, which has a relatively small diploid genome, has been adopted as a model species for legume genomics. To enhance its value as a model, we have generated a gene expression atlas that provides a global view of gene expression in all major organ systems of this species, with special emphasis on nodule and seed development. The atlas reveals massive differences in gene expression between organs that are accompanied by changes in the expression of key regulatory genes, such as transcription factor genes, which presumably orchestrate genetic reprogramming during development and differentiation. Interestingly, many legume-specific genes are preferentially expressed in nitrogen-fixing nodules, indicating that evolution endowed them with special roles in this unique and important organ. Comparative transcriptome analysis of Medicago versus Arabidopsis revealed significant divergence in developmental expression profiles of orthologous genes, which indicates that phylogenetic analysis alone is insufficient to predict the function of orthologs in different species. The data presented here represent an unparalleled resource for legume functional genomics, which will accelerate discoveries in legume biology.

Background: Plants encode a large number of leucine-rich repeat receptor-like kinases. Legumes encode several LRR-RLK linked to the process of root nodule formation, the ligands of which are unknown. To identify ligands for these receptors, we used a combination of profile hidden Markov models and position-specific iterative BLAST, allowing us to detect new members of the CLV3/ESR (CLE) protein family from publicly available sequence databases.

Strigolactones are a group of plant compounds of diverse but related chemical structures. They have similar bioactivity across a broad range of plant species, act to optimize plant growth and development, and promote soil microbe interactions. Carlactone, a common precursor to strigolactones, is produced by conserved enzymes found in a number of diverse species. Versions of the MORE AXILLARY GROWTH1 (MAX1) cytochrome P450 from rice and Arabidopsis thaliana make specific subsets of strigolactones from carlactone. However, the diversity of natural strigolactones suggests that additional enzymes are involved and remain to be discovered. Here, we use an innovative method that has revealed a missing enzyme involved in strigolactone metabolism. By using a transcriptomics approach involving a range of treatments that modify strigolactone biosynthesis gene expression coupled with reverse genetics, we identified LATERAL BRANCHING OXIDOREDUCTASE (LBO), a gene encoding an oxidoreductase-like enzyme of the 2-oxoglutarate and Fe(II)-dependent dioxygenase superfamily. Arabidopsis lbo mutants exhibited increased shoot branching, but the lbo mutation did not enhance the max mutant phenotype. Grafting indicated that LBO is required for a graft-transmissible signal that, in turn, requires a product of MAX1. Mutant lbo backgrounds showed reduced responses to carlactone, the substrate of MAX1, and methyl carlactonoate (MeCLA), a product downstream of MAX1. Furthermore, lbo mutants contained increased amounts of these compounds, and the LBO protein specifically converts MeCLA to an unidentified strigolactone-like compound. Thus, LBO function may be important in the later steps of strigolactone biosynthesis to inhibit shoot branching in Arabidopsis and other seed plants.plant | branching | strigolactone | biosynthesis | Arabidopsis

Phylogenetic profiles constitute a novel way of graphically displaying the coherence of the sequence relationships over the entire length of a set of aligned homologous sequences. Using a sliding-window technique, this method determines the pairwise distances of all sequences in the windows and evaluates, for each sequence, the degree to which the patterns of distances in these regions agree. This method is suited for exploring data consistency as well as detecting recombinant sequences. A computer program implementing the algorithm has been developed, and examples with simulated and natural sequences are given to demonstrate the sensitivity and accuracy of the method for identifying recombinant sequences and their recombination junctions as well as detecting hot spots of recombinational activity.