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Nonsymbiotic hemoglobins (nsHbs) and leghemoglobins (Lbs) are plant proteins that can reversibly bind O(2) and other ligands. The nsHbs are hexacoordinate and appear to modulate cellular concentrations of NO and maintain energy levels under hypoxic conditions. The Lbs are pentacoordinate and facilitate the diffusion of O(2) to symbiotic bacteroids within legume root nodules. Multiple lines of evidence suggest that all plant Hbs evolved from a common ancestor and that Lbs originated from nsHbs. However, little is known about the structural intermediates that occurred during the evolution of pentacoordinate Lbs from hexacoordinate nsHbs. We have cloned and characterized a Hb (ppHb) from the root nodules of the ancient caesalpinoid legume Chamaecrista fasciculata. Protein sequence, modeling data, and spectral analysis indicated that the properties of ppHb are intermediate between that of nsHb and Lb, suggesting that ppHb resembles a putative ancestral Lb. Predicted structural changes that appear to have occurred during the nsHb to Lb transition were a compaction of the CD-loop and decreased mobility of the distal His inhibiting its ability to coordinate directly with the heme-Fe, leading to a pentacoordinate protein. Other predicted changes include shortening of the N- and C-termini, compaction of the protein into a globular structure, disappearance of positive charges outside the heme pocket and appearance of negative charges in an area located between the N- and C-termini. A major consequence for some of these changes appears to be the decrease in O(2)-affinity of ancestral nsHb, which resulted in the origin of the symbiotic function of Lbs.

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Agricultural application of plant-beneficial bacteria to improve crop yield and alleviate the stress caused by environmental conditions, pests, and pathogens is gaining popularity. However, before using these bacterial strains in plant experiments, their environmental stress responses and plant health improvement potential should be examined. In this study, we explored the applicability of three unsupervised machine learning-based data integration methods, including principal component analysis (PCA) of concatenated data, multiple co-inertia analysis (MCIA), and multiple kernel learning (MKL), to select osmotic stress-tolerant plant growth-promoting (PGP) bacterial strains isolated from the rice phyllosphere. The studied datasets consisted of direct and indirect PGP activity measurements and osmotic stress responses of eight bacterial strains previously isolated from the phyllosphere of drought-tolerant rice cultivar. The production of phytohormones, such as indole-acetic acid (IAA), gibberellic acid (GA), abscisic acid (ABA), and cytokinin, were used as direct PGP traits, whereas the production of hydrogen cyanide and siderophore and antagonistic activity against the foliar pathogens Pyricularia oryzae and Helminthosporium oryzae were evaluated as measures of indirect PGP activity. The strains were subjected to a range of osmotic stress levels by adding PEG 6000 (0, 11, 21, and 32.6%) to their growth medium. The results of the osmotic stress response experiments showed that all bacterial strains accumulated endogenous proline and glycine betaine (GB) and exhibited an increase in growth, when osmotic stress levels were increased to a specific degree, while the production of IAA and GA considerably decreased. The three applied data integration methods did not provide a similar grouping of the strains. Especially deviant was the ordination of microbial strains based on the PCA of concatenated data. However, all three data integration methods indicated that the strains Bacillus altitudinis PB46 and B. megaterium PB50 shared high similarity in PGP traits and osmotic stress response. Overall, our results indicate that data integration methods complement the single-table data analysis approach and improve the selection process for PGP microbial strains.

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