Application of data integration for rice bacterial strain selection by combining their osmotic stress response and plant growth-promoting traits.

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.

Use of in silico (computer) methods to predict and analyze the tertiary structure of plant hemoglobins.

Amino acid sequences for more than 60 plant hemoglobins (Hbs) are deposited in databases, but the tertiary structure of only 4 plant Hbs have been reported; thus, the gap between the reported sequences and structures of plant Hbs is large. Elucidating the structure of plant Hbs is essential to fully understanding the function of these proteins in plant cells. Determining the actual protein structure by experimental methods (i.e., by X-ray crystallography) requires considerable protein material and is expensive; thus, this type of work is limited to few laboratories around the world. In silico (computer) methods to predict the tertiary structure of proteins from amino acid sequences have been implemented and are helping reduce the sequence-structure gap. Thus, in silico methods are useful tools for predicting the tertiary structure of several plant Hbs from amino acid sequences deposited in databases. In this chapter, we describe a method for predicting and analyzing the structure of a rice Hb2 from the template structure of native rice Hb1. This method is based on a comparative modeling method that uses programs from the SWISS-MODEL server.

Plant hemoglobins: what we know six decades after their discovery.

This review describes contributions to the study of plant hemoglobins (Hbs) from a historical perspective with emphasis on non-symbiotic Hbs (nsHbs). Plant Hbs were first identified in soybean root nodules, are known as leghemoglobins (Lbs) and have been characterized in detail. It is widely accepted that a function of Lbs in nodules is to facilitate the diffusion of O(2) to bacteroids. For many years Hbs could not be identified in plants other than N(2)-fixing legumes, however in the 1980s a Hb was isolated from the nodules of the non-legume dicot plant Parasponia, a hb gene was cloned from the non-nodulating Trema, and Hbs were detected in nodules of actinorhizal plants. Gene expression analysis showed that Trema Hb transcripts exist in non-symbiotic roots. In the 1990s nsHb sequences were also identified in monocot and primitive (bryophyte) plants. In addition to Lbs and nsHbs, Hb sequences that are similar to microbial truncated (2/2) Hbs were also detected in plants. Plant nsHbs have been characterized in detail. These proteins have very high O(2)-affinities because of an extremely low O(2)-dissociation constant. Analysis of rice Hb1 showed that distal His coordinates heme Fe and stabilizes bound O(2); this means that O(2) is not released easily from oxygenated nsHbs. Non-symbiotic hb genes are expressed in specific plant tissues, and overexpress in organs of stressed plants. These observations suggest that nsHbs have functions additional to O(2)-transport, such as to modulate levels of ATP and NO.