A Genome-Wide Prediction and Identification of Intergenic Small RNAs by Comparative Analysis in Mesorhizobium huakuii 7653R

A Germin-Like Protein GLP1 of Legumes Mediates Symbiotic Nodulation by Interacting with an Outer Membrane Protein of Rhizobia

Rhizobia can infect legumes and induce the coordinated expression of symbiosis and defense genes for the establishment of mutualistic symbiosis. Numerous studies have elucidated the molecular interactions between rhizobia and host plants, which are associated with Nod factor, exopolysaccharide, and T3SS effector proteins. However, there have been relatively few reports about how the host plant recognizes the outer membrane proteins (OMPs) of rhizobia to mediate symbiotic nodulation. In our previous work, a gene (Mhopa22) encoding an OMP was identified in Mesorhizobium huakuii 7653R, whose homologous genes are widely distributed in Rhizobiales. In this study, a germin-like protein GLP1 interacting with Mhopa22 was identified in Astragalus sinicus. RNA interference of AsGLP1 resulted in a decrease in nodule number, whereas overexpression of AsGLP1 increased the number of nodules in the hairy roots of A. sinicus. Consistent symbiotic phenotypes were identified in Medicago truncatula with MtGLPx (refer to medtr7g111240.1, the isogeny of AsGLP1) overexpression or Tnt1 mutant (glpx-1) in symbiosis with Sinorhizobium meliloti 1021. The glpx-1 mutant displayed hyperinfection and the formation of more infection threads but a decrease in root nodules. RNA sequencing analysis showed that many differentially expressed genes were involved in hormone signaling and symbiosis. Taken together, AsGLP1 and its homology play an essential role in mediating the early symbiotic process through interacting with the OMPs of rhizobia. IMPORTANCE This study is the first report to characterize a legume host plant protein to sense and interact with an outer membrane protein (OMP) of rhizobia. It can be speculated that GLP1 plays an essential role to mediate early symbiotic process through interacting with OMPs of rhizobia. The results provide deeper understanding and novel insights into the molecular interactive mechanism of a legume symbiosis signaling pathway in recognition with rhizobial OMPs. Our findings may also provide a new perspective to improve the symbiotic compatibility and nodulation of legume.

Identification of sigma factor 54-regulated small non-coding RNAs by employing genome-wide and transcriptome-based methods in rhizobium strains

Rhizobium-legume symbiosis is considered as the major contributor of biological nitrogen fixation. Bacterial small non-coding RNAs are crucial regulators in several cellular adaptation processes that occur due to the changes in metabolism, physiology, or the external environment. Identifying and analysing the conditional specific/sigma factor-54 regulated sRNAs provides a better understanding of sRNA regulation/mechanism in symbiotic association. In the present study, we have identified sigma factor 54-regulated sRNAs from the genome of six rhizobium strains and from the RNA-seq data of free-living and symbiotic conditions of Bradyrhizobium diazoefficiens USDA 110 to identify the novel putative sRNAs that are over expressed during the regulation of nitrogen fixation. A total of 1351 sRNAs were predicted from the genome of six rhizobium strains and 1375 sRNAs were predicted from the transcriptome data of B. diazoefficiens USDA 110. Analysis of target mRNA for these novel sRNAs was inferred to target several nodulation and nitrogen fixation genes including nodC, nodJ, nodY, nodJ, nodM, nodW, nodZ, nifD, nifN, nifQ, fixK, fixL, fdx, nolB, and several cytochrome proteins. In addition, sRNAs of B. diazoefficiens USDA 110 which targeted the regulatory genes of nitrogen fixation were confirmed by wet-lab experiments with semi-quantitative reverse transcription polymerase chain reaction. Predicted target mRNAs were functionally classified based on the COG analysis and GO annotations. The genome-wide and transcriptome-based integrated methods have led to the identification of several sRNAs involved in the nodulation and symbiosis. Further validation of the functional role of these sRNAs can help in exploring the role of sRNAs in nitrogen metabolism during free-living and symbiotic association with legumes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03394-x.

Prediction of Bacterial sRNAs Using Sequence-Derived Features and Machine Learning

Small ribonucleic acid (sRNA) sequences are 50–500 nucleotide long, noncoding RNA (ncRNA) sequences that play an important role in regulating transcription and translation within a bacterial cell. As such, identifying sRNA sequences within an organism’s genome is essential to understand the impact of the RNA molecules on cellular processes. Recently, numerous machine learning models have been applied to predict sRNAs within bacterial genomes. In this study, we considered the sRNA prediction as an imbalanced binary classification problem to distinguish minor positive sRNAs from major negative ones within imbalanced data and then performed a comparative study with six learning algorithms and seven assessment metrics. First, we collected numerical feature groups extracted from known sRNAs previously identified in Salmonella typhimurium LT2 (SLT2) and Escherichia coli K12 (E. coli K12) genomes. Second, as a preliminary study, we characterized the sRNA-size distribution with the conformity test for Benford’s law. Third, we applied six traditional classification algorithms to sRNA features and assessed classification performance with seven metrics, varying positive-to-negative instance ratios, and utilizing stratified 10-fold cross-validation. We revisited important individual features and feature groups and found that classification with combined features perform better than with either an individual feature or a single feature group in terms of Area Under Precision-Recall curve (AUPR). We reconfirmed that AUPR properly measures classification performance on imbalanced data with varying imbalance ratios, which is consistent with previous studies on classification metrics for imbalanced data. Overall, eXtreme Gradient Boosting (XGBoost), even without exploiting optimal hyperparameter values, performed better than the other five algorithms with specific optimal parameter settings. As a future work, we plan to extend XGBoost further to a large amount of published sRNAs in bacterial genomes and compare its classification performance with recent machine learning models’ performance.

Pan-Genome of the Genus Streptomyces and Prioritization of Biosynthetic Gene Clusters With Potential to Produce Antibiotic Compounds

Species of the genus Streptomyces are known for their ability to produce multiple secondary metabolites; their genomes have been extensively explored to discover new bioactive compounds. The richness of genomic data currently available allows filtering for high quality genomes, which in turn permits reliable comparative genomics studies and an improved prediction of biosynthetic gene clusters (BGCs) through genome mining approaches. In this work, we used 121 genome sequences of the genus Streptomyces in a comparative genomics study with the aim of estimating the genomic diversity by protein domains content, sequence similarity of proteins and conservation of Intergenic Regions (IGRs). We also searched for BGCs but prioritizing those with potential antibiotic activity. Our analysis revealed that the pan-genome of the genus Streptomyces is clearly open, with a high quantity of unique gene families across the different species and that the IGRs are rarely conserved. We also described the phylogenetic relationships of the analyzed genomes using multiple markers, obtaining a trustworthy tree whose relationships were further validated by Average Nucleotide Identity (ANI) calculations. Finally, 33 biosynthetic gene clusters were detected to have potential antibiotic activity and a predicted mode of action, which might serve up as a guide to formulation of related experimental studies.

Riboregulation in Nitrogen-Fixing Endosymbiotic Bacteria

Small non-coding RNAs (sRNAs) are ubiquitous components of bacterial adaptive regulatory networks underlying stress responses and chronic intracellular infection of eukaryotic hosts. Thus, sRNA-mediated regulation of gene expression is expected to play a major role in the establishment of mutualistic root nodule endosymbiosis between nitrogen-fixing rhizobia and legume plants. However, knowledge about this level of genetic regulation in this group of plant-interacting bacteria is still rather scarce. Here, we review insights into the rhizobial non-coding transcriptome and sRNA-mediated post-transcriptional regulation of symbiotic relevant traits such as nutrient uptake, cell cycle, quorum sensing, or nodule development. We provide details about the transcriptional control and protein-assisted activity mechanisms of the functionally characterized sRNAs involved in these processes. Finally, we discuss the forthcoming research on riboregulation in legume symbionts.

Two Broad Host Range Rhizobial Strains Isolated From Relict Legumes Have Various Complementary Effects on Symbiotic Parameters of Co-inoculated Plants

Two bacterial strains Ach-343 and Opo-235 were isolated, respectively from nodules of Miocene-Pliocene relict legumes Astragalus chorinensis Bunge and Oxytropis popoviana Peschkova originated from Buryatia (Baikal Lake region, Russia). For identification of these strains the sequencing of 16S rRNA (rrs) gene was used. Strain Opo-235 belonged to the species Mesorhizobium japonicum, while the strain Ach-343 was identified as M. kowhaii (100 and 99.9% rrs similarity with the type strains MAFF 303099T and ICMP 19512T, respectively). Symbiotic genes of these strains as well as some genes that promote plant growth (acdS, gibberellin- and auxin-synthesis related genes) were searched throughout the whole genome sequences. The sets of plant growth-promoting genes found were almost identical in both strains, whereas the sets of symbiotic genes were different and complemented each other with several nod, nif, and fix genes. Effects of mono- and co-inoculation of Astragalus sericeocanus, Oxytropis caespitosa, Glycyrrhiza uralensis, Medicago sativa, and Trifolium pratense plants with the strains M. kowhaii Ach-343 and M. japonicum Opo-235 expressing fluorescent proteins mCherry (red) and EGFP (green) were studied in the gnotobiotic plant nodulation assay. It was shown that both strains had a wide range of host specificity, including species of different legume genera from two tribes (Galegeae and Trifolieae). The effects of co-microsymbionts on plants depended on the plant species and varied from decrease, no effect, to increase in the number of nodules, nitrogen-fixing activity and plant biomass. One of the reasons for this phenomenon may be the discovered complementarity in co-microsymbionts of symbiotic genes responsible for the specific modification of Nod-factors and nitrogenase activity. Localization and co-localization of the strains in nodules was confirmed by the confocal microscopy. Analysis of histological and ultrastructural organization of A. chorinensis and O. popoviana root nodules was performed. It can be concluded that the strains M. kowhaii Ach-343 and M. japonicum Opo-235 demonstrate lack of high symbiotic specificity that is characteristic for primitive legume-rhizobia systems. Further study of the root nodule bacteria having complementary sets of symbiotic genes will contribute to clarify the evolutionary paths of legume-rhizobia relationships and the mechanisms of effective integration between partners.