Genome-wide identification and characterization of NBLRR genes in finger millet (Eleusine coracana L.) and their expression in response to Magnaporthe grisea infection.

BACKGROUND: The nucleotide binding site leucine rich repeat (NBLRR) genes significantly regulate defences against phytopathogens in plants. The genome-wide identification and analysis of NBLRR genes have been performed in several species. However, the detailed evolution, structure, expression of NBLRRs and functional response to Magnaporthe grisea are unknown in finger millet (Eleusine coracana (L.) Gaertn.). RESULTS: The genome-wide scanning of the finger millet genome resulted in 116 NBLRR (EcNBLRRs1-116) encompassing 64 CC-NB-LRR, 47 NB-LRR and 5 CCR-NB-LRR types. The evolutionary studies among the NBLRRs of five Gramineae species, viz., purple false brome (Brachypodium distachyon (L.) P.Beauv.), finger millet (E. coracana), rice (Oryza sativa L.), sorghum (Sorghum bicolor L. (Moench)) and foxtail millet (Setaria italica (L.) P.Beauv.) showed the evolution of NBLRRs in the ancestral lineage of the target species and subsequent divergence through gene-loss events. The purifying selection (Ka/Ks < 1) shaped the expansions of NBLRRs paralogs in finger millet and orthologs among the target Gramineae species. The promoter sequence analysis showed various stress- and phytohormone-responsive cis-acting elements besides growth and development, indicating their potential role in disease defence and regulatory mechanisms. The expression analysis of 22 EcNBLRRs in the genotypes showing contrasting responses to Magnaporthe grisea infection revealed four and five EcNBLRRs in early and late infection stages, respectively. The six of these nine candidate EcNBLRRs proteins, viz., EcNBLRR21, EcNBLRR26, EcNBLRR30, EcNBLRR45, EcNBLRR55 and EcNBLRR76 showed CC, NB and LRR domains, whereas the EcNBLRR23, EcNBLRR32 and EcNBLRR83 showed NB and LRR somains. CONCLUSION: The identification and expression analysis of EcNBLRRs showed the role of EcNBLRR genes in assigning blast resistance in finger millet. These results pave the foundation for in-depth and targeted functional analysis of EcNBLRRs through genome editing and transgenic approaches.

Fine-scale mapping of the microbiome on phylloplane and spermoplane of aromatic and non-aromatic rice genotypes.

Adaxial, abaxial phylloplane (leaf), and spermoplane (seed) are proximal yet contrasting habitats for a microbiota that needs to be adequately explored. Here, we proposed novel methods to decipher the adaxial/abaxial-phylloplane and spermoplane-microbiomes. Comparison of 22 meta barcoded-NGS datasets (size of total data set-1980.48 Mb) enabled us to fine-map the microbiome of the rice foliar niche, which encompasses the lower, middle, top leaf as well panicle. Here, the total- and the cultivable-microbiome profiling revealed 157 genera representing ten phyla and 87 genera from 4 bacterial phyla, respectively, with a predominance of Proteobacteria and Actinobacteria. Interestingly, more bacterial communities (124-genera) preferred the abaxial than the adaxial phylloplane (104-genera) and spermoplane (67-genera) for colonization. The microbiome profiles were nearly identical on the aromatic (125-genera) and non-aromatic rice (116-genera) with high representation of Pantoea, Methylobacterium, Curtobacterium, Sphingopyxis, and Microbacterium. The culturomics investigation confirmed the abundance of Pantoea, Chryseobacterium, Pseudomonas, Acinetobacter, Sphingobacterium, and Exiguobacterium. One hundred bacterial isolates characterized and identified by polyphasic-taxonomic tools revealed the dominance of Acinetobacter, Chryseobacterium, Enterobacter, Massilia, Pantoea, Pseudomonas, and Stenotrophomonas on adaxial/abaxial-phylloplane and spermoplane. The study culminated in identifying hitherto unexplored bacterial communities on the adaxial/abaxial phylloplane and spermoplane of rice that can be harnessed for microbiome-assisted rice cultivation in the future.

Spectral characterization and severity assessment of rice blast disease using univariate and multivariate models.

Rice is the staple food of more than half of the population of the world and India as well. One of the major constraints in rice production is frequent occurrence of pests and diseases and one of them is rice blast which often causes yield loss varying from 10 to 30%. Conventional approaches for disease assessment are time-consuming, expensive, and not real-time; alternately, sensor-based approach is rapid, non-invasive and can be scaled up in large areas with minimum time and effort.  In the present study, hyperspectral remote sensing for the characterization and severity assessment of rice blast disease was exploited. Field experiments were conducted with 20 genotypes of rice having sensitive and resistant cultivars grown under upland and lowland conditions at Almora, Uttarakhand, India. The severity of the rice blast was graded from 0 to 9 in accordance to International Rice Research Institute (IRRI).  Spectral observations in field were taken using a hand-held portable spectroradiometer in range of 350-2500 nm followed by spectral discrimination of different disease severity levels using Jeffires-Matusita (J-M) distance. Then, evaluation of 26 existing spectral indices (r≥0.8) was done corresponding to blast severity levels and linear regression prediction models were also developed. Further, the proposed ratio blast index (RBI) and normalized difference blast index (NDBI) were developed using all possible combinations of their correlations with severity level followed by their quantification to identify the best indices. Thereafter, multivariate models like support vector machine regression (SVM), partial least squares (PLS), random forest (RF), and multivariate adaptive regression spline (MARS) were also used to estimate blast severity. Jeffires-Matusita distance was separating almost all severity levels having values >1.92 except levels 4 and 5. The 26 prediction models were effective at predicting blast severity with R2 values from 0.48 to 0.85. The best developed spectral indices for rice blast were RBI (R1148, R1301) and NDBI (R1148, R1301) with R2 of 0.85 and 0.86, respectively. Among multivariate models, SVM was the best model with calibration R2=0.99; validation R2=0.94, RMSE=0.7, and RPD=4.10. The methodology developed paves way for early detection and large-scale monitoring and mapping using satellite remote sensors at farmers' fields for developing better disease management options.

Mechanisms of Microbial Plant Protection and Control of Plant Viruses.

Plant viral diseases are major constraints causing significant yield losses worldwide in agricultural and horticultural crops. The commonly used methods cannot eliminate viral load in infected plants. Many unconventional methods are presently being employed to prevent viral infection; however, every time, these methods are not found promising. As a result, it is critical to identify the most promising and sustainable management strategies for economically important plant viral diseases. The genetic makeup of 90 percent of viral diseases constitutes a single-stranded RNA; the most promising way for management of any RNA viruses is through use ribonucleases. The scope of involving beneficial microbial organisms in the integrated management of viral diseases is of the utmost importance and is highly imperative. This review highlights the importance of prokaryotic plant growth-promoting rhizobacteria/endophytic bacteria, actinomycetes, and fungal organisms, as well as their possible mechanisms for suppressing viral infection in plants via cross-protection, ISR, and the accumulation of defensive enzymes, phenolic compounds, lipopeptides, protease, and RNase activity against plant virus infection.

Exploring the Impact of Climatic Variables on Arecanut Fruit Rot Epidemic by Understanding the Disease Dynamics in Relation to Space and Time.

To understand the spatio-temporal dynamics and the effect of climate on fruit rot occurrence in arecanut plantations, we evaluated the intensity of fruit rot in three major growing regions of Karnataka, India for two consecutive years (2018 and 2019). A total of 27 sampling sites from the selected regions were monitored and the percentage disease intensity (PDI) was assessed on 50 randomly selected palms. Spatial interpolation technique, ordinary kriging (OK) was employed to predict the disease occurrence at unsampled locations. OK resulted in aggregated spatial maps, where the disease intensity was substantial (40.25-72.45%) at sampling sites of the Malnad and coastal regions. Further, Moran's I spatial autocorrelation test confirmed the presence of significant spatial clusters (p ≤ 0.01) across the regions studied. Temporal analysis indicated the initiation of disease on different weeks dependent on the sampling sites and evaluated years with significant variation in PDI, which ranged from 9.25% to 72.45%. The occurrence of disease over time revealed that the epidemic was initiated early in the season (July) at the Malnad and coastal regions in contrary to the Maidan region where the occurrence was delayed up to the end of the season (September). Correlations between environmental variables and PDI revealed that, the estimated temperature (T), relative humidity (RH) and total rainfall (TRF) significantly positively associated (p = 0.01) with disease occurrence. Regression model analysis revealed that the association between Tmax, RH1 and TRF with PDI statistically significant and the coefficients for the predictors Tmax, RH1 and TRF are 1.731, 1.330 and 0.541, respectively. The information generated in the present study will provide a scientific decision support system, to generate forecasting models and a better surveillance system to develop adequate strategies to curtail the fruit rot of arecanut.

Deciphering core-microbiome of rice leaf endosphere: Revelation by metagenomic and microbiological analysis of aromatic and non-aromatic genotypes grown in three geographical zones.

We have deciphered the leaf endophytic-microbiome of aromatic (cv. Pusa Basmati-1) and non-aromatic (cv. BPT-5204) rice-genotypes grown in the mountain and plateau-zones of India by both metagenomic NGS (mNGS) and conventional microbiological methods. Microbiome analysis by sequencing V3-V4 region of ribosomal gene revealed marginally more bacterial operational taxonomic units (OTU) in the mountain zone at Palampur and Almora than plateau zone at Hazaribagh. Interestingly, the rice leaf endophytic microbiomes in mountain zone were found clustered separately from that of plateau-zone. The Bray-Curtis dissimilarity indices indicated influence of geographical location as compared to genotype per se for shaping rice endophytic microbiome composition. Bacterial phyla, Proteobacteria followed by Bacteroidetes, Firmicutes, and Actinobacteria were found abundant in all three locations. The core-microbiome analysis devulged association of Acidovorax; Acinetobacter; Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium; Aureimonas; Bradyrhizobium; Burkholderia-Caballeronia-Paraburkholderia; Enterobacter; Pantoea; Pseudomonas; Sphingomonas; and Stenotrophomonas with the leaf endosphere. The phyllosphere and spermosphere microbiota appears to have contributed to endophytic microbiota of rice leaf. SparCC network analysis of the endophytic-microbiome showed complex cooperative and competitive intra-microbial interactions among the microbial communities. Microbiological validation of mNGS data further confirmed the presence of core and transient genera such as Acidovorax, Alcaligenes, Bacillus, Chryseobacterium, Comamonas, Curtobacterium, Delftia, Microbacterium, Ochrobactrum, Pantoea, Pseudomonas, Rhizobium, Rhodococcus, Sphingobacterium, Staphylococcus, Stenotrophomonas, and Xanthomonas in the rice genotypes. We isolated, characterized and identified core-endophytic microbial communities of rice leaf for developing microbiome assisted crop management by microbiome reengineering in future.

Revisiting the plant growth-promoting rhizobacteria: lessons from the past and objectives for the future.

Plant beneficial rhizobacteria (PBR) is a group of naturally occurring rhizospheric microbes that enhance nutrient availability and induce biotic and abiotic stress tolerance through a wide array of mechanisms to enhance agricultural sustainability. Application of PBR has the potential to reduce worldwide requirement of agricultural chemicals and improve agro-ecological sustainability. The PBR exert their beneficial effects in three major ways; (1) fix atmospheric nitrogen and synthesize specific compounds to promote plant growth, (2) solubilize essential mineral nutrients in soils for plant uptake, and (3) produce antimicrobial substances and induce systemic resistance in host plants to protect them from biotic and abiotic stresses. Application of PBR as suitable inoculants appears to be a viable alternative technology to synthetic fertilizers and pesticides. Furthermore, PBR enhance nutrient and water use efficiency, influence dynamics of mineral recycling, and tolerance of plants to other environmental stresses by improving health of soils. This report provides comprehensive reviews and discusses beneficial effects of PBR on plant and soil health. Considering their multitude of functions to improve plant and soil health, we propose to call the plant growth-promoting bacteria (PGPR) as PBR.

Genome Sequence of a Unique Magnaporthe oryzae RMg-Dl Isolate from India That Causes Blast Disease in Diverse Cereal Crops, Obtained Using PacBio Single-Molecule and Illumina HiSeq2500 Sequencing.

The whole-genome assembly of a unique rice isolate from India, Magnaporthe oryzae RMg-Dl that causes blast disease in diverse cereal crops is presented. Analysis of the 34.82 Mb genome sequence will aid in better understanding the genetic determinants of host range, host jump, survival, pathogenicity, and virulence factors of M. oryzae.