Stress mitigation strategies of plant growth-promoting rhizobacteria: Plant growth-promoting rhizobacteria mechanisms

One of the major challenges that the world is facing currently is the inadequate amount of food production with high nutrient content in accordance with the increase in population size. Moreover, availability of cultivable area with fertile soil is reducing day by day owing to ever increasing population. Further, water scarcity and expensive agricultural equipment have led to the use of agrochemicals and untreated water. Excessive use of chemical fertilizers to increase crop yield have resulted in deleterious effects on the environment, health and economy, which can be overcome to a great extent by employing biological fertilizers. There are various microbes that grows in the rhizospheric region of plants known as plant growth-promoting rhizobacteria (PGPR). PGPR act by direct and indirect modes to stimulate plant growth and improve stress reduction in plants. PGPRs are used for potential agriculture practices having a wide range of benefits like increase in nutrients content, healthy growth of crops, production of phytohormones, prevention from heavy metal stress conditions and increase in crop yield. This review reports recent studies in crop improvement strategies using PGPR and describes the mechanisms involved. The potential mechanisms of PGPR and its allies pave the way for sustainable development towards agriculture and commercialization of potential bacteria

Identification of extracellular matrix proteins of rice (Oryza sativa L.) involved in dehydration-responsive network: a proteomic approach

Water-deficit or dehydration impairs almost all physiological processes and greatly influences the geographical distribution of many crop species. It has been postulated that higher plants rely mostly on induction mechanisms to maintain cellular integrity during stress conditions. Plant cell wall or extracellular matrix (ECM) forms an important conduit for signal transduction between the apoplast and symplast and acts as front-line defense, thereby playing a key role in cell fate decision under various stress conditions. To better understand the molecular mechanism of dehydration response in plants, four-week-old rice seedlings were subjected to progressive dehydration by withdrawing water and the changes in the ECM proteome were examined using two-dimensional gel electrophoresis. Dehydration-responsive temporal changes revealed 192 proteins that change their intensities by more than 2.5-fold, at one or more time points during dehydration. The proteomic analysis led to the identification of about 100 differentially regulated proteins presumably involved in a variety of functions, including carbohydrate metabolism, cell defense and rescue, cell wall modification, cell signaling and molecular chaperones, among others. The differential rice proteome was compared with the dehydration-responsive proteome data of chickpea and maize. The results revealed an evolutionary divergence in the dehydration response as well as organ specificity, with few conserved proteins. The differential expression of the candidate proteins, in conjunction with previously reported results, may provide new insight into the underlying mechanisms of the dehydration response in plants. This may also facilitate the targeted alteration of metabolic routes in the cell wall for agricultural and industrial exploitation

Mapman analysis of genes differentially regulated in <i>gcr1</i> mutant.

<p>Out of the total list of 350 DEGs, 119 mapped onto biotic stress response. The red dots represent the up-regulated genes, green dots represent the down-regulated genes and the grey dots represent the genes to which none of the DEGs were assigned. The level of differential regulation is according to the scale given.</p

List of the top 20 up-regulated and the top 20 down-regulated genes in the GCR1 mutant.

<p>List of the top 20 up-regulated and the top 20 down-regulated genes in the GCR1 mutant.</p

Validation and comparison of microarray results using qPCR for a few genes selected from each of the important biological processes.

<p>The experiment was carried out using biological triplicates and the values are presented as log2FC ± SE. (AT2G36690–2-OG; AT1G65390—ATPP2-A5; AT1G49570—peroxidase family protein; AT2G35710—PGSIP7; AT5G44420—PDF1.2; AT2G44840—ERF13; AT5G20550–2-oxoglutarate; AT4G01350—Cysteine/Hisidine-rich C1 domain family protein; AT5G20150—SPX1; AT3G26830—PAD3; AT5G40990—GLIP1; AT1G15520—PDR12; AT3G09922—IPS1; AT1G19250—FMO1; AT2G45130—SPX3; AT2G02160—CCCH type zinc finger family protein; AT5G50300—AZG2.</p

Distribution of differentially regulated transcription factors into highly represented transcription factor families.

<p>Distribution of differentially regulated transcription factors into highly represented transcription factor families.</p

(a) T-DNA insertion site/orientation in the mutated gene of GCR1.

<p>The exons are represented as boxes and the introns are represented as lines. LB and RB represent the left and right border respectively. (b) qPCR validation of the mutant. The real time RT-PCR was performed in triplicate using independent samples of total RNA and the values are represented as relative quantity ± SE.</p

(a) Heat map of differentially expressed genes.

<p>The background-subtracted microarray data was subjected to hierarchical clustering using Genespring software ver. 11.5 to generate the heatmap. Yellow represents the control data, while red and green represent up-regulation and down regulation respectively. (b) GO categorization of DEGs. The DEGs were categorized into GO classes using classification superviewer tool of Bioarray resource (<a href="http://www.bar.utoronto.ca" target="_blank">www.bar.utoronto.ca</a>)</p