bioRxivMicrobiologyPreprint
Putrescine acts as a signaling metabolite in the transition from nodulation to nitrogen fixation in Rhizobium phaseoli
E. M. Hernandez-Benitez, E. Martinez-Romero, J. L. Aguirre-Noyola, D. Ledezma-Tejeida
Abstract
Growth of the common bean plant Phaseolus vulgaris is tightly linked to its symbiotic relationship with diverse rhizobial species, particularly Rhizobium phaseoli, an alphaproteobacteria that forms root nodules and provides high levels of nitrogen to the plant. Molecular cross-talk is known to happen via plant-derived metabolites, but only flavonoids have been identified as signals. Flavonoids are transported inside the bacteria, where they signal the NodD regulator to elicit nodulation. Although seven other regulators are known to be involved, our knowledge of the regulatory mechanisms underlying the nodulation, and nitrogen fixation processes is limited, and the signals recognized by regulators are mostly unknown. Here, we identified 75 transcription factors in R. phaseoli genome through sequence conservation from Escherichia coli, and assembled a transcriptional regulatory network comprising 24 regulators, and 652 target genes. We identified the interactions relevant to nodulation via gene expression, and signaled out putrescine as a signaling metabolite. We propose a model where putrescine acts as a switch on the transition from nodulation to nitrogen fixation via the dual transcription factor PuuR, and its regulation of the nodI and nifU2 genes. ImportanceThis study provides new insights into the molecular cross-talk between Phaseolus vulgaris and Rhizobium phaseoli, which is crucial for the development of alternative cropping strategies, including biopesticides and biofertilizers. In addition, we present the first transcriptional regulatory network systematically assembled for Rhizobium phaseoli, which opens new avenues of research in the relationship between the transcriptional regulation and metabolism of these organisms.
- organism: bacteria (other)
- research focus: chromatin & epigenetic regulation
- approach: statistical model
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