Hernandez-Benitez, E. M. | Search bioRxiv

bioRxiv2024

Putrescine acts as a signaling metabolite in the transition from nodulation to nitrogen fixation in Rhizobium phaseoli

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.

microbiology↗

bioRxiv2023

Improving gene regulatory network inference and assessment: The importance of using network structure

Gene regulatory networks are graph models representing cellular transcription events. Networks are far from complete due to time and resource consumption for experimental validation and curation of the interactions. Previous assessments have shown the modest performance of the available network inference methods based on gene expression data. Here, we study several caveats on the inference of regulatory networks and methods assessment through the quality of the input data and gold standard, and the assessment approach with a focus on the global structure of the network. We used synthetic and biological data for the predictions and experimentally-validated biological networks as the gold standard (ground truth). Standard performance metrics and graph structural properties suggest that methods inferring co-expression networks should no longer be assessed equally with those inferring regulatory interactions. While methods inferring regulatory interactions perform better in global regulatory network inference than co-expression-based methods, the latter is better suited to infer function-specific regulons and co-regulation networks. When merging expression data, the size increase should outweigh the noise inclusion and graph structure should be considered when integrating the inferences. We conclude with guidelines to take advantage of inference methods and their assessment based on the applications and available expression datasets.

systems biology↗