Juarez-Verdayes, M. A. | bioRxiv Science

bioRxiv2026

Calreticulin modulates the infection process and nodule organogenesis in the Phaseolus vulgaris-Rhizobium symbiosis

Calreticulins are multifunctional proteins involved in calcium homeostasis, protein folding, and cellular signaling. In common bean (Phaseolus vulgaris), the molecular mechanisms that regulate infection and nodule development remain incompletely understood. The main objective of this study was to characterize the role of the calreticulin gene PvCRT08 during infection and nodulation processes. We first analyzed the calreticulin gene family in the P. vulgaris genome and identified three members, with PvCRT08 showing the highest transcript accumulation in roots and after inoculation with rhizobia. Spatial and temporal promoter analyses in transgenic composite bean roots revealed PvCRT08 activity in root hairs and in infected cells and vascular bundles of mature nodules. RNA interference (RNAi)-mediated PvCRT08 down-regulation in transgenic roots increased the number of infection threads and enhanced nitrogen fixation efficiency, leading to the formation of larger and more functional nodules, although total nodule number was unaffected. In contrast, overexpression of PvCRT08 impaired infection thread progression, reduced the expression of key nodulation marker genes (PvCyclin and PvNIN), decreased nodule number, and diminished nitrogen fixation capacity. These findings identify PvCRT08 as a key regulatory component of early infection events and nodule development in common bean. Furthermore, the study provides new insights into the molecular control of symbiotic efficiency and highlights PvCRT08 expression is critical to optimize the equilibrium between infection efficiency and nodule functionality.

plant biology↗

bioRxiv2026

Efficient training of neural networks using natural vectors with covariates for the plant microRNA precursor prediction.

The Fabaceae plants (Legumes) are important for the economy and food sovereignty of Mexico. Traits development of agronomic interest, and other biological activities of the Fabaceae plants are tightly related to the gene regulation, like the post-transcriptional gene repression mediated by microRNAs. Several artificial intelligence models have been developed for the miRNA precursor sequence prediction. They were based mainly on Convolutional Neural Network and Multi-Layer Perceptron architectures. Although the numerical encoding of nucleotide sequence and its secondary structure of pre-miRNAs implemented in these neural networks showed good performance, there are other encoding methods that have not been explored. Recently, a geometric construction of viral genome space and the numerical encoding of the archaea, bacteria, fungi and viruses genomes were successfully achieved employing natural vectors with covariance component. Natural vectors have also been used as input data during neural networks training for the classification of viral genomes. In consequence, in this work we mainly assessed the performance of neural networks as regression or classifier models trained with nucleotide sequences and its secondary structure representation encoded by natural vectors with covariance component alone or nested within the three sequences method. Additionally, we tested other characteristics of neural networks, and the results of training neural networks with natural vectors with covariates showed a better performance in predicting intrinsic nucleotide features, such as percentage of guanine and cytosine, pairwise-aligned sequence identity. Also, it showed good accuracy in categorizing miRNA precursor sequences compared with the results obtained from other encoding methods, that are often used in the numerical representation of nucleotide sequences.

bioinformatics↗