Back

Genomic-Based Prediction of Exopolysaccharide Composition and Structure: Insights from Rhizobium and Sinorhizobium Species

Tulumello, J.; Long, J.; Achouak, W.; Garron, M.-L.; Terrapon, N.; Heulin, T.

2026-08-26 bioinformatics
10.64898/2026.08.21.746188 bioRxiv
Show abstract

Bacterial exopolysaccharides (EPS) are key components in biofilm formation, stress protection, and symbiosis in Rhizobiaceae. While EPS structural diversity is extensive, experimental characterization remains limited. In this study, we experimentally determined and compared four distinct EPS structures produced by ten Rhizobium alamii strains. Using genomic data, we bioinformatically identified supra-operonic clusters (SOCs) responsible for these EPS biosynthesis. We introduced a computational framework to predict, score, and compare EPS SOCs across 84 Rhizobium and Sinorhizobium species, linking gene content to structural and functional EPS diversity. A total of 743 EPS SOCs was selected for network analyses, allowing the identification of 36 major groups of orthologous EPS SOCs, successfully recovering all known EPS biosynthetic loci and two novels SOCs potentially encoding uncharacterized EPS (xEPS-I, xEPS-II). Profiles of EPS SOCs correlated with taxonomical groups, with a single EPS SOC conserved through all 84 genomes and distinct additional EPS SOCs depending on the group, but do not strictly explain symbiotic capacity. Genetic comparisons of transporters (Wzx, Wzy) and glycosyltransferase sequences indicated these proteins as key markers of EPS structure. Overall, this computational framework accurately identified and classified EPS SOCs, providing a scalable, genome-based method for predicting EPS biosynthetic potential in Rhizobiaceae and usable in other microbial genera.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.