Back

Leaf and root microbiome signatures of gray mangrove trees in the Red Sea

Jimenez, D. J.; Jamil, T.; Pena-Valencia, M. F.; Alzubaidy, H.; Baazeem, A.; O'Toole, K.; Mendes, L. W.; Carvalho, S.; Rosado, A. S.

2026-09-23 microbiology
10.64898/2026.09.22.753136 bioRxiv
Show abstract

Mangroves persist under strong environmental constraints with support from microbes. Although microbial communities differ among mangrove compartments, their assembly mechanisms and compartment-specific functional signatures remain poorly resolved. Here, we combine peptide nucleic acid-clamping coupled with 16S rRNA gene amplicon sequencing and genome-resolved metagenomics to investigate the microbial communities associated with the leaves and roots of gray mangrove trees in the Red Sea. Our results suggest homogeneous selection and homogenizing dispersal as key processes of microbial community assembly in leaves. This phyllosphere hosts novel prokaryotic lineages, with some of its members probably synthesizing rhodopsins, plant polysaccharide-degrading enzymes, and gamma-aminobutyric acid, a metabolite that increase tolerance to salinity stress. Genomes affiliated with Desulfobacterales and Sedimenticolaceae taxa are abundant belowground, suggesting complementary sulfur- and nitrogen-cycling capacities similar as occur in other blue-carbon ecosystems. Comparison with root-derived genomes from cordgrass revealed a host-driven selection of Sedimenticolaceae species with convergent metabolic profiles. Overall, this study provides an integrative view about the microbial biology of gray mangrove trees, offering foundational insights into the diversity, ecology, and predictive functionality of their aboveground-belowground microbiomes.

Matching journals

The top 3 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.