Biology subjects

Waagepetersen, R.

Publications and source records attributed to Waagepetersen, R..

3 recordsLinked to original sources

bioRxiv2026

The common symbiosis pathway controls plant root microbiomes in a host-specific manner

Crop nutrition depends on plant-microbe interactions, yet it remains unclear whether conserved genetic pathways impose universal rules on root microbiome assembly across plant hosts. Here, we show that the Common Symbiosis Signalling Pathway (CSSP), a conserved genetic module controlling endosymbiosis with arbuscular mycorrhizal fungi and nitrogen-fixing bacteria, regulates root microbiome assembly in a host-specific manner across contrasting fertilisation regimes. Using Lotus japonicus and Hordeum vulgare, we demonstrate that mutations in orthologous CSSP genes remodel root bacterial communities in both species, but with distinct taxonomic outcomes. In Lotus, CSSP disruption reduces rhizobial colonisation and promotes niche replacement by commensal taxa, whereas in Hordeum, the same mutations broadly restructure bacterial lineages without converging on Lotus-like responses. Root exudate profiling reveals host-specific metabolic differences, particularly in phenylpropanoid (flavonoids and coumarins) and gibberellin pathways, linking CSSP activity to chemically distinct rhizosphere environments that correlate with divergent microbiome assembly patterns across hosts. Moreover, root bacterial community composition accurately predicts plant nutritional status, highlighting tight coupling between host physiology and microbiome composition. Together, our results show that conserved symbiosis signalling regulates root microbiome assembly, while host-specific metabolic environments determine taxonomic outcomes. This extends CSSP function beyond canonical endosymbioses and positions symbiosis signalling as a general determinant of plant-microbiome interactions with implications for crop nutrition. Significance StatementRoot microbiomes influence plant nutrition, yet how conserved host genetic pathways controlling interactions with intracellular symbionts shape root-associated microbiome assembly across divergent plant species remains unresolved. The Common Symbiosis Signalling Pathway (CSSP), conserved across most land plants, regulates root microbiome composition in both a legume and a cereal, but with distinct taxonomic outcomes. These effects correlate with CSSP-dependent differences in root exudate chemistry and host metabolic profiles. Together, our results show that conserved symbiosis signalling operates within host-specific metabolic contexts, providing a framework for understanding why disruption of the same genetic pathway can lead to divergent microbiome configurations across plant species.

plant biology↗

bioRxiv2023

Nitrogen source and Nod factor signaling map out the assemblies of Lotus japonicus root bacterial communities

Symbiosis with soil-dwelling bacteria that fix atmospheric nitrogen allows legume plants to grow in nitrogen-depleted soil. Symbiosis impacts the assembly of root microbiota, but it is not known how this process takes place and whether it is independent of nitrogen nutrition. We use plant and bacterial mutants to address the role of Nod factor signaling on Lotus japonicus root microbiota assembly. We found that Nod factors are produced by symbionts to activate Nod factor signaling in the host, and this modulates the assembly of a symbiotic root microbiota. Lotus plants grown in symbiosis-permissive or suppressive soils delineated three nitrogen-dependent nutritional states: starved, symbiotic, or inorganic. We found that root and rhizosphere microbiomes associated with these states differ in composition and connectivity, demonstrating that symbiosis and inorganic nitrogen impact the legume root microbiota differently. Finally, we demonstrated that selected bacterial genera delineating state-dependent microbiomes have a high level of accurate prediction.

plant biology↗

bioRxiv2023

Compositionally aware estimation of cross-correlations for microbiome data

In the field of microbiome studies, it is of interest to infer correlations between abundances of different microbes (here referred to as operational taxonomic units, OTUs). Several methods taking the compositional nature of the sequencing data into account exist. However, these methods cannot infer correlations between OTU abundances and other variables. In this paper we introduce the methods SparCEV (Sparse Correlations with External Variables) and SparXCC (Sparse Cross-Correlations between Compositional data) for quantifying correlations between OTU abundances and either continuous phenotypic variables or components of other compositional datasets, such as transcriptomic data. We compare these new methods to empirical Pearson cross-correlations after applying naive transformations of the data (log and log-TSS). Additionally, we test the centered log ratio transformation (CLR) and the variance stabilising transformation (VST). We find that CLR and VST outperform naive transformations, except when the correlation matrix is dense. For large numbers of OTUs, SparCEV and SparXCC perform similarly to CLR and VST. SparCEV outperforms all other tested methods when the number of OTUs is small (less than 100). SparXCC outperforms all tested methods when at least one of the compositional datasets has few variables (less than 50), and more so when both datasets have few variables. Author summarySequencing data of the microbiome posses a unique and challenging structure that renders many standard statistical tools invalid. Features such as compositionality and sparsity complicates statistical analysis, and as a result, specialized tools are needed. Practitioners have long been interested in the construction of correlation networks within the microbiome, and several methods for accomplishing this exist. However, less attention has been paid to the estimation of cross-correlations between microbial abundances and other variables (such as gene expression data or environmental and phenotypic variables). Here, we introduce novel approaches, SparCEV and SparXCC, for inferring such cross-correlations, and compare these to transformation-based approaches, namely log, log-TSS, CLR and VST. In some cases, SparCEV and SparXCC yield superior results, while in other cases, a simpler transformation-based approach suffices. The methods are used to study cross-correlations between bacterial abundances in the skin microbiome and the severity of atopic dermatitis, as well as cross-correlations between fungal and bacterial OTUs in the root microbiome of the legume Lotus japonicus.

bioinformatics↗