Zancarini, A. | bioRxiv Science

bioRxiv2025

Quantitative trait locus mapping of root exudate metabolome in a Solanum lycopersicum Moneymaker x S. pimpinellifolium RIL population and their putative links to rhizosphere microbiome

Crop resilience to abiotic and biotic stresses is vital for sustainable agriculture and global food security. The root exudates (RE)--a complex mixture of metabolites exuded by the plant roots--plays a central role in this resilience by mediating interactions with organisms around the roots, including soil microorganisms. These interactions influence nutrient cycling, the recruitment of beneficial microbes, and ultimately plant health. Although host genotype is known to affect the RE composition, the specific metabolites and underlying genetic mechanisms remain poorly understood. Here, we employed untargeted metabolomics to analyse the tomato RE composition and mapped individual metabolic features to the tomato genome using quantitative trait locus (QTL) analysis in a recombinant inbred line population derived from Solanum lycopersicum and Solanum pimpinellifolium. Our results shed light on the intricate chemical composition of the tomato RE, and reveal domestication-driven shifts in the RE composition and genetic loci associated with the abundance of specific metabolites. Notably, we identify overlap between metabolic and earlier published microbial QTLs, supportive of a link between RE composition and microbiome assembly, and providing a basis for further unravelling of the plant-microbe chemical interaction.

plant biology↗

bioRxiv2023

A Strategy for Differential Abundance Analysis of Sparse Microbiome Data with Group-wise Structured Zeros

Comparing the abundance of microbial communities between different groups or obtained under different experimental conditions using count sequence data is a challenging task due to various issues such as inflated zero counts, overdispersion, and non-normality. Several methods and procedures based on counts, their transformation and compositionality have been proposed in the literature to detect differentially abundant species in datasets containing hundreds to thousands of microbial species. Despite efforts to address the the large numbers of zeros present in microbiome datasets, even after careful data preprocessing, the performance of existing methods is impaired by the presence of inflated zero counts and group-wise structured zeros (i.e., all zero counts in a group). We propose and validate using extensive simulations an approach combining two differential abundance testing methods, namely DESeq2-ZINBWaVE and DESeq2, to address the issues of zero-inflation and group-wise structured zeros, respectively. This combined approach was subsequently successfully applied to two plant microbiome datasets that revealed a number of taxa as interesting candidates for further experimental validation.

bioinformatics↗