Biology subjects

Dangl, J.

Publications and source records attributed to Dangl, J..

3 recordsLinked to original sources

bioRxiv2026

Streptomyces enrichment in roots during drought is uncoupled from plant benefit and is driven by host suppression of iron uptake and immunity

Drought reshapes plant root microbiota, yet the mechanistic drivers and consequences of this observation remain unclear. We discovered that suppression of host immunity and iron homeostasis is required for Streptomyces enrichment in roots during drought across diverse soils. Genetic and physiological manipulation of these host pathways confirmed their requirement in modulating Streptomyces root enrichment. Drought-induced suppression of iron uptake was conserved across the [~]160 My monocot-eudicot divergence. Some Streptomyces strains enhanced plant growth and rescued iron uptake under drought. These benefits were uncoupled from Streptomyces root enrichment. They were instead shaped by intra-Streptomyces antagonism. We propose a two-step model: drought-driven down regulation of host iron and immune pathways enriches Streptomyces, while intra-genus dynamics fine-tune strain-level assembly and functional outcomes. Our data refine the idea that Streptomyces are enriched in roots during drought in response to a plant cry for help and consequently contribute to alleviation of this abiotic stress.

plant biology↗

bioRxiv2024

Multi-laboratory Study Establishes Reproducible Methods for Plant-Microbiome Research in Fabricated Ecosystems

Inter-laboratory replicability is crucial yet challenging in microbiome research. Leveraging microbiomes to promote soil health and plant growth requires understanding underlying molecular mechanisms using reproducible experimental systems. In a global collaborative effort involving five laboratories, we aimed to help advance reproducibility in microbiome studies by testing our ability to replicate synthetic community assembly experiments. Our study compared fabricated ecosystems constructed using two different synthetic bacterial communities, the model grass Brachypodium distachyon, and sterile EcoFAB 2.0 devices. All participating laboratories observed consistent inoculum-dependent changes in plant phenotype, root exudate composition, and final bacterial community structure where Paraburkholderia sp. OAS925 could dramatically shift microbiome composition. Comparative genomics and exudate utilization linked the pH-dependent colonization ability of Paraburkholderia, which was further confirmed with motility assays. The study provides detailed protocols, benchmarking datasets, and best practices to help advance replicable science and inform future multi-laboratory reproducibility studies.

microbiology↗

bioRxiv2017

Efficient online-group-screening designs for agent identification

Identifying significant causal agents among a large number of candidates is challenging. When experimental resources are limited, exhaustively screening a large number of agents for the desired effect could incur a large cost and take a substantial amount of time. However, in many large scale experiments, such as high-throughput screening (HTS), the ratio of causal to non-causal agents is usually very low.\n\nIn this paper, we introduce a group-screening strategy to efficiently screen causal agents by grouping them into treatments. Our analysis shows that when a large number of candidates factors are screened and true agent percentage is very low (less than 1%), even in the worst case we could save up to 80% of the experiment runs. In the case where experiments span many rounds, we provide an online version of the group-screening that can determine the best strategy automatically based on the existing results. We applied this method to a real HTS experiment with 50,000 candidates that would require 9 rounds to finish in an exhaustive case. Our analysis showed that by applying the online-group-screening method, in the worst case, we can use 3 rounds and 19.7% (9828/50000) total tests to identify all the agents.\n\nFinally, we show that with minor modifications, this framework extends to more complex agent discovery problems.

bioinformatics↗