Influence of resistance breeding in common bean on rhizosphere microbiome composition and function

Abstract

The rhizosphere microbiome has a key role in plant growth and health, providing a first line of defense against root infections by soil-borne pathogens. Here, we investigated the composition and metabolic potential of the rhizobacterial community of different common bean (Phaseolus vulgaris) cultivars with variable levels of resistance to the fungal root pathogen Fusarium oxysporum (Fox). For the different bean cultivars grown in two soils with contrasting physicochemical properties and microbial diversity, rhizobacterial abundance was positively correlated with Fox resistance. Pseudomonadaceae, bacillaceae, solibacteraceae and cytophagaceae were more abundant in the rhizosphere of the Fox-resistant cultivar. Network analyses showed a modular topology of the rhizosphere microbiome of the Fox-resistant cultivar, suggesting a more complex and highly connected bacterial community than in the rhizosphere of the Fox-susceptible cultivar. Metagenome analyses further revealed that specific functional traits such as protein secretion systems and biosynthesis genes of antifungal phenazines and rhamnolipids were more abundant in the rhizobacterial community of the Fox-resistant cultivar. Our findings suggest that breeding for Fox resistance in common bean may have co-selected for other unknown plant traits that support a higher abundance of specific beneficial bacterial families in the rhizosphere with functional traits that reinforce the first line of defense.

Genome‐wide association studies on the phyllosphere microbiome: Embracing complexity in host–microbe interactions

“…Then, the frequencies of these systems can be compared across plant genotypes. This approach was used to find microbial functions that were more frequent in the rhizosphere of a fungus‐resistant bean cultivar than in the rhizosphere of a related, susceptible cultivar (Mendez et al ., ). In particular, samples from the resistant cultivar had a higher abundance of microbial sequences associated with the production of rhamnolipids and phenazines.…”

A suite of complementary biocontrol traits allows a native consortium of root‐associated bacteria to protect their host plant from a fungal sudden‐wilt disease

“…Bacterial biocontrols protect plants from phytopathogens by competing for these nutrients and niches, which in turn limit pathogen growth (Duffy, ). This effect has been demonstrated for bacterial strains belonging to the genus Pseudomonas that protect plants from Fusarium wilt disease (Mendes, Raaijmakers, Hollander, Mendes, & Tsai, ; Raaijmakers et al, ; Raaijmakers, Paulitz, Steinberg, Alabouvette, & Moënne‐Loccoz, ). Another efficient bacterial biocontrol strategy is the production of iron chelators, termed siderophores, that sequester iron from the surrounding soil, thereby reducing the growth of the pathogens (Doornbos, Loon, & Bakker, ; de Weger et al, ).…”

Inference based PICRUSt accuracy varies across sample types and functional categories

“…The datasets used in this study include 2 human datasets (named as Human_KW [28] and Human_TY [29] in our study after the initials of their first authors), 1 gorilla [30], 1 mouse [31], 1 chicken [32] and 2 soil datasets Soil_LWM [33] and Soil_AAN [34]. Each dataset has publicly available 16S rRNA and metagenome sequences and is associated with a two-level categorical metadata.…”

“…The PICRUSt predictions of the 16S rRNA sequences in the datasets followed the developer’s instructions [4]. The authors’ metagenome analysis results were used when available [29, 31, 33, 34], otherwise the raw sequences were analyzed with humann2 following the developer’s instructions [35]. In each dataset, all PICRUSt-predicted gene families and pathways were compared to those from metagenome sequencing to determine their discrepancy in the number and types of genes revealed.…”