Bacterial Community Structure Dynamics in Meloidogyne incognita -Infected Roots and Its Role in Worm-Microbiome Interactions

Abstract

Plant parasitic nematodes such as Meloidogyne incognita have a complex life cycle, occurring sequentially in various niches of the root and rhizosphere. They are known to form a range of interactions with bacteria and other microorganisms that can affect their densities and virulence. High-throughput sequencing can reveal these interactions in high temporal and geographic resolutions, although thus far we have only scratched the surface. In this study, we have carried out a longitudinal sampling scheme, repeatedly collecting rhizosphere soil, roots, galls, and second-stage juveniles from 20 plants to provide a high-resolution view of bacterial succession in these niches, using 16S rRNA metabarcoding. Our findings indicate that a structured community develops in the root, in which gall communities diverge from root segments lacking a gall, and that this structure is maintained throughout the crop season. We describe the successional process leading toward this structure, which is driven by interactions with the nematode and later by an increase in bacteria often found in hypoxic and anaerobic environments. We present evidence that this structure may play a role in the nematode’s chemotaxis toward uninfected root segments. Finally, we describe the J2 epibiotic microenvironment as ecologically deterministic, in part, due to the active bacterial attraction of second-stage juveniles. IMPORTANCE The study of high-resolution successional processes within tightly linked microniches is rare. Using the power and relatively low cost of metabarcoding, we describe the bacterial succession and community structure in roots infected with root-knot nematodes and in the nematodes themselves. We reveal separate successional processes in galls and adjacent non-gall root sections, which are driven by the nematode’s life cycle and the progression of the crop season. With their relatively low genetic diversity, large geographic range, spatially complex life cycle, and the simplified agricultural ecosystems they occupy, root-knot nematodes can serve as a model organism for terrestrial holobiont ecology. This perspective can improve our understanding of the temporal and spatial aspects of biological control efficacy.

Intergrated metagenomics and metabolomics analysis discovers nematicidal microbes, enzymes and metabolites from the plant rhizosphere microbiota

“…Bacterial community richness and diversity in root-knot nematode diseased soils was significantly different from that in healthy soils [8, 28-30, 86, 87]. Our results are consistent to some findings in these studies: (1) the rhizosphere and endophyte microbial communities of plant root (especially Solanaceae) are affected by nematode-pathogenesis [29,30,87]; (2) potential biological control microorganisms such as Arthrobacter, Bacillus, Lysobacter, and Pseudomonas showed large proportions in non-infested soil and fields with lower population densities of M. incognita [28,30]; (3) In rhizosphere and endophyte community, the most abundant phylum is Proteobacteria [87]. In our study, Proteobacteria was also the most abundant phylum.…”

The gut microbiota of Cystidicola farionis parasitizing the swim bladder of the nosed charr morph Salvelinus malma complex in Lake Kronotskoe (Kamchatka, Russia)

“…In comparison to fish, the microbiome research associated with nematodes isolated from different environments has become an increasingly popular area of study. Using the 16S rRNA gene sequencing approaches the microbiota of Caenorhabditis elegans ( Berg et al, 2016 ; Dirksen et al, 2016 ; Samuel et al, 2016 ), various marine nematodes ( Schuelke et al, 2018 ; Bellec et al, 2019 ), soil-associated nematodes ( Baquiran et al, 2013 ; Berg et al, 2016 ; Zheng et al, 2020 ), a ruminant parasite ( Cortés et al, 2020 ), and plant parasitic nematodes ( Yergaliyev et al, 2020 ) were investigated. To date, the most intensively studied model species for the microbiome research is the nematode C. elegans ( Berg et al, 2016 ; Dirksen et al, 2016 ; Samuel et al, 2016 , and others).…”

Composition identification and functional verification of bacterial community in disease‐suppressive soils by machine learning

“…Among 827 raw data we collected for machine learning, a subset of data for disease‐conducive soils provided by Yergaliyev et al . (2020) contained 167 samples (PRJNA614519). If we considered all these 167 samples, results of metadata analysis and machine learning will be dominated by this subset of data and cannot obtain the general patterns of bacterial communities in multiple disease‐suppressive soils.…”

“…And the diseasesuppressive soil was defined as soils that rarely cause disease even when virulent pathogens and susceptible plant hosts are present, or the soils were confirmed that had the ability to suppress the diseases in the laboratory. Among 827 raw data we collected for machine learning, a subset of data for disease-conducive soils provided by Yergaliyev et al (2020) contained 167 samples (PRJNA614519). If we considered all these 167 samples, results of metadata analysis and machine learning will be dominated by this subset of data and cannot obtain the general patterns of bacterial communities in multiple disease-suppressive soils.…”