Tomato microbiome under long‐term organic and conventional farming

Zhang, Zeyu; Xiao, Yang; Zhan, Yabin; Zhang, Zengqiang; Liu, Youzhou; Wei, Yuquan; Xu, Ting; Li, Ji

doi:10.1002/imt2.48

Cited by 13 publications

(12 citation statements)

References 57 publications

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“…Additionally, nitrate reductase ( NarG ) was in class 0 and can be represented by landmark genes that encode superoxide dismutase ( SOD ) and 4‐hydroxy‐tetrahydrodipicolinate synthase ( DapA ), both of which were critical enzymes in antioxidant systems, mainly involved in superoxide scavenging. This was also supported by the domain role of SOD in determining the microbial network in a previous study [35].…”

Section: Resultssupporting

confidence: 79%

From mechanism to application: Decrypting light‐regulated denitrifying microbiome through geometric deep learning

Liao,

Zhao,

Bian

et al. 2024

iMeta

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Regulation on denitrifying microbiomes is crucial for sustainable industrial biotechnology and ecological nitrogen cycling. The holistic genetic profiles of microbiomes can be provided by meta‐omics. However, precise decryption and further applications of highly complex microbiomes and corresponding meta‐omics data sets remain great challenges. Here, we combined optogenetics and geometric deep learning to form a discover–model–learn–advance (DMLA) cycle for denitrification microbiome encryption and regulation. Graph neural networks (GNNs) exhibited superior performance in integrating biological knowledge and identifying coexpression gene panels, which could be utilized to predict unknown phenotypes, elucidate molecular biology mechanisms, and advance biotechnologies. Through the DMLA cycle, we discovered the wavelength‐divergent secretion system and nitrate‐superoxide coregulation, realizing increasing extracellular protein production by 83.8% and facilitating nitrate removal with 99.9% enhancement. Our study showcased the potential of GNNs‐empowered optogenetic approaches for regulating denitrification and accelerating the mechanistic discovery of microbiomes for in‐depth research and versatile applications.

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Section: Resultssupporting

confidence: 79%

From mechanism to application: Decrypting light‐regulated denitrifying microbiome through geometric deep learning

Liao,

Zhao,

Bian

et al. 2024

iMeta

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“…Our data revealed that tomato core microbiota was dominated by phyla Proteobacteria, Actinobacteria, and Firmicutes in the substrate, rhizosphere, and fruit samples; comparable bacterial diversity has been reported in numerous studies with tomato plants cultivated in soil-based and SCSs [20,27,[63][64][65][66][67][68][69][70]. Together, these findings suggest that, regardless of the cultivation system, tomato plants have evolved a close biological interaction with members of Proteobacteria, Actinobacteria, and Firmicutes.…”

Section: Tomato Core Microbiota In Soilless Culture Systemssupporting

confidence: 85%

“…Particularly, it was revealed that most of the effects in the organic production system could be linked to Flavobacteriaceae , Erythrobacteraceae , Bradyrhizobiaceae , and Nocardioidaceae in substrates; Caulobacteraceae , Chitinophagaceae , Flavobacteriaceae, Rhodobacteraceae , Streptomycetaceae , in the rhizosphere; and Enterobacteriaceae in tomato fruit samples. These potential microbial biomarkers have been highlighted by other studies with tomato and lettuce cultivars [ 19 , 67 , 69 , 96 , 99 ].…”

Section: Discussionmentioning

confidence: 69%

“…Specifically, the present study revealed that substrate, rhizosphere, and fruit samples under an organic SCS have altered relative abundance of 20, 21, and 12 families (listed in Figure 2 e), respectively, with all of them members of Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria phyla. Importantly, the dysbiosis characterized by a remarkable change in the relative abundance of Enterobacteriaceae , Flavobacteriaceae , Hyphomicrobiaceae , Pseudomonadaceae Rhizobiaceae , Rhodobacteraceae , and Xanthomonadaceae in substrate, rhizosphere, and fruit samples from the organic production system has been documented by many other studies [ 19 , 69 , 96 , 98 , 99 , 100 ], suggesting that these bacterial families could represent a microbial target that could be used to improve tomato plant health, productivity, and quality.…”

Section: Discussionmentioning

confidence: 85%

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Tomato Plant Microbiota under Conventional and Organic Fertilization Regimes in a Soilless Culture System

Resendiz-Nava,

Alonso-Onofre,

Silva-Rojas

et al. 2023

Microorganisms

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Tomato is the main vegetable cultivated under soilless culture systems (SCSs); production of organic tomato under SCSs has increased due to consumer demands for healthier and environmentally friendly vegetables. However, organic tomato production under SCSs has been associated with low crop performance and fruit quality defects. These agricultural deficiencies could be linked to alterations in tomato plant microbiota; nonetheless, this issue has not been sufficiently addressed. Thus, the main goal of the present study was to characterize the rhizosphere and phyllosphere of tomato plants cultivated under conventional and organic SCSs. To accomplish this goal, tomato plants grown in commercial greenhouses under conventional or organic SCSs were tested at 8, 26, and 44 weeks after seedling transplantation. Substrate (n = 24), root (n = 24), and fruit (n = 24) composite samples were subjected to DNA extraction and high-throughput 16S rRNA gene sequencing. The present study revealed that the tomato core microbiota was predominantly constituted by Proteobacteria, Actinobacteria, and Firmicutes. Remarkably, six bacterial families, Bacillaceae, Microbacteriaceae, Nocardioidaceae, Pseudomonadaceae, Rhodobacteraceae, and Sphingomonadaceae, were shared among all substrate, rhizosphere, and fruit samples. Importantly, it was shown that plants under organic SCSs undergo a dysbiosis characterized by significant changes in the relative abundance of Bradyrhizobiaceae, Caulobacteraceae, Chitinophagaceae, Enterobacteriaceae, Erythrobacteraceae, Flavobacteriaceae, Nocardioidaceae, Rhodobacteraceae, and Streptomycetaceae. These results suggest that microbial alterations in substrates, roots, and fruits could be potential factors in contributing to the crop performance and fruit quality deficiencies observed in organic SCSs.

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“…Therefore, the relationship of metabolites associated with the microbial community assembly is difficult to identify. The joint application of the topological overlap matrix (TOM) algorithm of weighted correlation network analysis (WGCNA) and the important prediction of variables in the random forest model, identifying the “key” metabolites induce rhizosphere microbiome assembly and reflect the microbial community characteristics (keystone, dominant species, and Bray–Curtis distance) in the “rebuild” state [ 21 ]. In addition, root exudates directly impact the soil, further influencing the interactions with microorganisms.…”

Section: Introductionmentioning

confidence: 99%

Rhizosphere interface microbiome reassembly by arbuscular mycorrhizal fungi weakens cadmium migration dynamics

Wang,

Du,

Zhang

et al. 2023

iMeta

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The prevalence of cadmium (Cd)‐polluted agricultural soils is increasing globally, and arbuscular mycorrhizal fungi (AMF) can reduce the absorption of heavy metals by plants and improve mineral nutrition. However, the immobilization of the rhizosphere on cadmium is often overlooked. In this study, Glomus mosseae and Medicago sativa were established as symbiotes, and Cd migration and environmental properties in the rhizosphere were analyzed. AMF reduced Cd migration, and Cd2+ changed to an organic‐bound state. AMF symbiosis treatment and Cd exposure resulted in microbial community variation, exhibiting a distinct deterministic process (|βNTI| > 2), which ultimately resulted in a core microbiome function of heavy metal resistance and nutrient cycling. AMF increased available N and P, extracellular enzyme activity (LaC, LiP, and CAT), organic matter content (TOC, EOC, and GRSP), and Eh of the rhizosphere soil, significantly correlating with decreased Cd migration (p < 0.05). Furthermore, AMF significantly affected root metabolism by upregulating 739 metabolites, with flavonoids being the main factor causing microbiome variation. The structural equation model and variance partial analysis revealed that the superposition of the root metabolites, microbial, and soil exhibited the maximum explanation rate for Cd migration reduction (42.4%), and the microbial model had the highest single explanation rate (15.5%). Thus, the AMF in the rhizosphere microenvironment can regulate metabolite–soil–microbial interactions, reducing Cd migration. In summary, the study provides a new scientific explanation for how AMF improves plant Cd tolerance and offers a sustainable solution that could benefit both the environment and human health.

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Tomato microbiome under long‐term organic and conventional farming

Cited by 13 publications

References 57 publications

From mechanism to application: Decrypting light‐regulated denitrifying microbiome through geometric deep learning

From mechanism to application: Decrypting light‐regulated denitrifying microbiome through geometric deep learning

Tomato Plant Microbiota under Conventional and Organic Fertilization Regimes in a Soilless Culture System

Rhizosphere interface microbiome reassembly by arbuscular mycorrhizal fungi weakens cadmium migration dynamics

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