Identifying plant genes shaping microbiota composition in the barley rhizosphere

Escudero-Martinez, Carmen; Coulter, Max; Terrazas, Rodrigo Alegria; Foito, Alexandre; Kapadia, Rumana; Pietrangelo, Laura; Maver, Mauro; Sharma, Rajiv; Aprile, Alessio; Morris, Jenny; Hedley, Pete E.; Maurer, Andreas; Pillen, Klaus; Naclerio, Gino; Mimmo, Tanja; Barton, Geoffrey J.; Waugh, Robbie; Abbott, James; Bulgarelli, Davide

doi:10.1038/s41467-022-31022-y

Cited by 67 publications

(45 citation statements)

References 106 publications

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“…Genome-resolved metagenomics with time-series root transcriptome implicated iron metabolism in the root microbiome dynamics in response to drought stress ( Xu et al, 2021 ). Lately, using metagenomics information as an external quantitative phenotype with genomic and transcriptomic data, candidate genes in barley were identified for shaping microbiota composition ( Escudero-Martinez et al, 2022 ). Also, in tomatoes, the bacterial genes involved in the metabolism of iron, sulfur, and vitamins were reported to associate with specific QTLs ( Oyserman et al, 2022 ).…”

Section: Integration Of Genomics Transcriptomics and Microbiomicsmentioning

confidence: 99%

Integration of multi-omics technologies for crop improvement: Status and prospects

Zhang

Zhang²,

Yu³

et al. 2022

Front. Bioinform.

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With the rapid development of next-generation sequencing (NGS), multi-omics techniques have been emerging as effective approaches for crop improvement. Here, we focus mainly on addressing the current status and future perspectives toward omics-related technologies and bioinformatic resources with potential applications in crop breeding. Using a large amount of omics-level data from the functional genome, transcriptome, proteome, epigenome, metabolome, and microbiome, clarifying the interaction between gene and phenotype formation will become possible. The integration of multi-omics datasets with pan-omics platforms and systems biology could predict the complex traits of crops and elucidate the regulatory networks for genetic improvement. Different scales of trait predictions and decision-making models will facilitate crop breeding more intelligent. Potential challenges that integrate the multi-omics data with studies of gene function and their network to efficiently select desirable agronomic traits are discussed by proposing some cutting-edge breeding strategies for crop improvement. Multi-omics-integrated approaches together with other artificial intelligence techniques will contribute to broadening and deepening our knowledge of crop precision breeding, resulting in speeding up the breeding process.

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Section: Integration Of Genomics Transcriptomics and Microbiomicsmentioning

confidence: 99%

Integration of multi-omics technologies for crop improvement: Status and prospects

Zhang

Zhang²,

Yu³

et al. 2022

Front. Bioinform.

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“…RTD is the major backbone for accurate gene quantification of RNA-seq data analysis and consists of a list of genes and possible transcript isoforms of an organism ( Brown et al, 2017 ; Zhang et al, 2017a ; Rapazote-Flores et al, 2019 ; Vitoriano et al, 2021 ). The quality of the transcriptome annotation could help with fast and accurate estimation of transcript expression and AS events using the RNA-seq data with the help of the 3DRNA-seq tool ( Chaudhary and Kalkal, 2021 ; Guo et al, 2021 ; Vitoriano et al, 2021 ; Escudero-Martinez et al, 2022 ). Despite rice being an important crop plant and several attempts being made in the past to improve its transcriptome annotation and AS diversity, IndicaRTD contains a significantly higher number of transcript isoforms ( Lu et al, 2010 ; Zhang et al, 2010 ; Zhang H. et al, 2019 ; Schaarschmidt et al, 2020 ; Wang S. et al, 2022 ; Hasan et al, 2022 ; He et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

High-quality reference transcriptome construction improves RNA-seq quantification in Oryza sativa indica

Srikakulam

Sridevi²,

Pandi³

2022

Front. Genet.

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The Reference Transcriptomic Dataset (RTD) is an accurate and comprehensive collection of transcripts originating from a given organism. It holds the key to precise transcript quantification and downstream analysis of differential expressions and regulations. Currently, transcriptome annotations for most crop plants are far from complete. For example, Oryza sativa indica (O. sativa indica) is reported to have 40,759 transcripts in the Ensembl database without alternative transcript isoforms and alternative splicing (AS) events. To generate a high-quality RTD, we conducted RNA sequencing of rice leaf samples collected at various time points during Rhizoctonia solani infection. The obtained reads were analyzed by adopting the recently developed computational analysis pipeline to assemble the RTD with increased transcript and AS diversity for O. sativa indica (IndicaRTD). After stringent quality filtering, the newly constructed transcriptome annotation was comprised of 122,968 non-redundant transcripts from 53,695 genes. This study identified many novel transcripts compared to Ensembl deposited data that are important for regulating molecular and physiological processes in the plant system. Currently, the assembled IndicaRTD must allow fast quantification of transcript and gene expression with high precision.

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“…Hub gene AT1G25550, encoding nitrate-inducible NIGT1.1/ HHO3 proteins, involves in regulating nitrate signaling and phosphorus starvation signals in Arabidopsis (Maeda et al, 2018). Hub gene HAL3A (AT3G18030) expresses HAL3-like protein A which is related to salt and osmotic tolerance and plant growth (Espinosa-Ruiz et al, 1999).…”

Section: Hub Qtls Associated With the Microbial Networkmentioning

confidence: 99%

“…The composition and functioning of the microbiomes even can predict plant health ( Wei et al, 2019 ) and help mitigate the negative consequences of climate change ( Jansson and Hofmockel, 2020 ). Microbiomes play a pivotal role in plant growth and health, but the genetic factors involved in microbiome assembly are still in their infancy ( Escudero-Martinez et al, 2022 ; Oyserman et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Disentangling leaf-microbiome interactions in Arabidopsis thaliana by network mapping

Cheng

Wang

et al. 2022

Front. Plant Sci.

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The leaf microbiota plays a key role in plant development, but a detailed mechanism of microbe-plant relationships remains elusive. Many genome-wide association studies (GWAS) have begun to map leaf microbes, but few have systematically characterized the genetics of how microbes act and interact. Previously, we integrated behavioral ecology and game theory to define four types of microbial interactions – mutualism, antagonism, aggression, and altruism, in a microbial community assembly. Here, we apply network mapping to identify specific plant genes that mediate the topological architecture of microbial networks. Analyzing leaf microbiome data from an Arabidopsis GWAS, we identify several heritable hub microbes for leaf microbial communities and detect 140–728 SNPs (Single nucleotide polymorphisms) responsible for emergent properties of microbial network. We reconstruct Bayesian genetic networks from which to identify 22–43 hub genes found to code molecular pathways related to leaf growth, abiotic stress responses, disease resistance and nutrition uptake. A further path analysis visualizes how genetic variants of Arabidopsis affect its fecundity through the internal workings of the leaf microbiome. We find that microbial networks and their genetic control vary along spatiotemporal gradients. Our study provides a new avenue to reveal the “endophenotype” role of microbial networks in linking genotype to end-point phenotypes in plants. Our integrative theory model provides a powerful tool to understand the mechanistic basis of structural-functional relationships within the leaf microbiome and supports the need for future research on plant breeding and synthetic microbial consortia with a specific function.

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Identifying plant genes shaping microbiota composition in the barley rhizosphere

Cited by 67 publications

References 106 publications

Integration of multi-omics technologies for crop improvement: Status and prospects

Integration of multi-omics technologies for crop improvement: Status and prospects

High-quality reference transcriptome construction improves RNA-seq quantification in Oryza sativa indica

Disentangling leaf-microbiome interactions in Arabidopsis thaliana by network mapping

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