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ABSTRACT: partitioned recirculating renovation for rice-fish farming impact soil bacteria

PROVIDER: PRJNA1172164 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR30980051_1.fastq.gz	Fastqsanger.gz
	SRR30980051_2.fastq.gz	Fastqsanger.gz
	SRR30980052_1.fastq.gz	Fastqsanger.gz
	SRR30980052_2.fastq.gz	Fastqsanger.gz
	SRR30980053_1.fastq.gz	Fastqsanger.gz

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Project description:Intensive application of inorganic nitrogen underlies marked increase in crop production yet imposes detrimental impact on ecosystems, hence it is crucial for future sustainable agriculture to improve nitrogen-use efficiency (NUE). Here we report the genetic basis of NUE associated with the local soil adaptation in rice. With a diverse rice germplasm panel collected from different ecogeographic regions, we performed genome-wide association study on tillering response to nitrogen (TRN), the most correlated trait with NUE of rice, and identified OsTCP19 as a modulator of TRN via transcriptionally responding to nitrogen and targeting to Dwarf and Low-Tillering (DLT), a tiller-promoting gene. A 29-bp InDel in OsTCP19 promoter confers differential transcription response to nitrogen and TRN variation among rice varieties. The high-TRN allele of OsTCP19 (OsTCP19-H) is prevalent in wild rice population, but largely lost in modern cultivars correlating with increased local soil nitrogen content, suggesting that it might have contributed to geographic adaptation in rice. Introgression of OsTCP19-H into modern rice cultivars boosts grain yield and NUE under low or moderate nitrogen levels, demonstrating its enormous potential for rice breeding and environment amelioration through reducing nitrogen application.

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Project description:The expanding scale and nature of rice fraud in the global food system has caused major economic and human health concerns. Herein, an untargeted metabolomics approach based on the UHPLC-Q-Orbitrap-HRMS system was utilized for the discrimination between authentic and commercial Sengcu rice, a local specialty cultivated by terraced farming in northern Vietnam. A total of 8398 positive and 5250 negative mode compounds were introduced to multivariate statistical analyses for the construction of classification models. The first two principal components explaining 52% of the total variance in both datasets exhibited distinguished clusters of authentic against commercial Sengcu rice. Partial least squares-discriminant analysis models were optimized to obtain the optimal number of retained components, the optimal number of variables retained in each component and the best prediction distance type for model evaluation. One component containing five positive (DMG, RSA, RCA, PAL and BOSe) and six negative mode variables (PXP, RXP, TDHP, ISS, MXP and RGB) was sufficient to discriminate between authentic and commercial Sengcu rice. The classification error rate was less than 1.1310-4, as determined from repeated k-fold cross validation. These putative signature metabolites clearly separated authentic and commercial Sengcu rice in the hierarchical clustering models. In addition, the isolated metabolites also reflected the cultivation practices of terraced farming of authentic Sengcu rice. Overall, we have proposed an effective method for the identification of key metabolites from fingerprinting metabolomics, and it could serve as a fundamental approach for other in-depth food authentication studies.

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