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Temperature is the main environmental determinant of seed oil fatty acid Q9 composition. There are no models describing common responses of main seed oil fatty acids to temperature in plants. The aim of thus work was to investigate common responses of seed oil fatty acids to minimum temperature during grain filling across species and genotypes. A database consisted of 164 genotypes of 9 species, sunflower, rapeseed, soybean, maize, flax, chia, safflower, olive and camelia, grown under a wide range of environmental conditions, was created and analyzed applying meta phenomics tools. Four widely sown species of the database was used to develop several common seed fatty acid responses and validate some models, and the other species were used to validate the General Model. The minimum temperature during grain filling responses of fatty acids in the General Model were close to responses found in genotypes of five independent species used to validate the model. Dissections of the general model by selecting the appropriate data allowed unraveling previously unknown features of the response of fatty acid to the minimum temperature during grain filling. The response of fatty acids to temperature for any species was unaffected by experimental conditions (field or controlled conditions) during the oil synthesis stage. The oleic acid trait did not affect the response to temperature of fatty acids synthesized downstream and upstream of it. Traits such as high stearic or high linoleic did not affect the response of fatty acids synthesized upstream or downstream of the trait. The established models and new knowledge could be applied to design cost effective and timely experiments to assess the potential responses of seed oil fatty acids to temperature of previously untested genotypes. [ABSTRACT FROM AUTHOR]

Temperature is the main environmental determinant of seed oil fatty acid Q9 composition. There are no models describing common responses of main seed oil fatty acids to temperature in plants. The aim of thus work was to investigate common responses of seed oil fatty acids to minimum temperature during grain filling across species and genotypes. A database consisted of 164 genotypes of 9 species, sunflower, rapeseed, soybean, maize, flax, chia, safflower, olive and camelia, grown under a wide range of environmental conditions, was created and analyzed applying meta phenomics tools. Four widely sown species of the database was used to develop several common seed fatty acid responses and validate some models, and the other species were used to validate the General Model. The minimum temperature during grain filling responses of fatty acids in the General Model were close to responses found in genotypes of five independent species used to validate the model. Dissections of the general model by selecting the appropriate data allowed unraveling previously unknown features of the response of fatty acid to the minimum temperature during grain filling. The response of fatty acids to temperature for any species was unaffected by experimental conditions (field or controlled conditions) during the oil synthesis stage. The oleic acid trait did not affect the response to temperature of fatty acids synthesized downstream and upstream of it. Traits such as high stearic or high linoleic did not affect the response of fatty acids synthesized upstream or downstream of the trait. The established models and new knowledge could be applied to design cost effective and timely experiments to assess the potential responses of seed oil fatty acids to temperature of previously untested genotypes.

Metagenomics is a contemporary discipline in science that introduces a new horizon in biomolecular and genetic studies and provides a standard method for understanding biological diversity. In this context, we can only assess the functional potential of a certain community through metagenomics, but by understanding the metaphenomics, we can assign the functional genetic potential with respect to environmental resources. Metaphenomics studies the phenome of soil, plants or other organisms through a meta-analysis with the primary goal of establishing dose–response relationships across a wide range of phenotypic characteristics and abiotic environmental factors. Rather than conjecturing gene functions by metagenomes, the metaphenomic approaches can assist in understanding the substantial functioning of the microbiome. Coupling metagenomics with metaphenomics has the potential to overcome practical challenges in agriculture and ecological sustainability. On the grounds, this review was performed to present a deep understanding of soil metagenomics linked with metaphenomics, i.e. the phenotypic response of the soil microbiome. It also discusses how metagenomic applications and basic methods help study the phenotypic response of uncultured soil microbiomes to external factors. Metaphenomics can apply an untargeted multiomics approach to decipher the soil microbiome metaphenotypic response to environmental perturbations, and its application is also reviewed in this article. [ABSTRACT FROM AUTHOR]

Evidence-based decisions governing sustainable agricultural land management practices require a mechanistic understanding of soil organic matter (SOM) transformations and stabilization of carbon in soil. Large amounts of carbon from organic fertilizers, root exudates, and crop residues are input into agricultural soils. Microbes then catalyze soil biogeochemical processes including carbon extracellular transformation, mineralization, and assimilation of resources that are later returned to the soil as metabolites and necromass. A systems biology approach for a holistic study of the transformation of carbon inputs into stable SOM requires the use of soil “omics” platforms (metagenomics, metatranscriptomics, metaproteomics, and metabolomics). Linking the data derived from these various platforms will enhance our knowledge of structure and function of the microbial communities involved in soil carbon cycling and stabilization. In this review, we discuss the application, potential, and suitability of different “omics” approaches (independently and in combination) for elucidating processes involved in the transformation of stable carbon in soil. We highlight biases associated with these approaches including limitations of the methods, experimental design, and soil sampling, as well as those associated with data analysis and interpretation.

Fleshy fruits are very varied, whether in terms of their composition, physiology, or rate and duration of growth. To understand the mechanisms that link metabolism to phenotypes, which would help the targeting of breeding strategies, we compared eight fleshy fruit species during development and ripening. Three herbaceous (eggplant, pepper, and cucumber), three tree (apple, peach, and clementine) and two vine (kiwifruit and grape) species were selected for their diversity. Fruit fresh weight and biomass composition, including the major soluble and insoluble components, were determined throughout fruit development and ripening. Best-fitting models of fruit weight were used to estimate relative growth rate (RGR), which was significantly correlated with several biomass components, especially protein content (R =84), stearate (R =0.72), palmitate (R =0.72), and lignocerate (R =0.68). The strong link between biomass composition and RGR was further evidenced by generalized linear models that predicted RGR with R -values exceeding 0.9. Comparison of the fruit also showed that climacteric fruit (apple, peach, kiwifruit) contained more non-cellulosic cell-wall glucose and fucose, and more starch, than non-climacteric fruit. The rate of starch net accumulation was also higher in climacteric fruit. These results suggest that the way biomass is constructed has a major influence on performance, especially growth rate. [ABSTRACT FROM AUTHOR]