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Planting Season Impacts Sugarcane Stem Development, Secondary Metabolite Levels, and Natural Antisense Transcription.

Maryke WijmaCarolina Gimiliani LembkeAugusto Lima DinizLuciane SantiniLeonardo Zambotti-VillelaPio ColepicoloMonalisa Sampaio CarneiroGlaucia Mendes Souza
Published in: Cells (2021)
To reduce the potentially irreversible environmental impacts caused by fossil fuels, the use of renewable energy sources must be increased on a global scale. One promising source of biomass and bioenergy is sugarcane. The study of this crop's development in different planting seasons can aid in successfully cultivating it in global climate change scenarios. The sugarcane variety SP80-3280 was field grown under two planting seasons with different climatic conditions. A systems biology approach was taken to study the changes on physiological, morphological, agrotechnological, transcriptomics, and metabolomics levels in the leaf +1, and immature, intermediate and mature internodes. Most of the variation found within the transcriptomics and metabolomics profiles is attributed to the differences among the distinct tissues. However, the integration of both transcriptomics and metabolomics data highlighted three main metabolic categories as the principal sources of variation across tissues: amino acid metabolism, biosynthesis of secondary metabolites, and xenobiotics biodegradation and metabolism. Differences in ripening and metabolite levels mainly in leaves and mature internodes may reflect the impact of contrasting environmental conditions on sugarcane development. In general, the same metabolites are found in mature internodes from both "one-year" and "one-and-a-half-year sugarcane", however, some metabolites (i.e., phenylpropanoids with economic value) and natural antisense transcript expression are only detected in the leaves of "one-year" sugarcane.
Keyphrases
  • climate change
  • single cell
  • mass spectrometry
  • ms ms
  • amino acid
  • drinking water
  • poor prognosis
  • rna seq
  • wastewater treatment
  • big data
  • binding protein
  • machine learning
  • anaerobic digestion
  • essential oil