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Prospecting for Energy-Rich Renewable Raw Materials: Sorghum Stem Case Study.

Caitlin S ByrtNatalie S BettsHwei-Ting TanWai Li LimRiksfardini A ErmawarHai Yen NguyenNeil J ShirleyJelle LahnsteinKendall CorbinGeoffrey B FincherVic KnaufRachel A Burton
Published in: PloS one (2016)
Sorghum vegetative tissues are becoming increasingly important for biofuel production. The composition of sorghum stem tissues is influenced by genotype, environment and photoperiod sensitivity, and varies widely between varieties and also between different stem tissues (outer rind vs inner pith). Here, the amount of cellulose, (1,3;1,4)-β-glucan, arabinose and xylose in the stems of twelve diverse sorghum varieties, including four photoperiod-sensitive varieties, was measured. At maturity, most photoperiod-insensitive lines had 1% w/w (1,3;1,4)-β-glucan in stem pith tissue whilst photoperiod-sensitive varieties remained in a vegetative stage and accumulated up to 6% w/w (1,3;1,4)-β-glucan in the same tissue. Three sorghum lines were chosen for further study: a cultivated grain variety (Sorghum bicolor BTx623), a sweet variety (S. bicolor Rio) and a photoperiod-sensitive wild line (S. bicolor ssp. verticilliflorum Arun). The Arun line accumulated 5.5% w/w (1,3;1,4)-β-glucan and had higher SbCslF6 and SbCslH3 transcript levels in pith tissues than did photoperiod-insensitive varieties Rio and BTx623 (<1% w/w pith (1,3;1,4)-β-glucan). To assess the digestibility of the three varieties, stem tissue was treated with either hydrolytic enzymes or dilute acid and the release of fermentable glucose was determined. Despite having the highest lignin content, Arun yielded significantly more glucose than the other varieties, and theoretical calculation of ethanol yields was 10 344 L ha-1 from this sorghum stem tissue. These data indicate that sorghum stem (1,3;1,4)-β-glucan content may have a significant effect on digestibility and bioethanol yields. This information opens new avenues of research to generate sorghum lines optimised for biofuel production.
Keyphrases
  • gene expression
  • cell wall
  • ionic liquid
  • type diabetes
  • machine learning
  • data analysis
  • single cell
  • artificial intelligence
  • rna seq