Development of a Genetically Modified Silage Inoculant for the Biological Pretreatment of Lignocellulosic Biomass

Open Access
Author:
Speer, Michael Arthur
Graduate Program:
Agricultural and Biological Engineering
Degree:
Doctor of Philosophy
Document Type:
Dissertation
Date of Defense:
March 06, 2012
Committee Members:
  • Thomas Lehman Richard, Dissertation Advisor
  • Thomas Lehman Richard, Committee Chair
  • Howard M Salis, Committee Member
  • Ming Tien, Committee Member
  • Maryann Victoria Bruns, Committee Member
  • Patrick C Cirino, Special Member
Keywords:
  • Silage
  • Lactobacillus
  • Biomass
  • Bioenergy
  • Digestibility
Abstract:
In this work, novel strains of Lactobacillus plantarum were developed to provide enzymatic pretreatment to lignocellulosic biomass during ensilage. This pretreatment was catalyzed by the in process production of ferulic acid esterases. The substrates that these novel microorganisms were used to ensile were winter rye and corn stover. On winter rye, these inoculants were able to significantly increase the enzymatic digestibility of the silage by 11.1% ± 3.2% (P=0.05) compared to a control treatment. If combined with a subsequent thermochemical pretreatment, the benefit of inoculation with FAE producing strains of L. plantarum increased to 18.7% ± 5.0% (P=0.05). On corn stover modified strains produced no benefit for the raw silage, but after thermochemical pretreatment, the biologically treated silage had an increase in digestibility of over 30% (P=0.05) when compared to a control treatment. Furthermore, this work focused on the balance between heterologous expression and the ability to maintain a robust, competitive organism in a natural system. This work found that strains producing a heavy amount of heterologous esterase achieved a lower population in the fermented silage that strains producing lower amounts of esterase. In addition, this work found that using acid-inducible promoters to drive heterologous expression enabled the strain to achieve higher final populations. In final, this work presents a mathematical model for optimizing the balance between enzymatic production and survival to deliver the maximum amount of enzyme using a given organism.