Developing a systems biology framework to engineer anaerobic gut fungi

Lignocellulose is a complex, energy-rich heterogenous polymer composed of cellulose (40-50%), hemicellulose (20-40%) and lignin (20-35%). Using the more than 1.6 billion tons of agricultural lignocellulosic waste generated worldwide each year is a promising avenue to explore for sustainable bioprocessing. While lignocellulose is abundant, lignin acts as a protective barrier that prevents its decomposition into fermentable sugars and poses significant challenges for the utilization of this energy-rich resource in biotechnological applications. On the other hand, anaerobic gut fungi specialize in bio-converting unpretreated lignocellulose into fermentable sugar monomers and represent a promising opportunity to exploit lignocellulosic plant biomass for bioprocesses.Anaerobic gut fungi typically inhabit the digestive tracts of herbivores where they play an integral role in the decomposition of raw lignocellulose into its constitutive sugar monomers. The genomes of these fungi encode for the highest diversity, and largest number, of lignocellulolytic enzymes of any sequenced fungus to date. This, in combination with their filamentous morphology, causes them to excel at lignocellulose decomposition. Despite these advantages, anaerobic gut fungi are not utilized in bioprocesses due to challenges in cultivating and engineering them. In this thesis a marriage between experimental and multi-omic datasets is used to develop techniques that can be used to better understand and engineer anaerobic gut fungi for lignocellulose decomposition in upstream bioprocesses. A comprehensive metagenomic enrichment of goat fecal pellets was undertaken to better understand how rumen microbiome-based cultures respond to different environmental stressors. The outsize role anaerobic gut fungi play in these systems was highlighted by the markedly different way in which lignocellulosic carbon was metabolized to different fermentation products when anaerobic fungi were present. Overall, the analysis elucidated a natural compartmentalization that occurs between anaerobes during the degradation of lignocellulose, suggesting design rules that can be used to funnel carbon to different end-products based on the composition of the microbial consortia.Despite the importance of anaerobic gut fungi in lignocellulolytic systems, no stable genetic engineering tools have been developed for this class of fungi to facilitate strain optimization. This is partially due to several unique genomic traits possessed by the gut fungi, namely an extreme bias towards AT bases in their genomes, as well as a disproportionate abundance of repetitive genomic regions. By making use of omics databases, the consequences of these features were investigated. It was found that the carbohydrate active enzymes encoded for by the gut fungi are likely heavily glycosylated, which has ramifications for heterologous expression strategies. A novel codon optimization table was also introduced to facilitate the quest to genetically engineer these fungi.Genome-scale models form a cornerstone of modern metabolic engineering strategies, due to their ability to accurately model the metabolism of an organism from first principles. These models are most often made for well characterized organisms, but there is great benefit in constructing such a model of an anaerobic gut fungus due to its ability to act as a scaffold to build understanding upon. To this end, the first genome-scale model of an anaerobic gut fungus was constructed using a combination of experimental and omics data. This model captures the primary metabolism of Neocallimastix lanati, a novel anaerobic gut fungus isolate, and sheds light on the inner workings of the carbon metabolism unique to the gut fungi.Furthermore, genome-scale models can also be used to predict growth rate characteristics of organisms in silico. This aspect can be particularly useful when screening microbes for the development of stable consortia with the anaerobic gut fungi. A novel dynamic flux balance analysis algorithm, specifically geared towards the anaerobic gut fungi, was developed for this purpose. It was found that methanogens are likely the best partners due to their ability to metabolize by-products of the gut fungi and not compete with them for resources.Finally, due to challenges associated with cultivating the anaerobic gut fungi, indirect measurements are typically used to infer their growth rate. These usually take the form of pressure measurements, performed with a digital handheld pressure transducer. While high resolution experiments afford the most insight into the impact of environmental perturbations on the growth rate of the fungi, these are very labor and time intensive. An automatic pressure measurement and venting device was designed and built to automate this process. Beyond the time savings afforded by the device, an extremely high-resolution growth curve can now be automatically constructed, shedding light on the growth dynamics of the anaerobic gut fungi.In sum this thesis combines experimental and omics data to yield new insights in the behavior of anaerobic gut fungi and paves the way for their exploitation in biotechnology