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Abstract Background The chemoautotrophic bacterium Ralstonia eutropha can utilize H2/CO2 for growth under aerobic conditions. While this microbial host has great potential to be engineered to produce desired compounds (beyond polyhydroxybutyrate) directly from CO2, little work has been done to develop genetic part libraries to enable such endeavors. Results We report the development of a toolbox for the metabolic engineering of Ralstonia eutropha H16. We have constructed a set of broad-host-range plasmids bearing a variety of origins of replication, promoters, 5’ mRNA stem-loop structures, and ribosomal binding sites. Specifically, we analyzed the origins of replication pCM62 (IncP), pBBR1, pKT (IncQ), and their variants. We tested the promoters PBAD, T7, Pxyls/PM, PlacUV5, and variants thereof for inducible expression. We also evaluated a T7 mRNA stem-loop structure sequence and compared a set of ribosomal binding site (RBS) sequences derived from Escherichia coli, R. eutropha, and a computational RBS design tool. Finally, we employed the toolbox to optimize hydrocarbon production in R. eutropha and demonstrated a 6-fold titer improvement using the appropriate combination of parts. Conclusion We constructed and evaluated a versatile synthetic biology toolbox for Ralstonia eutropha metabolic engineering that could apply to other microbial hosts as well.

Abstract Background Thermophilic fungi have attracted increased interest for their ability to secrete enzymes that deconstruct biomass at high temperatures. However, development of thermophilic fungi as enzyme producers for biomass deconstruction has not been thoroughly investigated. Comparing the enzymatic activities of thermophilic fungal strains that grow on targeted biomass feedstocks has the potential to identify promising candidates for strain development. Thielavia terrestris and Thermoascus aurantiacus were chosen for characterization based on literature precedents. Results Thermoascus aurantiacus and Thielavia terrestris were cultivated on various biomass substrates and culture supernatants assayed for glycoside hydrolase activities. Supernatants from both cultures possessed comparable glycoside hydrolase activities when incubated with artificial biomass substrates. In contrast, saccharifications of ionic liquid pretreated switchgrass (Panicum virgatum) revealed that T. aurantiacus enzymes released more glucose than T. terrestris enzymes over a range of protein mass loadings and temperatures. Temperature-dependent saccharifications demonstrated that the T. aurantiacus proteins retained higher levels of activity compared to a commercial enzyme mixture sold by Novozymes, Cellic CTec2, at elevated temperatures. Enzymes secreted by T. aurantiacus released glucose at similar protein loadings to CTec2 on dilute acid, ammonia fiber expansion, or ionic liquid pretreated switchgrass. Proteomic analysis of the T. aurantiacus culture supernatant revealed dominant glycoside hydrolases from families 5, 7, 10, and 61, proteins that are key enzymes in commercial cocktails. Conclusions T. aurantiacus produces a complement of secreted proteins capable of higher levels of saccharification of pretreated switchgrass than T. terrestris enzymes. The T. aurantiacus enzymatic cocktail performs at the same level as commercially available enzymatic cocktail for biomass deconstruction, without strain development or genetic modifications. Therefore, T. aurantiacus provides an excellent platform to develop a thermophilic fungal system for enzyme production for the conversion of biomass to biofuels.

Abstract Recovery of ribosomal small subunit genes by assembly of short read community DNA sequence data generally fails, making taxonomic characterization difficult. Here, we solve this problem with a novel iterative method, based on the expectation maximization algorithm, that reconstructs full-length small subunit gene sequences and provides estimates of relative taxon abundances. We apply the method to natural and simulated microbial communities, and correctly recover community structure from known and previously unreported rRNA gene sequences. An implementation of the method is freely available at https://github.com/csmiller/EMIRGE.

Producing cellulosic biofuels from plant material has recently emerged as a key US Department of Energy goal. For this technology to be commercially viable on a large scale, it is critical to make production cost efficient by streamlining both the deconstruction of lignocellulosic biomass and fuel production. Many natural ecosystems efficiently degrade lignocellulosic biomass and harbor enzymes that, when identified, could be used to increase the efficiency of commercial biomass deconstruction. However, ecosystems most likely to yield relevant enzymes, such as tropical rain forest soil in Puerto Rico, are often too complex for enzyme discovery using current metagenomic sequencing technologies. One potential strategy to overcome this problem is to selectively cultivate the microbial communities from these complex ecosystems on biomass under defined conditions, generating less complex biomass-degrading microbial populations. To test this premise, we cultivated microbes from Puerto Rican soil or green waste compost under precisely defined conditions in the presence dried ground switchgrass (Panicum virgatum L.) or lignin, respectively, as the sole carbon source. Phylogenetic profiling of the two feedstock-adapted communities using SSU rRNA gene amplicon pyrosequencing or phylogenetic microarray analysis revealed that the adapted communities were significantly simplified compared to the natural communities from which they were derived. Several members of the lignin-adapted and switchgrass-adapted consortia are related to organisms previously characterized as biomass degraders, while others were from less well-characterized phyla. The decrease in complexity of these communities make them good candidates for metagenomic sequencing and will likely enable the reconstruction of a greater number of full-length genes, leading to the discovery of novel lignocellulose-degrading enzymes adapted to feedstocks and conditions of interest.