Design and characterization of synthetic fungal-bacterial consortia for direct production of isobutanol from cellulosic biomass

Abstract

Synergistic microbial communities are ubiquitous in nature and exhibit appealing features, such as sophisticated metabolic capabilities and robustness. This has inspired fast-growing interest in engineering synthetic microbial consortia for biotechnology development. However, there are relatively few reports of their use in real-world applications, and achieving population stability and regulation has proven to be challenging. In this work, we bridge ecology theory with engineering principles to develop robust synthetic fungal-bacterial consortia for efficient biosynthesis of valuable products from lignocellulosic feedstocks. The required biological functions are divided between two specialists: the fungus Trichoderma reesei, which secretes cellulase enzymes to hydrolyze lignocellulosic biomass into soluble saccharides, and the bacterium Escherichia coli, which metabolizes soluble saccharides into desired products. We developed and experimentally validated a comprehensive mathematical model for T. reesei/E. coli consortia, providing insights on key determinants of the system's performance. To illustrate the bioprocessing potential of this consortium, we demonstrate direct conversion of microcrystalline cellulose and pretreated corn stover to isobutanol. Without costly nutrient supplementation, we achieved titers up to 1.88 g/L and yields up to 62% of theoretical maximum. In addition, we show that cooperator-cheater dynamics within T. reesei/E. coli consortia lead to stable population equilibria and provide a mechanism for tuning composition. Although we offer isobutanol production as a proof-of-concept application, our modular system could be readily adapted for production of many other valuable biochemicals.

Keywords: consolidated bioprocessing; lignocellulosic biofuel; renewable energy.

Fig. 1.

Design and theoretical analysis of the TrEc consortium. (A) Schematic of the TrEc consortium. Key parameters, identified via sensitivity analysis (in B), are labeled (SI Appendix, section 1 and Table S1). T. reesei produces cellulases (CBHI, cellobiohydrolase I; CBHII, cellobiohydrolase II; EGI, endoglucanase I) that hydrolyze cellulose to soluble oligosaccharides. Oligosaccharides are further hydrolyzed to glucose via cell wall-localized β-glucosidase (BGL). Soluble saccharides serve as growth substrates for the microbes (cellobiose and glucose for T. reesei, glucose only for E. coli). E. coli ferments glucose into isobutanol, which inhibits microbial growth due to toxicity. (B) Global sensitivity analysis of the TrEc consortium model. PRCCs between model parameters and output metrics are shown with hierarchical clustering (Ward’s method, Pearson correlation distance). Parameters are labeled in A. Output metrics are as follows: , fraction of substrate carbon consumed by E. coli (grams per total grams); Q_I, isobutanol productivity (grams per gram of cellulose per hour); R_cel, mean cellulose hydrolysis rate (grams per liter per hour); R_Ec, mean E. coli growth rate (grams per liter per hour); R_Tr, mean T. reesei growth rate (grams per liter per hour); X_Ec, E. coli population fraction at fermentation end point (grams per gram of total microbial biomass); and , isobutanol yield (grams per gram of cellulose). The most significant PRCCs ( and ) are shown here; full results are provided in SI Appendix, Fig. S2. (C) Normalized kernel density estimate (200 × 200 grid, standard bivariate normal distribution kernel) for R_cel vs. over all sets of parameter and IC values sampled in sensitivity analysis. Individual points are shown in low-density (<1.25) regions. Axes are padded by 4% at each end to ensure visibility of all data. (D) Theoretical analysis of isobutanol production. Parameter values and ICs correspond to the point denoted by the white asterisk in C, with F_a, fraction of substrate bonds accessible to enzymes, modified to 0.011. Numerical solutions were calculated over a range of initial E. coli fraction, , values. Key fermentation metrics are shown (R_cel, , and Q_I); the green dashed line denotes the theoretical maximum of (0.41 g/g). More details are provided in SI Appendix, section 2.

Fig. 2.

Experimental and model analysis of T. reesei (Tr) RUTC30 monoculture and bicultures of T. reesei RUTC30 with E. coli (Ec) K12 or E. coli NV3 pSA55/69. Error bars are ±SD for n = 3 technical replicates. (A) T. reesei RUTC30 monoculture on 20 g/L MCC. Modeling results are shown as smooth lines, and experimental results are shown as points. (B) RUTC30/K12 biculture on 10 g/L MCC. (C) RUTC30/NV3 biculture on 20 g/L MCC. E. coli data points are color-coded to indicate population fraction retaining plasmids pSA55/69 (error bars are shown in SI Appendix, Fig. S4C). (D) Fermentation product titers for NV3 monoculture (20 g/L glucose) vs. biculture experiment in C. Error bars are ±SD for n = 2 biological replicates. Acet, acetate; EtOH, ethanol; iButOH, isobutanol; Succ, succinate. (E) Local sensitivity analysis of the RUTC30/NV3 parameter set. The model was integrated with one-at-a-time ±25% perturbations to each parameter/IC (SI Appendix, section 2). Parameter effects were quantified by response coefficients, defined as , where is the percentage change in output Z and is the percentage change in parameter X. Response coefficients for and R_cel are shown for top 10 parameters (ranked by ), with the color code indicating the direction of the parameter/IC perturbation for the plotted response.

Fig. 3.

Isobutanol production with RUTC30/NV3 bicultures on 20 g/L AFEX pretreated CS (21). Error bars are ±SD for n = 2 biological replicates. (A) Isobutanol concentration over time. (B) Carbohydrate conversion (percentage consumed) and isobutanol yield. ara, arabinose; cry cel, crystalline cellulose; glc, hemicellulose-derived glucose; Inoc, inoculation; xyl, xylose. For reference, relative proportions of major AFEX pretreated CS carbohydrates are shown to the right; more details are provided in SI Appendix, Table S7.

Fig. 4.

Cooperator–cheater dynamics in TrEc consortia. (A) Stability analysis of the TrEc model. Equilibrium population compositions were calculated over a range of plausible values for and . (B) Sample numerical solutions for and over a range of . (C) Experimentally observed dynamics for RUTC30/K12 bicultures at pH 5.3 . Cultures were inoculated with high or low (red or green points/lines, respectively) in duplicate (circles/squares). Data are shown for each culture, and error bars are ±SD for n = 2 technical replicates. (D) RUTC30/NV3 bicultures at pH 6.0 . (E) RUTC30/K12 bicultures at pH 6.0 .