Feedstock variability impacts the bioconversion of sugar and lignin streams derived from corn stover by Clostridium tyrobutyricum and engineered Pseudomonas putida

Abstract

Feedstock variability represents a challenge in lignocellulosic biorefineries, as it can influence both lignocellulose deconstruction and microbial conversion processes for biofuels and biochemicals production. The impact of feedstock variability on microbial performance remains underexplored, and predictive tools for microbial behaviour are needed to mitigate risks in biorefinery scale-up. Here, twelve batches of corn stover were deconstructed via deacetylation, mechanical refining, and enzymatic hydrolysis to generate lignin-rich and sugar streams. These batches and their derived streams were characterised to identify their chemical components, and the streams were used as substrates for producing muconate and butyrate by engineered Pseudomonas putida and wildtype Clostridium tyrobutyricum, respectively. Bacterial performance (growth, product titers, yields, and productivities) differed among the batches, but no strong correlations were identified between feedstock composition and performance. To provide metabolic insights into the origin of these differences, we evaluated the effect of twenty-three isolated chemical components on these microbes, including three components in relevant bioprocess settings in bioreactors, and we found that growth-inhibitory concentrations were outside the ranges observed in the streams. Overall, this study generates a foundational dataset on P. putida and C. tyrobutyricum performance to enable future predictive models and underscores their resilience in effectively converting fluctuating lignocellulose-derived streams into bioproducts.

FIGURE 1

Bioconversion of corn stover to biochemicals and biofuel precursors. The deconstruction process (DMR‐EH) of corn stover (top), the generation of lignin‐rich and sugar streams (middle), and biocatalysts used in this study (bottom) are shown. BCD, base‐catalysed depolymerisation. * Solids were washed with H₂O. Figure created with BioRender.com.

FIGURE 2

Origin and characterisation of corn stover bales utilised in the current study. (A) Description of field location and size reduction strategy. The size reduction methods are as follows: Method 1: Hammer mill, 3″ screen, followed by Knife mill, 1″ screen; Method 2 = Knife mill, 3/4″ screen; Method 3 = Hammer mill, 3/4″ screen. (B) Characterisation of the twelve feedstocks. Numeric data are also provided in Table S1. (C) Illustration of samples enriched with different anatomical fractions (from whole material, F6) utilised in the current study (F6_cob, F6_husk, F6_stalk, F6) before and after milling. (D) Illustration of corn stover bales representing different stages of degradation from which samples F7_mild, F7_moderate, and F7_severe were obtained.

FIGURE 3

Chemical characterization of black liquors and sugar hydrolysates derived from DMR‐EH of twelve corn stover samples. Data were obtained by high‐performance liquid chromatography (HPLC), inductively coupled plasma optical emission spectroscopy (ICP‐OES), anion scan, or ammonia analyses, depending on the analyte (see Materials and Methods). Numerical data are also provided in Tables S2–S4.

FIGURE 4

Bioconversion of black liquor, BCD liquor, and sugar hydrolysates. Cultivation profiles of (A–D) P. putida CJ781 in black liquor, (E–H) P. putida CJ781 in BCD liquor, and (I–L) C. tyrobutyricum in sugar hydrolysates. (A, E, I) Microbial growth, (B, F, J) product accumulation, and (C–D, G–H, K–L) product precursor utilisation are shown. Data represent the average of three biological replicates and error bars show ±SEM.

FIGURE 5

Quantification of (A) acetate and (B) lignin content in black liquors. Analyses were conducted in black liquors from 12 corn stover batches before and after 72‐h cultivation with P. putida CJ781. Results show the average of three biological replicates, and the error bars indicate ±SEM.

FIGURE 6

Performance metrics of P. putida CJ781 and C. tyrobutyricum in 0.5‐L bioreactors in the presence of CMAs. Performance of (A–D) P. putida in mock black liquor and (E–H) C. tyrobutyricum in mock sugar hydrolysates, both amended with corresponding EC25 or EC75 concentrations of CMAs. EC25 and EC75 concentrations were (respectively): 122 and 349 mM for sodium nitrate (NaNO₃), 133 and 268 mM for sodium sulphate (NaSO₄), 156 and 450 mM for sodium chloride (NaCl), 113 and 312 mM for sodium lactate (LAC), 105 and 304 mM for ammonium sulphate (AS), and 2.2 and 7.8 mM for sodium p‐coumarate (pCA). (A, E) Maximum titer, (B, F) productivity, (C, G) yield, and (D, H) maximum growth rate are shown. Full bioreactor profiles are shown in Figures S3 and S4. Bars represent the average of two biological replicates, except in the cases of P. putida CJ781 Control and NaNO₃_EC25, which were performed in triplicate. Error bars show absolute differences between duplicates or ±SEM for triplicates. Asterisks denote conditions that are significantly different from the control at p < 0.01.