Autotrophic adaptive laboratory evolution of the acetogenClostridium autoethanogenumdelivers the gas-fermenting strain LAbrini with superior growth, products, and robustness

Abstract

Microbes able to convert gaseous one-carbon (C1) waste feedstocks are of growing importance in transitioning to the biosustainable production of renewable chemicals and fuels. Acetogens are particularly interesting biocatalysts since gas fermentation usingClostridium autoethanogenumhas already been commercialised. Most non-commercial acetogen strains, however, need complex nutrients, display slow growth, and are not sufficiently robust for routine bioreactor fermentations. In this work, we used three adaptive laboratory evolution (ALE) strategies to evolve the wild-type model-acetogenC. autoethanogenumto grow faster, without complex nutrients and to be robust in operation of continuous bioreactor cultures. Seven evolved strains with improved phenotypes were isolated on minimal medium with one strain, named "LAbrini" (LT1), exhibiting superior performance in terms of the maximum specific growth rate, product profile, and robustness in continuous cultures. Differing performance of the strains between bottle batch and continuous cultures shows the importance of testing novel strains in industrially relevant continuous fermentation conditions. Interestingly, a very distinct transcriptome profile linked to a potential CO toxicity phenotype was observed in bioreactor cultures for one evolved strain. Whole-genome sequencing of the seven evolved strains identified 25 mutations with two genomic regions under stronger evolutionary pressure. Our analysis also suggests that the genotypic changes that are potentially responsible for the improved phenotypes may serve as useful candidates for metabolic engineering of cell factories. This work provides the robustC. autoethanogenumstrain LAbrini to the academic community to speed up phenotyping and genetic engineering, improve quantitative characterisation of acetogen metabolism, and facilitate the generation of high-quality steady-state datasets.

Deletion of genes linked to the C1-fixing gene cluster affects growth, by-products, and proteome ofClostridium autoethanogenum

“…Interestingly, while LAbrini maintained the acetate-to-ethanol ratio at ~1 on both media, the ratio was higher for the Δhp strain (~5 vs ~1) and lower for the Δnbp strain (~2 vs ~11) on rich medium compared to minimal medium during autotrophic growth (Figure 5C). These ratios are in the range of those previously seen in autotrophic batch and continuous bioreactor cultures of C. autoethanogenum (Ingelman et al, 2023;Valgepea et al, 2017aValgepea et al, , 2018. Our results imply that both hp and nbp genes play a notable role in carbon distribution between the two major by-products -acetate and ethanol -during autotrophic batch growth of C. autoethanogenum.…”

“…In addition, several oxidoreductases (05235, 14800, and 18000; ∼2-fold, q < 0.03) and sigma-54-related transcriptional regulators (14025 and 00450; ∼3.2- and 1.6-fold, q < 0.05) were up-regulated in the deletion strains. Also noteworthy is the ∼1.8-fold (q < 0.01) up-regulation of a PocR ligand-binding domain-containing protein (16380) in both Δ hp and Δ nbp strains, as we recently identified a missense mutation in the PocR gene of a CO-evolved C. autoethanogenum strain obtained via adaptive laboratory evolution (Ingelman et al, 2023). Moreover, PocR has an alternative annotation in C. autoethanogenum as a histidine kinase (Humphreys et al, 2015) that regulates sporulation and metabolism in Clostridia (Xin et al, 2020).…”

“…A derivate of C. autoethanogenum DSM 10061 strain – DSM X – deposited in the German Collection of Microorganisms and Cell Cultures (DSMZ) named “LAbrini” (Ingelman et al, 2023), was stored as a glycerol stock at −80°C and used as the base strain for genetic engineering. Escherichia coli strains NEB Turbo and NEB Express (New England BioLabs) were used for cloning, plasmid assembly, and propagation.…”

“…The MS was operated using a data-independent acquisition (DIA) approach (Gillet et al, 2012) DIA MS data analysis and differential protein expression analysis DIA MS data was analysed using version 1.8 of the software suite DIA-NN (Demichev et al, 2019) with default settings. The spectral library was generated in silico using smart profiling from the protein sequence database of C. autoethanogenum LAbrini (NCBI Genbank CP110420) (Ingelman et al, 2023) with a precursor of 1% false discovery rate (FDR) filter. The following settings were used for analysis: full trypsin specificity with 1 missed cleavage allowed for peptides with a length of 7-30…”

“…However, gene-editing tools for acetogens have rapidly improved recently (Bourgade et al, 2021; Jin et al, 2020; Pavan et al, 2022). We thus aimed to take advantage of these developments to shed light on the functionalities of the hp (CAETHG_1604) and the CooT family nickel-binding gene nbp (CAETHG_1605), referred to as LABRINI_07945 for hp and LABRINI_07950 for nbp based on the C. autoethanogenum base strain “LAbrini” used in this work (Ingelman et al, 2023).…”