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Omics-Based Analyses Revealed Metabolic Responses of Clostridium Acetobutylicum to Lignocellulose-Derived Inhibitors Furfural, F

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Omics-Based Analyses Revealed Metabolic Responses of Clostridium Acetobutylicum to Lignocellulose-Derived Inhibitors Furfural, F Liu et al. Biotechnol Biofuels (2019) 12:101 https://doi.org/10.1186/s13068-019-1440-9 Biotechnology for Biofuels RESEARCH Open Access Omics-based analyses revealed metabolic responses of Clostridium acetobutylicum to lignocellulose-derived inhibitors furfural, formic acid and phenol stress for butanol fermentation Huanhuan Liu1,2, Jing Zhang1,2, Jian Yuan3, Xiaolong Jiang3, Lingyan Jiang3, Guang Zhao4, Di Huang3* and Bin Liu3* Abstract Background: Clostridium acetobutylicum is a model fermentative anaerobe for consolidated bioprocessing of ligno- cellulose hydrolysates into acetone–butanol–ethanol (ABE). However, the main inhibitors (acids, furans and phenols) ubiquitous in lignocellulose hydrolysates strictly limit the conversion efciency. Thus, it is essential to understand the underlying mechanisms of lignocellulose hydrolysate inhibitors to identify key industrial bottlenecks that undermine efcient biofuel production. The recently developed omics strategy for intracellular metabolites and protein quantif- cation now allow for the in-depth mapping of strain metabolism and thereby enable the resolution of the underlying mechanisms. Results: The toxicity of the main inhibitors in lignocellulose hydrolysates against C. acetobutylicum and ABE produc- tion was systematically investigated, and the changes in intracellular metabolism were analyzed by metabolomics and proteomics. The toxicity of the main lignocellulose hydrolysate inhibitors at the same dose was ranked as fol- lows: formic acid > phenol > furfural. Metabolomic analysis based on weighted gene coexpression network analysis (WGCNA) revealed that the three inhibitors triggered the stringent response of C. acetobutylicum. Proteomic analysis based on peptide mass spectrometry (MS) supported the above results and provided more comprehensive conclu- sions. Under the stress of three inhibitors, the metabolites and key enzymes/proteins involved in glycolysis, reductive tricarboxylic acid (TCA) cycle, acetone–butanol synthesis and redox metabolism were lower than those in the control group. Moreover, proteins involved in gluconeogenesis, the oxidative TCA cycle, thiol peroxidase (TPX) for oxida- tive stress were signifcantly upregulated, indicating that inhibitor stress induced the stress response and metabolic regulation. In addition, the three inhibitors also showed stress specifcity related to fatty acid synthesis, ATP synthesis, nucleic acid metabolism, nicotinic acid metabolism, cell wall synthesis, spore synthesis and fagellum synthesis and so on. Conclusions: Integrated omics platforms provide insight into the cellular responses of C. acetobutylicum to cytotoxic inhibitors released during the deconstruction of lignocellulose. This insight allows us to fully improve the strain to adapt to a challenging culture environment, which will prove critical to the industrial efcacy of C. acetobutylicum. Keywords: Clostridium acetobutylicum, Lignocellulose hydrolysate inhibitors, Biofuel, Proteomics, Metabolomics *Correspondence: [email protected]; [email protected] 3 TEDA School of Biological Sciences and Biotechnology, Nankai University, TEDA, Tianjin 300457, China Full list of author information is available at the end of the article © The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creat iveco mmons .org/ publi cdoma in/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Liu et al. Biotechnol Biofuels (2019) 12:101 Page 2 of 20 Introduction crops, etc. as the substrates [13]. In previous studies, fur- Te traditional acetone–butanol–ethanol (ABE) fermen- fural and 5-hydroxymethyl furfural promoted the growth tation based on Clostridium acetobutylicum is ranked of Cupriavidus basilensis [14] and C. acetobutylicum [15] as the second largest in industrial fermentation, behind at low concentrations; Furfural and 5-hydroxymethyl bioethanol production. Biobutanol, a renewable energy furfural (3 g/L) were found to be stimulatory rather than fuel, has recently received considerable attention because inhibitory to C. beijerinckii BA101, although the mixture of its higher energy density, higher combustion heat, bet- of the two negatively afected the culture [16]; 1.0 g/L ter engine compatibility and less corrosivity than those phenolic compounds inhibited up to 64–74% of C. bei- of bioethanol [1]. As the environment deteriorates and jerinckii cell growth and completely inhibited the butanol food shortages expand, ABE fermentation using nongrain production [7]; Up to 80 mM sodium acetate promoted raw materials (sugar industry waste, lignocellulose, glyc- the growth of strain and stabilized butanol production by erin and syngas) as the substrates is being used by the preventing the strain degeneration of C. beijerinckii [17]. biobutanol industry [2]. Among the potential substrates, In addition, C. acetobutylicum was more sensitive to for- lignocelluloses, such as corn stover, wheat straw, switch- mic acid compared with C. beijerinckii [18], and 0.1 M grass and sweet sorghum slag, are the most attractive formic acid induced the “acid crash” and completely biomass raw materials due to their low price, wide range inhibited butanol production and cell growth in C. ace- of sources and renewability [3]. Te efective develop- tobutylicum [19]. Terefore, diferent strains have difer- ment of lignocellulosic materials will also signifcantly ent tolerance levels and stress responses to inhibitors, reduce the dependence on the petrochemical industry and diferent raw materials or hydrolysis processes gen- and improve the comprehensive utilization of agricul- erate diferent amounts and species of inhibitors [6, 20], tural waste. which creates problems in the elucidation of metabolic However, due to the “recalcitrance” of lignocellulosic response to inhibiting compounds present in lignocel- biomass [4], the raw materials are usually subjected to lulose materials at diferent cellular levels (metabolites, pretreatment under acidic, alkaline, steam and other proteins, etc.) to identify potential targets which could harsh conditions and then hydrolyzed by cellulase into be addressed to alleviate inhibition. Terefore, a system- monosaccharides (mainly hexoses and pentoses) for atic investigation to ascertain the mechanism used by microbial fermentation. It is believed that the pretreat- butanol-producing strain in the presence of lignocellu- ment and hydrolysis of lignocellulosic feedstocks pro- losic hydrolysate inhibitors is urgently needed. duce a range of substances that are toxic to microbial Te development of omics technology provides a the- cells [5], such as furfural, 5-hydroxymethyl furfural, for- oretical basis for understanding the metabolic response mic acid, acetic acid, phenol, catechol, ferulic acid, syrin- mechanism of microorganisms under various conditions galdehyde, vanillin and coumarin [6, 7]. Tese inhibitors and eliminating the production bottleneck of target prod- are generally classifed into three categories according to ucts. Great eforts have been made to dissect the meta- their sources and properties, namely, furans, weak acids bolic mechanism of C. acetobutylium by transcriptomics and phenols [5]. Te presence of these compounds will [21, 22], proteomics [23, 24] and metabolomics [25, 26]. inhibit cell growth, substrate utilization and product syn- At the same time, the methods based on mathematical thesis, thus greatly reducing the production efciency of statistical analysis and bioinformation analysis have been lignocellulosic butanol. Tere have been many reports developed and matured rapidly, such as principal compo- on the pretreatment, hydrolysis, detoxifcation and fer- nent analysis (PCA) [27], partial least square regression mentation of lignocellulose [6, 8, 9], but most of them (PLS regression) [28], weighted gene co-expression net- focus on the optimization of hydrolysis and fermentation work analysis (WGCNA) [29, 30], gene ontology (GO) processes and adopt the hydrolysis methods, explosion [31], KEGG pathway [32], all of which provide a strong conditions, and detoxifcation processes used in ligno- technical foundation for data mining. Among them, cellulosic ethanol production. Research on lignocellu- WGCNA is a method for the construction of biological lose-derived inhibitors has focused on Saccharomyces networks based on pairwise correlations between varia- cerevisiae and Zymomonas mobilis [10–12] and so on, bles. It allows one to defne modules (clusters), intramod- which has promoted the tremendous development of the ular hubs, and network nodes with regard to module bioethanol industry. membership, to explore the relationships between co- To date, few natural strains can efciently utilize lig- expression modules, and to compare the network topol- nocellulosic hydrolysate to produce butanol, and due to ogy of diferent networks (diferential network analysis) an insufcient understanding of the inhibition mecha- [29, 30]. Te WGCNA method is widely used in the func- nism, butanol production is lower than that of traditional tional genomics, especially in the transcriptomic analysis substrate fermentation
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