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Genome-Based Engineering of Ligninolytic Enzymes in Fungi

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Genome-Based Engineering of Ligninolytic Enzymes in Fungi Asemoloye et al. Microb Cell Fact (2021) 20:20 https://doi.org/10.1186/s12934-021-01510-9 Microbial Cell Factories REVIEW Open Access Genome-based engineering of ligninolytic enzymes in fungi Michael Dare Asemoloye1, Mario Andrea Marchisio1*, Vijai Kumar Gupta2 and Lorenzo Pecoraro1* Abstract Background: Many fungi grow as saprobic organisms and obtain nutrients from a wide range of dead organic mate- rials. Among saprobes, fungal species that grow on wood or in polluted environments have evolved prolifc mecha- nisms for the production of degrading compounds, such as ligninolytic enzymes. These enzymes include arrays of intense redox-potential oxidoreductase, such as laccase, catalase, and peroxidases. The ability to produce ligninolytic enzymes makes a variety of fungal species suitable for application in many industries, including the production of bio- fuels and antibiotics, bioremediation, and biomedical application as biosensors. However, fungal ligninolytic enzymes are produced naturally in small quantities that may not meet the industrial or market demands. Over the last decade, combined synthetic biology and computational designs have yielded signifcant results in enhancing the synthesis of natural compounds in fungi. Main body of the abstract: In this review, we gave insights into diferent protein engineering methods, including rational, semi-rational, and directed evolution approaches that have been employed to enhance the production of some important ligninolytic enzymes in fungi. We described the role of metabolic pathway engineering to optimize the synthesis of chemical compounds of interest in various felds. We highlighted synthetic biology novel techniques for biosynthetic gene cluster (BGC) activation in fungo and heterologous reconstruction of BGC in microbial cells. We also discussed in detail some recombinant ligninolytic enzymes that have been successfully enhanced and expressed in diferent heterologous hosts. Finally, we described recent advance in CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)-Cas (CRISPR associated) protein systems as the most promising biotechnology for large-scale production of ligninolytic enzymes. Short conclusion: Aggregation, expression, and regulation of ligninolytic enzymes in fungi require very complex procedures with many interfering factors. Synthetic and computational biology strategies, as explained in this review, are powerful tools that can be combined to solve these puzzles. These integrated strategies can lead to the produc- tion of enzymes with special abilities, such as wide substrate specifcations, thermo-stability, tolerance to long time storage, and stability in diferent substrate conditions, such as pH and nutrients. Keywords: Biosynthetic pathways, CRISPR-cas, Fungi, Fungal secretome, Gene editing, Heterologous protein expression, Ligninolytic enzymes, Synthetic promoters, Transcription activation Background Ligninolytic enzymes (LEs)—also referred to as lignolytic enzymes or lignin modifying enzymes *Correspondence: [email protected]; [email protected]; lorenzo. (LMEs)—are proteins that catalyze modifcations or [email protected]; [email protected] degradation of lignin into less complex molecules, to 1 School of Pharmaceutical Science and Technology, Tianjin University, Nankai District, 92 Weijin Road, Tianjin 300072, China produce CO2 eventually [1, 2]. Many fungal species Full list of author information is available at the end of the article produce LEs and possess complex enzyme systems for © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/. 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 in a credit line to the data. Asemoloye et al. Microb Cell Fact (2021) 20:20 Page 2 of 18 lignin degradation, including laccase (LCC), lignin per- In bioinformatics, genome mining refers to the identi- oxidase (LiP), manganese peroxidase (MnP), and versa- fcation of features of an organism through the analysis tile peroxidase (Vp). Tese enzymes are characterised of its genome. In this context, software has been written by broad specifcity in degradation/mineralization of ad hoc for the analysis of fungal genomes in order to both diferent complex substrates such as wood, paper, ani- trace BGCs associated with known NPs and discover mal feeds, pesticides, biofuels, and hydrocarbons [2–6]. silent BGCs that, once activated, potentially lead to the Te importance of LEs in biotechnological, industrial, expression of novel, valuable compounds [17, 18]. Algo- and environmental applications—especially in the pro- rithms for the identifcation of gene clusters into unan- duction of bioenergy or biomaterials, is worldwide rec- notated or partially annotated genomes are very accurate ognized [7]. However, many natural products (NPs) are but can detect only BGCs associated with well-defned not naturally produced in enough quantities for human families of NPs, such as polyketides and RiPPS (ribo- or industrial use. Beside NPs, specifc fungal enzymes, somally synthesized and post-translationally modifed which either play a role in metabolic pathways or are peptides). Secondary metabolite unique regions fnder secreted to carry out their activities in the environ- (SMURF) [19], antibiotics and secondary metabolites ment, have grabbed the attention of the Synthetic Biol- analysis shell (antiSMASH) [20, 21], secondary metabo- ogy community. lite by inter-pro-scan (SMIPS), and cluster assignment Metabolic pathway engineering, therefore, has become by islands of sites (CASSIS) [22] are popular software for quickly an important branch of Synthetic Biology, whose secondary metabolite detection and BGC recognition. goal is to optimize the synthesis of chemical compounds More obscure, silent fungal BGCs have been discovered of interest in various felds, from pharmaceutics to bio- by means of diferent computational tools. For instance, fuel [8]. For instance, the production in Saccharomy- motif-independent de novo detection algorithm for sec- ces cerevisiae (Desm.) Meyen of the artemisinic acid, a ondary metabolite biosynthetic gene clusters (MIDDAS- precursor of the antimalarial drug artemisinin—usually M) [23] has brought to light the BGC of ustiloxin B and extracted from the plant Artemisia annua L.—is still one MIPS-GS [24] has permitted to decipher the kojic acid of the most celebrated successes in Synthetic Biology [9]. BGC in Aspergillus favus var. oryzae (Ahlb.) Kurtzman, Beside plants, flamentous fungi are another primary M.J. Smiley, Robnett & Wicklow. Tese computational source of natural products (NPs), which are benefcial works, combined with experimental evidence, have laid to human health. Genes that drive the expression of NPs the foundation of the MIBiG (Minimum Information are, generally, located into small regions of the fungal about a Biosynthetic Gene cluster) [25] repository and genome, giving rise to so-called biosynthetic gene clus- data standard. MIBiG ofers a broad collection of build- ters (BGCs). ing blocks (genes, enzymes, and protein domains) for the As an engineering science, Synthetic Biology aims at in silico design of novel biosynthetic pathways [26, 27]. the rational design and model-driven in vivo implemen- Synthetic Biology has also put forward novel tech- tation of biological systems that carry out specifc tasks niques for BGC activation in fungo and heterologous [10]. Te frst Synthetic Biology artifacts were genetic reconstruction of BGC in microbial cells [28]. Control circuits, more precisely transcriptional networks, able of single genes or entire pathways in fungo has been to mimic electronic functions such as oscillations, tog- achieved through the expression of transcription factors gle switches, and Boolean gates [11]. Circuits, initially, based on bacterial proteins, a technique already largely were hosted by bacteria—often Escherichia coli—and exploited in mammalian and yeast cells. Te Tet-ON sys- required the construction of a handful of transcription tem [29] has allowed the synthesis of a novel NP—named units i.e. DNA sequences where a promoter is followed, fumipyrrole—in Aspergillus fumigatus Fresen [30]. More in the order, by a ribosome binding site (RBS), a cod- recently, Rantasalo and co-authors [31] have shown that a ing sequence (CDS), and fnally, a terminator. In paral- synthetic activator, made by the fusion of the DNA-bind- lel, computational tools were developed to facilitate the ing bacterial protein Bm3R1 [32] and the viral activation design, modeling, and analysis of genetic circuits [12, 13]. domain VP16 [33], enhances red fuorescence expression Te increasing complexity
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