Bioprospecting Microbial Diversity for Lignin Valorization: Dry and Wet Screening Methods

Abstract

Lignin is an abundant cell wall component, and it has been used mainly for generating steam and electricity. Nevertheless, lignin valorization, i.e. the conversion of lignin into high value-added fuels, chemicals, or materials, is crucial for the full implementation of cost-effective lignocellulosic biorefineries. From this perspective, rapid screening methods are crucial for time- and resource-efficient development of novel microbial strains and enzymes with applications in the lignin biorefinery. The present review gives an overview of recent developments and applications of a vast arsenal of activity and sequence-based methodologies for uncovering novel microbial strains with ligninolytic potential, novel enzymes for lignin depolymerization and for unraveling the main metabolic routes during growth on lignin. Finally, perspectives on the use of each of the presented methods and their respective advantages and disadvantages are discussed.

Keywords: activity-based screening; bioprocess; biosensors; high-throughput screening; lignin-degrading enzymes.

FIGURE 1

Global distribution and potential availability of major agro-industrial residues and lignin for valorization purposes. The global agricultural production in 2017 was obtained from the Food and Agriculture Organization of the United Nations (2019) Database (9), ^a(5), ^b(10), ^c(11), ^d(12), ^e(13), ^f(14), ^g(15), ^h(16), and ^I(17). Figure footnotes: The global production of wheat, corn, rice, sugarcane, soybeans and barley in 2017 was obtained from the Food and Agriculture Organization of the United Nations (2019) Database (http://www.fao.org/faostat/en/#data) and was used as input data to calculate the availability of crop residues and lignin for biorefining purposes. The total amount of residue generated from the processing of each crop (wheat straw, corn stover, rice straw, sugarcane bagasse and straw, soybean hulls and barley straw) was calculated by multiplying the total crop amount by the “residue-to-crop ratio”. The “residue-to-crop ratio” for each crop was calculated elsewhere and is available in the literature, as indicated in the figure. The realistic availability of crop residues for biorefining purposes was estimated by multiplying the total amount of crop residues by 0.3 [i.e. corresponding to 30% of the total, as estimated by Daioglou et al. (2016)]. Last, the availability of lignin for biorefining purposes was calculated by multiplying the estimated realistic availability of crop residues for biorefining purposes by the lignin content of each residue (as obtained from the literature, as indicated in the figure). The raw data employed to build the figure are shown in Supplementary Material.

FIGURE 2

Schematic overview of novel technologies for bioprocessing technical lignins (TLs) into specialty and bulk chemicals. (A) Technical lignins are depolymerized by lignolytic enzymes into depolymerized technical lignins (DTLs) consisting in a mixture of monomeric and dimeric aromatic compounds. DTLs are further converted by microorganisms via upper funneling pathways into central aromatic branching nodes (e.g. catechol or protocatechuate acid). These compounds are converted by different metabolic pathways depending on the target bioproduct category, as exemplified here with aromatic specialty chemicals, ketoacid or acetyl-coA-derived precursors for polymers, e.g. bioplastics or lipids. (B) Primary lignin depolymerizing enzymes categorized by family and type of substrate.

FIGURE 3

Overview of growth screening methods. Cell growth and activity-based screening methods for the identification of lignin-degrading microorganisms and enzymes. Growth in solid and liquid media containing lignin and aromatic dyes (Colorimetric assays) coupled with GC-MS, LC-MS. The structures of common substrates used in the activity-based screening assays is shown.

FIGURE 4

DNA-based annotation strategies. Homology-based annotation. The BLAST search algorithm uses 3 k-mer words to anchor and extend the alignment for establishing homology between the queried sequence and the deposited sequence. Conserved domain-based annotation. Hidden Markov Models (HMM) are built based on a multiple alignment from homologous sequences resulting in conserved domain signatures for specific family proteins. The signatures are used as in silico probes against DNA sequences (or vice versa). Subsystem-based annotation. DNA sequences are allocated in curated subsystems (experimental data) based on K-mer searching for the identification of isofunctional homolog genes in closely related genomes harbored in protein families assigned by the Fellowship Interpretation of Genomes (FigFam), connecting the functional role and in chromosomal cluster with genes implementing functional roles from the same subsystem (red arrow along the genomes). The pie chart (below) and the genomic arrangements (on the right) are a graphic representation of the SEED server subsystem distribution category for Pseudomonas putida KT2440.

FIGURE 5

Lignin depolymerization-related genes annotated over the last 20 years.

FIGURE 6

Biosensor-based screening methods. Enables the screening of metagenomic libraries, for selecting enzymes for industrial purposes and DNA sequencing. The method begins with selecting metagenomic DNA from environmental samples, which will be used in the genetic circuit construction consisting of the gene of interest, a strong promoter and a reporter gene. After the metagenomic library is constructed, the clones can be sorted by fluorescent signal detection through activation by a phenolic compound. Then, the fluorescent clones containing genes of interest are selected by functional mining, and lignolytic enzymes can be identified. Another option is to extract the selected metagenomic DNA, which will be sequenced and matched in databases, the eLignin database for instance, with a search for homologous lignolytic activities in the metagenomic samples.