sub-sections in Sect. 2 for details and examples). Proteome analysis of lignin active
culture supernatants can thereby be used as a tool that can accelerate the discovery
process through integrating peptide signals with DNA sequence information.
In parallel to the discovery routes involving strain cultivation, in the past two
decades enzyme discovery based on environmental metagenomes has developed
into a powerful alternative approach to expand the spectrum of novel enzymes,
including ligninolytic enzymes. For that route, capable of capturing the metabolic
potential of the entire microbial biodiversity, including the vast majority of strains
in an environment that cannot be readily cultivated in the lab (estimated to be
regularly above 95% of all strains), total DNA of the microbial fraction of samples
from a relevant environment is isolated. Suitable natural environments targeted for
new ligninolytic enzymes thereby usually contain lignocellulose, like decaying
wood, compost piles, animal intestinal content, etc., or represent artificial situations
set up to specifically enrich for microbes capable of degrading lignin in situ (e.g., by
means of microbial traps filled with lignin substrate and placed e.g. in a compost
pile) or ex situ (e.g., IBIDI biofilm flow system inoculated with relevant environmental microbiomes and using lignin substrate as carbon source (manuscript of the
authors in preparation). Metagenomic DNA can then be subjected to
high-throughput (HT) sequencing. Approaches include, e.g., (i) 16S and 18S rRNA
gene or ITS amplicon sequencing for determining the prokaryotic and eukaryotic
consortium structure, respectively, (ii) shotgun DNA sequencing for determining
the full metabolic potential of the microbial assemblage, and, (iii) if starting with
cDNA, reverse-transcribed from total RNA, the metabolically active portion of the
metagenome (i.e., metatranscriptome).
Metagenome sequences are regularly stored in public databases like EBI
metagenomics, MG-RAST, and IMG/M, which are rapidly growing sources for
bioinformatics-based in silico mining for new enzyme candidates of the lignin
active families presented above. Important tools applied to utilize
sequence-homology (e.g., BLAST), sequence signatures (e.g., profiles Hidden
Markov Model (HMM)), and protein structure similarity (e.g., 3D-BLAST,
SWISS-MODEL) and link to dedicated databases for relevant enzyme classes
(NCBI, CAZy, LccED, eLignin, profiles HMM from Pfam, PDB, etc.) [22, 31, 32,
148]. As for the analysis of genomes, carefully in silico analyzed and selected
candidate genes of putative lignin-active enzymes are then cloned in suitable
expression systems for enzyme production, purification, and characterization. In
silico approaches to discover new enzymes have lately improved a lot to more
accurately predict enzyme function based on DNA sequence, increasing the chances
of correct functional prediction of gene candidates. However, being dependent on
previous results (sequences, annotations, structures), they fail to predict entirely
new enzymes with no apparent homology to any existing database entries.
Here, functional metagenomics approaches are complementary as they are
purely based on observable function. For functional screening, metagenomic DNA
(or cDNA) is used for library cloning, with fosmid cloning or short insert fragment
cloning into (broad host-range) expression vectors currently being the most popular
techniques. Libraries are usually established in E. coli and then transferred to (a
Recent Advances in Enzymatic Conversion of Lignin …
457
culture supernatants can thereby be used as a tool that can accelerate the discovery
process through integrating peptide signals with DNA sequence information.
In parallel to the discovery routes involving strain cultivation, in the past two
decades enzyme discovery based on environmental metagenomes has developed
into a powerful alternative approach to expand the spectrum of novel enzymes,
including ligninolytic enzymes. For that route, capable of capturing the metabolic
potential of the entire microbial biodiversity, including the vast majority of strains
in an environment that cannot be readily cultivated in the lab (estimated to be
regularly above 95% of all strains), total DNA of the microbial fraction of samples
from a relevant environment is isolated. Suitable natural environments targeted for
new ligninolytic enzymes thereby usually contain lignocellulose, like decaying
wood, compost piles, animal intestinal content, etc., or represent artificial situations
set up to specifically enrich for microbes capable of degrading lignin in situ (e.g., by
means of microbial traps filled with lignin substrate and placed e.g. in a compost
pile) or ex situ (e.g., IBIDI biofilm flow system inoculated with relevant environmental microbiomes and using lignin substrate as carbon source (manuscript of the
authors in preparation). Metagenomic DNA can then be subjected to
high-throughput (HT) sequencing. Approaches include, e.g., (i) 16S and 18S rRNA
gene or ITS amplicon sequencing for determining the prokaryotic and eukaryotic
consortium structure, respectively, (ii) shotgun DNA sequencing for determining
the full metabolic potential of the microbial assemblage, and, (iii) if starting with
cDNA, reverse-transcribed from total RNA, the metabolically active portion of the
metagenome (i.e., metatranscriptome).
Metagenome sequences are regularly stored in public databases like EBI
metagenomics, MG-RAST, and IMG/M, which are rapidly growing sources for
bioinformatics-based in silico mining for new enzyme candidates of the lignin
active families presented above. Important tools applied to utilize
sequence-homology (e.g., BLAST), sequence signatures (e.g., profiles Hidden
Markov Model (HMM)), and protein structure similarity (e.g., 3D-BLAST,
SWISS-MODEL) and link to dedicated databases for relevant enzyme classes
(NCBI, CAZy, LccED, eLignin, profiles HMM from Pfam, PDB, etc.) [22, 31, 32,
148]. As for the analysis of genomes, carefully in silico analyzed and selected
candidate genes of putative lignin-active enzymes are then cloned in suitable
expression systems for enzyme production, purification, and characterization. In
silico approaches to discover new enzymes have lately improved a lot to more
accurately predict enzyme function based on DNA sequence, increasing the chances
of correct functional prediction of gene candidates. However, being dependent on
previous results (sequences, annotations, structures), they fail to predict entirely
new enzymes with no apparent homology to any existing database entries.
Here, functional metagenomics approaches are complementary as they are
purely based on observable function. For functional screening, metagenomic DNA
(or cDNA) is used for library cloning, with fosmid cloning or short insert fragment
cloning into (broad host-range) expression vectors currently being the most popular
techniques. Libraries are usually established in E. coli and then transferred to (a
Recent Advances in Enzymatic Conversion of Lignin …
457