1. Metagenomics
The sequencing and analysis of DNA is not
limited to organisms culturable in laboratory
conditions. In the context of metagenomics,
DNA from environmental samples can be purified and even enriched to select for a particular
group of organisms, e.g., eukaryotes or fungi
(Cha ´vez et al. 2015). The subsequent treatment
branches out and may involve the complex,
labor-intensive process of sequencing, assembly, annotation, and bioinformatic search for
biosynthetic clusters. Alternatively, a metagenomic library construction may follow to produce vectors carrying the isolated DNA. This
approach is well established in bacteria [e.g.,
Iqbal et al. (2016)] and has led to the discovery
of numerous bioactive compounds, including
the recent malacidins (Hover et al. 2018). In
fungi, this has been elegantly demonstrated in
the construction of fungal artificial chromosomes (Bok et al. 2015), as discussed above.
Following suit, Robey et al. (2018) used the
same technology to identify a novel biosynthetic mechanism from an Aspergillus aculeatus
NRPS. Metagenomic mining is a prospective
tool for the identification of potentially valuable
natural products from microbes that are unculturable under laboratory conditions (Milshteyn
et al. 2014). Cha ´vez et al. (2015) underline the
importance of extremophilic ascomycetes as an
untapped source. Similarly, symbiotic and
endophytic fungi are garnering attention as a
poorly characterized biological system with
biosynthetic capacity (Borges et al. 2009).
B. Transcriptomics
The utility of transcriptomics in the identification of active BGCs, as an intermediate
approach between genomics and metabolomics, is evident. The comparative analysis of
expression profiles between samples (e.g., treated/untreated, induced/uninduced, wild type/
mutant) yields information about pathway
expression, thus supporting other omics
approaches, and helping to identify (co-) regulation patterns.
In an aforementioned example, Schroeckh
et al. (2009) used a microarray-based transcriptomics approach to analyze the changes
induced in A. nidulans upon co-cultivation
with S. rapamycinicus. Within the large set of
differentially expressed genes, genes from several SM clusters were found whose expression
was induced by contact with the bacterium.
One of these was the previously unidentified
cluster encoding orsellinic acid biosynthetic
genes.
Similarly, an uncharacterized NRPS cluster with an
unidentified product was linked to virulence and late
stage of infection in Fusarium graminearum (Zhang
et al. 2012) using microarray transcriptional profiling.
Transcriptional regulation at a global scale
can be revealed via transcriptome analyses. The
comparison of expression profiles between
samples was used to identify regulators linked
to SM silencing in bacteria (Amos et al. 2017); it
is conceivable that a similar approach may be
used in identifying regulatory elements in
fungi. Furthermore, an apparent co-regulation
of genes uncharacteristically scattered over
multiple chromosomes (rendering them
unidentifiable by conventional genomic predictions) revealed a regulatory crosstalk between
gene clusters. In A. nidulans, an activator gene
from a putative NRPS cluster on chromosome
II induced the expression of a silent PKS cluster
on chromosome VIII, leading to the identification of the polyketide asperfuranone (Bergmann et al. 2010).
The production of nidulanin A, discovered in the same
organism more recently, also requires two biosynthetic
genes, an NRPS and a prenyltransferase, physically
distant in their genomic context (Andersen et al. 2013).
MIDDAS-M, a bioinformatics tool designed
to detect BGCs, including those lacking a
canonical backbone enzyme, incorporates an
algorithm that can be used to analyze coregulatory phenomena in transcriptome data
by generating virtual clusters and scoring
expression differences. This method was
applied to identify the gene cluster responsible
for ustiloxin B in A. flavus (Takeda et al. 2014).
11 New Avenues Toward Drug Discovery in Fungi
283
The sequencing and analysis of DNA is not
limited to organisms culturable in laboratory
conditions. In the context of metagenomics,
DNA from environmental samples can be purified and even enriched to select for a particular
group of organisms, e.g., eukaryotes or fungi
(Cha ´vez et al. 2015). The subsequent treatment
branches out and may involve the complex,
labor-intensive process of sequencing, assembly, annotation, and bioinformatic search for
biosynthetic clusters. Alternatively, a metagenomic library construction may follow to produce vectors carrying the isolated DNA. This
approach is well established in bacteria [e.g.,
Iqbal et al. (2016)] and has led to the discovery
of numerous bioactive compounds, including
the recent malacidins (Hover et al. 2018). In
fungi, this has been elegantly demonstrated in
the construction of fungal artificial chromosomes (Bok et al. 2015), as discussed above.
Following suit, Robey et al. (2018) used the
same technology to identify a novel biosynthetic mechanism from an Aspergillus aculeatus
NRPS. Metagenomic mining is a prospective
tool for the identification of potentially valuable
natural products from microbes that are unculturable under laboratory conditions (Milshteyn
et al. 2014). Cha ´vez et al. (2015) underline the
importance of extremophilic ascomycetes as an
untapped source. Similarly, symbiotic and
endophytic fungi are garnering attention as a
poorly characterized biological system with
biosynthetic capacity (Borges et al. 2009).
B. Transcriptomics
The utility of transcriptomics in the identification of active BGCs, as an intermediate
approach between genomics and metabolomics, is evident. The comparative analysis of
expression profiles between samples (e.g., treated/untreated, induced/uninduced, wild type/
mutant) yields information about pathway
expression, thus supporting other omics
approaches, and helping to identify (co-) regulation patterns.
In an aforementioned example, Schroeckh
et al. (2009) used a microarray-based transcriptomics approach to analyze the changes
induced in A. nidulans upon co-cultivation
with S. rapamycinicus. Within the large set of
differentially expressed genes, genes from several SM clusters were found whose expression
was induced by contact with the bacterium.
One of these was the previously unidentified
cluster encoding orsellinic acid biosynthetic
genes.
Similarly, an uncharacterized NRPS cluster with an
unidentified product was linked to virulence and late
stage of infection in Fusarium graminearum (Zhang
et al. 2012) using microarray transcriptional profiling.
Transcriptional regulation at a global scale
can be revealed via transcriptome analyses. The
comparison of expression profiles between
samples was used to identify regulators linked
to SM silencing in bacteria (Amos et al. 2017); it
is conceivable that a similar approach may be
used in identifying regulatory elements in
fungi. Furthermore, an apparent co-regulation
of genes uncharacteristically scattered over
multiple chromosomes (rendering them
unidentifiable by conventional genomic predictions) revealed a regulatory crosstalk between
gene clusters. In A. nidulans, an activator gene
from a putative NRPS cluster on chromosome
II induced the expression of a silent PKS cluster
on chromosome VIII, leading to the identification of the polyketide asperfuranone (Bergmann et al. 2010).
The production of nidulanin A, discovered in the same
organism more recently, also requires two biosynthetic
genes, an NRPS and a prenyltransferase, physically
distant in their genomic context (Andersen et al. 2013).
MIDDAS-M, a bioinformatics tool designed
to detect BGCs, including those lacking a
canonical backbone enzyme, incorporates an
algorithm that can be used to analyze coregulatory phenomena in transcriptome data
by generating virtual clusters and scoring
expression differences. This method was
applied to identify the gene cluster responsible
for ustiloxin B in A. flavus (Takeda et al. 2014).
11 New Avenues Toward Drug Discovery in Fungi
283