lism in the fungus and identify two novel compounds (Wiemann et al. 2013b). Similarly, a
large-scale genomic comparative functional
analysis of the biosynthetic potential of Penicillia, coupled with phylogenetic analysis and a
metabolomics approach, led to the identification of novel antimicrobial yanuthone derivatives and underlines the importance and
potential of this fungal clade as a bioactive compound source (Nielsen et al. 2017). Acharya
et al. (2019) selected a combined omics
approach for an in-depth characterization of
the interaction between a Rhodococcus sp. strain
and a strain of Micromonospora sp. producing a
novel glycosylated anthracycline, keyicin, with
antimicrobial properties.
Using genomics, transcriptomics, and proteomics technologies, the authors were able to elucidate regulatory
pathways that affect keycin production and correlate
changes appearing in the protein and transcript profiles
with changes consistent with quorum sensing signaling.
The intrinsic complementarity of individual omics datasets has been recognized, and
various tools have been developed to aid the
integration that will eventually improve efficiency and reduce complexity of sample analysis, as well as accelerate the discovery of
novel compounds in fungi. XCMS Online is a
data-processing platform originally developed
for metabolomics studies, which has been
expanded to integrate genomic, proteomic and
transcriptomic data. It provides metabolic
pathway analysis and cross-referencing with
other types of omics data for a comprehensive
characterization of the sample (Forsberg et al.
2018).
Andersen et al. (2013) developed a method for identification of new BGCs that combines transcriptomic and
metabolomic data with genomic analysis. Clusters were
predicted from genome data using the SMURF algorithm and scored based on similarity of the expression
profiles. These predictions correlated well with published data and the algorithm could be extended to
include biosynthetic superclusters spread over several
chromosomes; the aforementioned nidulanin A was
discovered using this integrative strategy (see above).
It is, however, worthwhile to note that the
integration of omics methods produces big data
volumes, compelling further development of
bioinformatics tools for processing, as well as
a consolidated effort among researchers to
ensure standardization and improve accessibility and comparability of individual datasets in
fungal research.
As has been illustrated, each type of omics
data provides unique information about a
biological system. In fungi, the inclusion of
several complementary techniques may significantly increase the NP discovery power of the
approach. Thus, omics-based methods represent a promising strategy for the identification
of new leads for drug discovery and development.
V. Conclusion
The latest report on fungal diversity estimates
that there are 2.2 to 3.8 million species in this
ancient clade, of which 120,000 (i.e. ca. 8%)
have been described (Hawksworth and Lu ¨cking
2017). Considering the majority of fungi yet to
be identified in underexplored environments
(e.g., lichens, tropical diversity hotspots,
extreme environments), as well as the wealth
of NPs discovered to date, it is justifiable to
predict that a vast number of fungal SMs will
be described in future. The emergence of novel
techniques, as well as advancements in established protocols, in the post-omics era promises a bright future for NP research.
Still, there persists a fundamental disconnect between the ample natural products being
discovered at present and the ever-growing call
for novel antimicrobial compounds used as
drugs in a pharmaceutical context. The contrast
is evident when considering the drying out
antibiotics pipeline (Cooper and Shlaes 2011)
and the overwhelming number of claims
regarding the unexplored potential of microbes
as a largely untapped source of natural products, many of them drug-like. As evidenced
by this review, fungi are not exempt. Moreover,
naturally derived SMs are intrinsically biologically relevant, thus occupying a much larger
drug-like chemical space than synthetic compounds (Harvey et al. 2015). Logically, NPs,
286
M. Flak et al.
large-scale genomic comparative functional
analysis of the biosynthetic potential of Penicillia, coupled with phylogenetic analysis and a
metabolomics approach, led to the identification of novel antimicrobial yanuthone derivatives and underlines the importance and
potential of this fungal clade as a bioactive compound source (Nielsen et al. 2017). Acharya
et al. (2019) selected a combined omics
approach for an in-depth characterization of
the interaction between a Rhodococcus sp. strain
and a strain of Micromonospora sp. producing a
novel glycosylated anthracycline, keyicin, with
antimicrobial properties.
Using genomics, transcriptomics, and proteomics technologies, the authors were able to elucidate regulatory
pathways that affect keycin production and correlate
changes appearing in the protein and transcript profiles
with changes consistent with quorum sensing signaling.
The intrinsic complementarity of individual omics datasets has been recognized, and
various tools have been developed to aid the
integration that will eventually improve efficiency and reduce complexity of sample analysis, as well as accelerate the discovery of
novel compounds in fungi. XCMS Online is a
data-processing platform originally developed
for metabolomics studies, which has been
expanded to integrate genomic, proteomic and
transcriptomic data. It provides metabolic
pathway analysis and cross-referencing with
other types of omics data for a comprehensive
characterization of the sample (Forsberg et al.
2018).
Andersen et al. (2013) developed a method for identification of new BGCs that combines transcriptomic and
metabolomic data with genomic analysis. Clusters were
predicted from genome data using the SMURF algorithm and scored based on similarity of the expression
profiles. These predictions correlated well with published data and the algorithm could be extended to
include biosynthetic superclusters spread over several
chromosomes; the aforementioned nidulanin A was
discovered using this integrative strategy (see above).
It is, however, worthwhile to note that the
integration of omics methods produces big data
volumes, compelling further development of
bioinformatics tools for processing, as well as
a consolidated effort among researchers to
ensure standardization and improve accessibility and comparability of individual datasets in
fungal research.
As has been illustrated, each type of omics
data provides unique information about a
biological system. In fungi, the inclusion of
several complementary techniques may significantly increase the NP discovery power of the
approach. Thus, omics-based methods represent a promising strategy for the identification
of new leads for drug discovery and development.
V. Conclusion
The latest report on fungal diversity estimates
that there are 2.2 to 3.8 million species in this
ancient clade, of which 120,000 (i.e. ca. 8%)
have been described (Hawksworth and Lu ¨cking
2017). Considering the majority of fungi yet to
be identified in underexplored environments
(e.g., lichens, tropical diversity hotspots,
extreme environments), as well as the wealth
of NPs discovered to date, it is justifiable to
predict that a vast number of fungal SMs will
be described in future. The emergence of novel
techniques, as well as advancements in established protocols, in the post-omics era promises a bright future for NP research.
Still, there persists a fundamental disconnect between the ample natural products being
discovered at present and the ever-growing call
for novel antimicrobial compounds used as
drugs in a pharmaceutical context. The contrast
is evident when considering the drying out
antibiotics pipeline (Cooper and Shlaes 2011)
and the overwhelming number of claims
regarding the unexplored potential of microbes
as a largely untapped source of natural products, many of them drug-like. As evidenced
by this review, fungi are not exempt. Moreover,
naturally derived SMs are intrinsically biologically relevant, thus occupying a much larger
drug-like chemical space than synthetic compounds (Harvey et al. 2015). Logically, NPs,
286
M. Flak et al.