166
metabolome analysis. Genome mining is conducted, the gene clusters and their NPs
are identified with MS-based analysis and a spectral network is generated to compare the different products for the similarities and diversities. Thus, metabolomics
techniques have assisted the structural characterization of new metabolites and also
provided novel information on underlying pathways. Such an approach was conducted in analysing the siderophore metabolism of the bacteria Azotobacter vinelandii to reveal unreported derivatives of siderophores and their origins (Baars
et al. 2016).
A next-generation automated pipeline developed for metabolomic analysis
recently attempted to directly predict the products of BGCs including NRPS from
genomes claiming to be a genomes-to-natural products (GNP) platform (Johnston
et al. 2015). Consequent to the GNP platform, an implementation termed as
‘Prediction Informatics for Secondary Metabolomes’ (PRISM, not to be confused
with PrISM – explained before) was also developed by the same group that uses
genome mining to identify NRPSs and their corresponding substrates. (Skinnider
et al. 2015, 2017). The implementation was later linked to an automated pipeline via
two tools GRAPE, a retro-biosynthetic tool, and GARLIC, which compares the
substrate prediction result of PRISM with GRAPE output to assess the production
of a given compound (Dejong et al. 2016).
One of the major limitations of secondary metabolite study is the low amount of
production compared to the primary metabolism. Mathematical representations of
metabolism in genome-scale metabolic models (GEM) inclusive of secondary
metabolism can be utilized to get around such limitations (Nielsen and Nielsen
2017). GEMs are useful models that assist the design of metabolic strategies in
organisms. The functionality of GEM is based on connecting annotated genes to the
biochemical reactions that are catalysed by the respective enzymes. This provides a
subjective overview of the metabolic capabilities of the organism (Price et al. 2003;
Agren et al. 2013). Further, along with primary metabolism, inclusion of secondary
metabolism in genome-scale metabolic models of organisms could also help to optimize the production of fungal NPs such as siderophores.
Techniques of flux mode analysis and elementary flux mode analysis were commonly used in computational metabolic analysis to identify optimization techniques
(Agren et al. 2013; Zanghellini et al. 2013). Whereas flux balance analysis deals
with the simulation of flux distribution in the reconstructed GEM network, elementary flux mode analysis determines all the feasible and minimal pathway routes in
the network (Lotz et al. 2014). The advantages of metabolic flux analysis in GEMs
lie in the fact that only limited experimental data is required and the model can be
associated with an iterative cycle of prediction and validation strategies for the
rational design of engineering strategies. In recent years, a number of works using
GEMs to focus on secondary metabolism in prokaryotes have been conducted to
optimize the production of useful metabolites (Kim et al. 2016). However, such
studies are lacking in fungi or are limited to only a few major species. The secondary metabolism of several actinomycetes were studied using GEMs. The study in
Streptomyces coelicolor A3(2) GEM identified two complete secondary metabolism pathways including an NRP (Borodina et al. 2005). Recently, secondary the
D. Subramanian et al.
metabolome analysis. Genome mining is conducted, the gene clusters and their NPs
are identified with MS-based analysis and a spectral network is generated to compare the different products for the similarities and diversities. Thus, metabolomics
techniques have assisted the structural characterization of new metabolites and also
provided novel information on underlying pathways. Such an approach was conducted in analysing the siderophore metabolism of the bacteria Azotobacter vinelandii to reveal unreported derivatives of siderophores and their origins (Baars
et al. 2016).
A next-generation automated pipeline developed for metabolomic analysis
recently attempted to directly predict the products of BGCs including NRPS from
genomes claiming to be a genomes-to-natural products (GNP) platform (Johnston
et al. 2015). Consequent to the GNP platform, an implementation termed as
‘Prediction Informatics for Secondary Metabolomes’ (PRISM, not to be confused
with PrISM – explained before) was also developed by the same group that uses
genome mining to identify NRPSs and their corresponding substrates. (Skinnider
et al. 2015, 2017). The implementation was later linked to an automated pipeline via
two tools GRAPE, a retro-biosynthetic tool, and GARLIC, which compares the
substrate prediction result of PRISM with GRAPE output to assess the production
of a given compound (Dejong et al. 2016).
One of the major limitations of secondary metabolite study is the low amount of
production compared to the primary metabolism. Mathematical representations of
metabolism in genome-scale metabolic models (GEM) inclusive of secondary
metabolism can be utilized to get around such limitations (Nielsen and Nielsen
2017). GEMs are useful models that assist the design of metabolic strategies in
organisms. The functionality of GEM is based on connecting annotated genes to the
biochemical reactions that are catalysed by the respective enzymes. This provides a
subjective overview of the metabolic capabilities of the organism (Price et al. 2003;
Agren et al. 2013). Further, along with primary metabolism, inclusion of secondary
metabolism in genome-scale metabolic models of organisms could also help to optimize the production of fungal NPs such as siderophores.
Techniques of flux mode analysis and elementary flux mode analysis were commonly used in computational metabolic analysis to identify optimization techniques
(Agren et al. 2013; Zanghellini et al. 2013). Whereas flux balance analysis deals
with the simulation of flux distribution in the reconstructed GEM network, elementary flux mode analysis determines all the feasible and minimal pathway routes in
the network (Lotz et al. 2014). The advantages of metabolic flux analysis in GEMs
lie in the fact that only limited experimental data is required and the model can be
associated with an iterative cycle of prediction and validation strategies for the
rational design of engineering strategies. In recent years, a number of works using
GEMs to focus on secondary metabolism in prokaryotes have been conducted to
optimize the production of useful metabolites (Kim et al. 2016). However, such
studies are lacking in fungi or are limited to only a few major species. The secondary metabolism of several actinomycetes were studied using GEMs. The study in
Streptomyces coelicolor A3(2) GEM identified two complete secondary metabolism pathways including an NRP (Borodina et al. 2005). Recently, secondary the
D. Subramanian et al.