Forum

Identifying Missing
Biosynthesis Enzymes
of Plant Natural Products

Thomas Dugé de Bernonville,¹

Nicolas Papon,²

Marc Clastre,¹

Sarah E. O'Connor,³,\* and

Vincent Courdavault¹,\*

<figure><img src="image_1.png" /></figure>

Elucidating plant-specialized bio-
synthetic pathways has always
constituted a laborious task, nota-
bly for natural products with high
pharmaceutical values. Here, we
discuss emerging omics-based
strategies that facilitate the identifi-
cation of genes from these com-
plex metabolic pathways, paving
the way to engineered supplies
of these compounds through syn-
thetic biology approaches.

Finding a Needle in a Haystack

Plants represent a remarkable source of
natural metabolites, many of which remain
the basis of the pharmacopoeia used
by humans to treat various disorders
[1]. Highly potent compounds are found
within monoterpene indole alkaloids
of the Apocynaceae plant family, yew
taxane-type terpenoids, mayapple lignans,
poppy isoquinoline alkaloids, and hemp
cannabinoids. Due to their pharmaceutical
importance, the biosynthetic pathways
responsible for the production of these
compounds *in planta* have attracted the
attention of many research groups for de-
cades. This area remains in focus as it is
essential to be able to propose cheaper
and high-throughput alternatives of pro-
duction of these valuable pharmaceutical
compounds, in a short period.

While most of the enzymes catalyzing bio-
synthesis steps have been progressively

identified using **sequence homology** (see Glossary)-based cloning or protein purification, these discovery efforts have always been laborious, particularly until the arrival of massive omics resources such as **next generation sequencing (NGS)**. This is likely because downstream branches of these pathways: (i) differ among plant species, even within the same genus; and (ii) involve enzymes from superfamilies containing several tens to hundreds of members [2]. Such superfamilies include reductases, oxidases, methyltransferases, and cytochrome P450s [3]. Cytochrome P450s in particular are key components of plant biosynthetic pathways and may catalyze multiple types of reactions, including hydroxylation, epoxidation, demethylation, dealkylation, decarboxylation, and C–C bond cleavage [4]. Fortunately, recent advances in omics-based strategies have accelerated the identification of missing biosynthetic enzymes. Below, we briefly highlight these advances, also summarized in Figure 1.

Identifying First Sets of Candidate
Genes through Metabolomics and
Transcriptomics

Identifying enzymes catalyzing steps of
given pathways requires a prior knowl-
edge on potential reaction scenarios
leading to the formation of the expected
compound, as illustrated on the left part
of Figure 1. Metabolomics are typically
used to identify preferential accumulation
sites of specialized metabolites and to
detect plausible reaction intermediates.
Molecular networking also represents a
considerable advance in the processing
of the resulting metabolomics data ac-
quired with either liquid chromatography
or gas chromatography coupled to mass
spectrometry by connecting related sepa-
rated compounds based on their mass
spectra [5]. Ideally, connected com-
pounds may reflect an entire biosynthetic
branch. Once a reaction sequence has
been postulated, a type of enzymatic

Glossary

**Next generation sequencing (NGS):** second generation technologies refers to the high-throughput sequencing of DNA fragment ends, usually outputting millions of short reads (up to 250 nt long). The third generation technologies sequence single DNA molecules, generating long reads (at least >1000 nt long).

RNA-seq: high-throughput sequencing of reverse-transcribed mRNA. Short read-based RNA-seq is typically a two-step workflow, including: (i) transcript reconstruction (genome-based or de novo); and (ii) gene expression inference from read attribution to reconstructed transcript sequences.

**Self-organizing maps:** an unsupervised machine learning algorithm used to cluster genes into a given number of nodes forming a 2D map. In this map, regions contain nodes reflecting genes with similar expression levels.

**Sequence homology:** homology defines similarity between two nucleic or protein sequences. Similarities can be searched against specific databases with the well-known BLAST algorithm. Phylogenies compare a set of sequences to highlight evolutionary relationships through statistical approaches.

**Virus-induced gene silencing (VIGS):** a reverse genetics approach allowing transient plant gene downregulation mediated by the infection of a modified virus harboring a short sequence targeting a specific gene.

**Whole genome assembly:** a complex task due to inherent genome complexity and requires a combination of short reads and long reads to ensure the reconstruction of long gDNA scaffolds.

activity required for each step may be hy-
pothesized based on literature data
or bioinspired organic chemistry. Activity
of enzymes relies on specific functional
conserved domains that can drive their
identification. For instance, a hydroxylation
step identified as described above may
be catalyzed by a cytochrome P450 or
a dioxygenase, each bearing their own
functional domain. Defining a first set
of candidate genes requires access to
genomes or transcriptome assemblies,
providing gene or transcript sequences,
respectively, to look for such specific
domains. Although challenging in terms
of computational requirements, whole
genomes may be sequenced at lower
cost from short DNA reads generated
through NGS. RNA-seq-based
<figure><img src="image_2.png" /></figure>

Trends in Pharmacological Sciences

**Figure 1.** Workflow for Identification of Missing Steps in Plant-Specialized Metabolic Pathways. Identification process starts from a thorough understanding of possible chemical reactions transforming substrate A to product F with biosynthetic intermediates B, C, D, and E. Conversions may involve already characterized reactions, here, for example, those involving a dioxygenase (DOX), a acetyltransferase (ACT), an alcohol dehydrogenase (ADH), and an O-methyltransferase (OMT). In this example, we illustrate identification of the enzymatic step converting compound D to E. Previous data show that E is a hydroxylated form of D and, based on literature precedence, it is expected that this hydroxylation is catalyzed by a cytochrome P450 monooxygenase. The first step consists of the identification of genes encoding cytochrome P450s in predicted gene sets from a genome (whole-genome sequencing) or a transcriptome (RNA-seq) assembly. This will result in a large list of candidate cytochrome P450s as it is a large family in plants. The second step aims at reducing this large list through different approaches described in the main text. This includes homology-based, random mutagenesis, physical gene clusters in genomes and coexpression analysis. This last analysis may be performed using either differentially expressed genes (DEG), gene coexpression networks (GCN), or unsupervised machine learning (UML). The resulting candidates are then functionally validated (pTRV1 and 2 represent plasmids encoding the two genomic components of the tobacco rattle virus classically used for virus induced gene silencing). An integrative approach such as supervised machine learning (SML) will merge the power of each approach to refine candidate genes. An SML approach should be able to integrate different variables (gene expression, gene clusters, etc.) and set rules to correctly class genes in a given metabolic pathway.
transcriptome analysis is simpler to con-
duct than **whole genome assembly** be-
cause the transcriptome only covers
a small part of the genome. Based on
genome or transcriptome annotation,
functional domains can be systematically
attributed to define first sets of candidate
genes that will require a further prioritiza-
tion through the methodological aspects
described below.

Prioritization by Homology-Based
Screening

One of the most direct and obvious ways
to unravel missing steps is to take inspira-
tion from previous works reported in the
literature and screen for homology to
orthologous gene sequences of already
characterized biosynthesis steps in other
plant species (Figure 1). This can be done
by searching homologies [e.g., with basic
local alignment search tool (BLAST)] or
constructing phylogenies statistically fitted
on evolutionary models encompassing
protein sequences from multiple species
[6]. For example, Farrow et al. [7] recently
identified the ibogamine 10-hydroxylase
from the African shrub (Tabernanthe
iboga) by searching for genes with homol-
ogy to tabersonine 16-hydroxylase from
Madagascar periwinkle (Catharanthus
roseus), which catalyzes indole ring
hydroxylation at the same position. How-
ever, while often successful, this approach
must be considered with caution since
it has been demonstrated in the above
example that the closest ortholog does
not necessarily catalyze a similar reaction
[7].

Prioritization by Random
Mutagenesis

Unlike the above described targeted ap-
proach, wide-scale untargeted studies can
be deployed using ethyl methanesulfonate-
based random mutagenesis (Figure 1) [8].
In this approach, mutagenized plants are
screened for changes in the production of
the desired metabolite. Plants with altered
profiles are then sequenced to identify the

source of the mutation by NGS that is re-
sponsible for changes in the metabolism.
This is a labor-intensive but powerful strat-
egy already applied to C. roseus to isolate
mutants with altered alkaloid contents. This
approach led to the identification of an
O-acetylstemmadenine oxidase catalyzing
a major oxidation step in catharanthine and
tabersonine biosynthesis [9].

Prioritization by Searching
Physical Gene Clusters

Since plant biosynthetic pathways some-
times involve enzymes encoded by genes
physically clustered in the same genomic
region, genome sequence may improve
homology searches by the analysis of
genomic neighbors. Candidate genes
can thus be refined through the *in silico*
processing of large sequencing data
using previously characterized biosynthe-
sis genes as baits (Figure 1). It is par-
ticularly efficient with more complete
genome sequences composed of very
long DNA sequence assemblies, as re-
cently exemplified with the identification
of a gene cluster encoding five enzymes
involved in thebaine biosynthesis in
opium poppy (*Papaver somniferum*)
[10]. With the explosion in the availability
of plant genome sequences in the com-
ing years, new gene clusters are likely to
be discovered. However, one of the
disadvantages of this method is that bio-
synthetic pathways show only few, if
no, physically clustered genes, such
as recently observed in happy tree
(Camptotheca acuminata) [11].

Prioritization by Gene
Coexpression Analysis

Candidate gene selection may also rely on
expression pattern similarities among tis-
sues or experimental conditions displayed
by genes related to a common biosyn-
thetic branch (Figure 1). The underlying
hypothesis is that metabolites accumu-
lated in a tissue-specific manner should
be produced through a chain of enzymes
displaying a similar spatio-temporal

expression pattern, which should be visi-
ble at the transcript level.

Such coexpression can guide identifica-
tion of missing steps by generating gene
lists corresponding to groups of genes
that have similar expression across
tissues. Groups of similarly expressed
transcripts that contain genes already
identified as part of the investigated
metabolic pathway are used to identify
candidate genes for the missing steps.
It typically requires the measurement of
genome-wide gene expression in different
tissues or experimental conditions using
RNA-seq [12]. Three major approaches
can be used on the generated data
(expression matrices containing genes in
rows and sample types in columns) for
coexpression analyses and eventual iden-
tification of missing biosynthetic enzymes.

A first approach entails the statistical com-
parison of contrasted samples (e.g., roots
versus leaves or control versus treatment)
to identify differentially expressed genes.
In this way, geissoschizine oxidase was
found to be upregulated in stressed leaves
of C. roseus, together with strictosidine
glucosidase that commonly act in the
monoterpene indole alkaloid biosynthetic
pathway [13]. More extensively, gene
expression profiles can be compared by
a hierarchical clustering approach in multi-
ple sample comparisons, such as per-
formed to identify enzymes converting
matairesinol into etoposide aglycone in
the mayapple (Podophyllum hexandrum)
lignan biosynthetic pathway [14].

A second method is based on the con-
struction of gene coexpression networks
(GCNs) to provide a more exhaustive
view of coexpression relationships among
genes [12]. GCNs visualize similarities in
gene expression profiles where distances
between each possible gene pairs
are calculated using specific metrics
(e.g., Pearson correlation coefficient)
and only the best coexpressed gene
pairs are retained to construct a GCN. In GCN type representations, genes (also referred to as 'nodes') are connected by edges representing these distances [12]. This approach has been successfully used to streamline the identification of precondylocarpine acetate synthase and tabersonine synthase in *C. roseus* [15].

Another method for analyzing gene coexpression is based on unsupervised machine learning (UML). UML methods group similarly expressed genes into a specific expression cluster. These methods are unsupervised because they cluster genes according to their expression levels without prior knowledge of their function. UML has been successfully used several times, in particular with **self-organizing maps**. As an example, this algorithm has been used to identify the sarpagan-bridge enzyme involved in ajmaline biosynthesis in the devil pepper (*Rauvolfia serpentina*), an Apocynaceae closely related to *C. roseus* [16].

## Validating Functions of Candidate Genes

Elucidating candidate gene functions is a mandatory step but can be laborious and time consuming especially in nonmodel plant species for which no mutant libraries or no quantitative genetics data are available, albeit they are the main source of specialized metabolites. While biochemical characterization relying on recombinant protein expression can be envisaged for a small number of candidates, it always requires access to potential enzyme substrates. Over the last few years, wider gene function analyses have been enabled through the development of efficient and straightforward reverse genetic approaches based on transient transcript degradation through **virus-induced gene silencing (VIGS)**, notably [17]. Besides confirming involvement in biosynthetic pathways, such an approach can also provide evidence of gene function through the identification of accumulated biosynthetic intermediates

resulting from silencing [13]. However, final biochemical characterization is still required to confirm VIGS results. Finally, for validation of multiples genes from the same pathway, heterologous reconstitution of partial pathways can be performed by simultaneous gene co-overexpression combined to biosynthetic precursor feeding, as performed for podophyllotoxin and tabersonine pathways [14,15].

## Concluding Remarks and Future Perspectives

The present forum article briefly describes current procedures used to characterize missing steps from plant metabolic pathways. The advent of NGS technologies have largely fueled these procedures, allowing completion of pathway knowledge in several medicinal plants.

According to the different strategies depicted in Figure 1 and described above, candidate genes are selected using their sequence and/or expression properties. However, it is likely the case that, in the near future, integrative approaches combining several of these features will streamline the candidate gene prioritization process. For example, Carqueijeiro *et al.* [18] combined gene expression and physical clustering analysis to identify an acetyl transferase involved in the biosynthesis of root alkaloids in *C. roseus*. Further automated integrative approaches will undoubtedly facilitate the identification of missing enzymes, as knowledge on plant biosynthetic machineries is rapidly progressing. Because many biosynthetic genes are now fully characterized, this knowledge may indeed drive supervised machine learning (SML)-based approaches to predict gene function [19]. A training set representing 80% of a transcriptome labeled as following: characterized genes or orthologs as 'alkaloid-related' and the remaining ones as 'nonalkaloid'. SML tries to construct the best model from input data to correctly

classify genes with the correct label and predicts possible labels for the remaining 20% of genes. Although promising, fine optimization is required to correctly deploy SML approaches for missing gene identification.

It is important to note that although natural product biosynthesis in plants displays a certain form of homogeneity (coexpression, physical clustering, or sequence homology), some steps are catalyzed by enzymes encoded by genes without obvious distinguishing features, making them difficult to detect through these genomic approaches. In this case, many candidate genes identified in silico must be tested before the desired catalytic activity is discovered. High-throughput techniques to clone candidate genes or more conventional procedures such as native protein purification through cell fractionation must thus be considered as part of the elucidation process. Based on this whole set of approaches, completion of biosynthetic pathways will accelerate in the coming years and, in turn, will facilitate the development of microbial cell factories synthesizing plant-derived drugs [20,21]. This remains an essential prerequisite to be able to propose, in the short term, cheaper and high-throughput alternatives of production of these valuable pharmaceutical compounds.

## Acknowledgments

We acknowledge funding from the EU Horizon 2020 research and innovation program (MIAMI project-Grant agreement N°814645), ARD2020 Biopharmaceutical program of the Région Centre Val de Loire (BioPROPHARM and CatharSIS projects), La ligue Contre le Cancer (Yeast4LiFE), and from le Studium (Consortium fellowship) and ERC 788301. The authors want to apologize for other excellent studies which have not been cited here due to length restriction.

<footnote n="1">¹Biomolécules et Biotechnologies Végétales, BBV, EA2106, Université de Tours, Tours, France</footnote>

<footnote n="2">²Groupe d'Etude des Interactions Hôte-Pathogène, GEIHP, EA3142, Univ Angers, SFR 4208 ICAT, Angers, France</footnote>
⁵Department of Natural Product Biosynthesis, Max Planck Institute for Chemical Ecology, Jena, Germany

*Correspondence: oconnor@ice.mpg.de (S.E. O'Connor) and vincent.courdavault@univ-tours.fr (V. Courdavault).
https://doi.org/10.1016/j.tips.2019.12.006

© 2020 Elsevier Ltd. All rights reserved.

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