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Article
New Cross-Talks between Pathways Involved in Grapevine
Infection with ‘Candidatus Phytoplasma solani’ Revealed by
Temporal Network Modelling
Blaž Škrlj 1,2,* , Maruša Pompe Novak 3,4 , Günter Brader 5, Barbara Anžiˇc 3, Živa Ramšak 3,
Kristina Gruden 3, Jan Kralj 2, Aleš Kladnik 6, Nada Lavraˇc 1,2, Thomas Roitsch 7and Marina Dermastia 3
Citation: Škrlj, B.; Novak, M.P.;
Brader, G.; Anžiˇc, B.; Ramšak, Ž.;
Gruden, K.; Kralj, J.; Kladnik, A.;
Lavraˇc, N.; Roitsch, T.; et al. New
Cross-Talks between Pathways
Involved in Grapevine Infection with
‘Candidatus Phytoplasma solani’
Revealed by Temporal Network
Modelling. Plants 2021,10, 646.
https://doi.org/10.3390/
plants10040646
Academic Editor: Atsushi Fukushima
Received: 1 February 2021
Accepted: 24 March 2021
Published: 29 March 2021
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4.0/).
1Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia; Nada.Lavrac@ijs.si
2Jožef Stefan Institute, 1000 Ljubljana, Slovenia; jan.kralj@ijs.si
3National Institute of Biology, 1000 Ljubljana, Slovenia; marusa.pompe.novak@nib.si (M.P.N.);
barbara.anzic@gmail.com (B.A.); ziva.ramsak@nib.si (Ž.R.); kristina.gruden@nib.si (K.G.);
marina.dermastia@nib.si (M.D.)
4School of Viticulture and Enology, University of Nova Gorica, 5271 Vipava, Slovenia
5Austrian Institute of Technology, Bioresources Unit, 3430 Tulln, Austria; guenter.brader@ait.ac.at
6Department of Biology, Biotechnical Faculty, University of Ljubljana, 1000 Ljubljana, Slovenia;
ales.kladnik@bf.uni-lj.si
7Department of Plant and Environmental Sciences, University of Copenhagen, 2630 Taastrup, Denmark;
roitsch@plen.ku.dk
*Correspondence: blaz.skrlj@ijs.si
Abstract:
Understanding temporal biological phenomena is a challenging task that can be approached
using network analysis. Here, we explored whether network reconstruction can be used to better
understand the temporal dynamics of bois noir, which is associated with ‘Candidatus Phytoplasma
solani’, and is one of the most widespread phytoplasma diseases of grapevine in Europe. We proposed
a methodology that explores the temporal network dynamics at the community level, i.e., densely
connected subnetworks. The methodology offers both insights into the functional dynamics via
enrichment analysis at the community level, and analyses of the community dissipation, as a measure
that accounts for community degradation. We validated this methodology with cases on experimental
temporal expression data of uninfected grapevines and grapevines infected with ‘Ca. P. solani’.
These data confirm some known gene communities involved in this infection. They also reveal
several new gene communities and their potential regulatory networks that have not been linked to
‘Ca. P. solani’ to date. To confirm the capabilities of the proposed method, selected predictions were
empirically evaluated.
Keywords: network analysis; phytoplasma; bois noir; community detection; enrichment analysis
1. Introduction
Bois noir (BN) is an important economic grapevine yellows disease that is caused by
the phytopathogenic bacterium ‘Candidatus Phytoplasma solani’, from the solbur 16SrXII-A
subgroup of the order Acholeplasmatales in the class Mollicutes [
1
]. This phytoplasma
is endemic across a broad Mediterranean region [
2
–
6
], and it has also been reported
from China, Chile and Canada [
6
]. Its spread occurs via a complicated disease cycle that
includes insect vectors and multiple herbaceous plants as phytoplasma reservoirs [
7
,
8
].
In addition, different environmental conditions and grapevine cultivars also contribute to
the development of BN disease [
6
]. Several studies of BN have shown transcriptional and
metabolic changes in host plants [
9
–
16
] that involve the major plant signal transduction
pathways. However, between these ‘highways’ of information flow, there are ‘side-streets’
that interconnect these pathways that are likely to be overlooked by classical methods,
especially in a system as complex as BN. Some approaches to define how to combine these
Plants 2021,10, 646. https://doi.org/10.3390/plants10040646 https://www.mdpi.com/journal/plants
Plants 2021,10, 646 2 of 18
diverse data into a suitable model for explaining the pathogenicity of phytoplasmas that
cause grapevine yellows have been proposed recently [17–20].
The analysis of RNA sequencing (RNA-Seq) data is becoming one of the prevailing
empirical approaches to understanding a wide array of biological systems. In recent years,
it has also become increasingly possible to perform sequencing across time, and thus to
obtain multiple gene expression ‘snapshots’ of the same organism or its tissues that can be
used for the more accurate analysis of time-dependent phenomena, such as the progression
of disease. As molecules in the cell are seldom completely independent, network-based
approaches have been adopted as the tools of choice to study such interconnected systems.
Applied network-based methodologies have shown promising results in many branches
of plant biology, including studies of immunity [
21
] and regulatory pathways [
22
,
23
].
When considering, for example, community enrichment [
24
], drug design [
25
] or the
structural analysis of protein binding sites [
26
,
27
], network-based approaches have also
been applied to organisms other than plants.
In this study, we adopted methods applied for the analysis of such information-rich
structures to the modelling of BN-related natural events. The main contributions of this
study are: (i) a methodology that considers temporal network community dynamics that
is additionally enriched with domain background knowledge in the form of ontologies;
(ii) a scalable algorithm for the reconstruction of scale-free networks that can be used to
reconstruct networks from genome-wide RNA-Seq data; and (iii) the application of the
developed modelling method to a dataset of grapevine samples infected with ‘Ca. P. solani’,
through reconstruction of the networks.
2. Proposed Methodology
An overview of the proposed methodology is shown in Figure 1and is described in
the following paragraphs.
Plants 2021, 10, x FOR PEER REVIEW 2 of 18
methods, especially in a system as complex as BN. Some approaches to define how to
combine these diverse data into a suitable model for explaining the pathogenicity of
phytoplasmas that cause grapevine yellows have been proposed recently [17–20].
The analysis of RNA sequencing (RNA-Seq) data is becoming one of the prevailing
empirical approaches to understanding a wide array of biological systems. In recent years,
it has also become increasingly possible to perform sequencing across time, and thus to
obtain multiple gene expression ‘snapshots’ of the same organism or its tissues that can
be used for the more accurate analysis of time-dependent phenomena, such as the
progression of disease. As molecules in the cell are seldom completely independent,
network-based approaches have been adopted as the tools of choice to study such
interconnected systems. Applied network-based methodologies have shown promising
results in many branches of plant biology, including studies of immunity [21] and
regulatory pathways [22,23]. When considering, for example, community enrichment [24],
drug design [25] or the structural analysis of protein binding sites [26,27], network-based
approaches have also been applied to organisms other than plants.
In this study, we adopted methods applied for the analysis of such information-rich
structures to the modelling of BN-related natural events. The main contributions of this
study are: (i) a methodology that considers temporal network community dynamics that
is additionally enriched with domain background knowledge in the form of ontologies;
(ii) a scalable algorithm for the reconstruction of scale-free networks that can be used to
reconstruct networks from genome-wide RNA-Seq data; and (iii) the application of the
developed modelling method to a dataset of grapevine samples infected with ‘Ca. P.
solani’, through reconstruction of the networks.
2. Proposed Methodology
An overview of the proposed methodology is shown in Figure 1 and is described in
the following paragraphs.
Figure 1. Schematic overview of the proposed approach. First, the RNA sequencing (RNA-Seq) expression networks are
used to reconstruct four different regulatory networks, each corresponding to a particular phenotype uninfected with ‘Ca.
Figure 1.
Schematic overview of the proposed approach. First, the RNA sequencing (RNA-Seq) expression networks are
used to reconstruct four different regulatory networks, each corresponding to a particular phenotype uninfected with ‘Ca.
P. solani’ (U) or infected with ‘Ca. P. solani’ (I), or the time of sequencing (t1, t2). The proposed methodology enables
the exploration of eight main directions, depending on the time and phenotype considered (vertical lines, V), and also an
exploration of differences between phenotypes within the same time frame (horizontal lines, H). D, dissipation, e.g., DVU,
dissipation-vertical-uninfected; DVI, dissipation-vertical-infected.
Plants 2021,10, 646 3 of 18
2.1. Network Reconstruction
The purpose of network reconstruction is to explore higher-order feature (gene) in-
teractions, attempting to overcome the independence assumption adopted many times
in conventional statistical analysis. The field of network reconstruction has shown great
promise for the analysis of biological data sets; namely, the methods such as LionessR [
28
],
RAVEN [
29
,
30
], WGCNA and community-based reconstruction [
31
] were shown to accu-
rately reconstruct yeast and human metabolic networks, offering novel insights into the
space of potentially interesting biomarkers and new pathways.
The proposed methodology uses RNA-Seq expression data based on the normalised
transcript counts, as commonly used throughout RNA-Seq experiments [32].
The expression data were initially pre-processed as follows. Only expressions, greater
than a certain user-defined threshold, were used for edge construction—the process of
inferring how a given pair of, e.g., genes (an edge), is connected. Here, 80% of the most
expressed genes were selected, and the others were discarded (the 20th percentile was
used as the threshold). This threshold was determined based on space requirements
of the following steps in the network construction. For each gene, logarithms of the
Euclidean distances betweenits and the remaining expression vectors were computed in
a pairwise manner. The additional log step was performed due to high variability in the
expression vectors, resulting in a large spectrum of possible distances at different scales.
Hence, for each pair of genes, the distance that indicates the difference in their expression
was recorded.
The obtained matrix was then used for the reconstruction of the regulatory network
(Figure 2). The key step in the network reconstruction is the identification of a distance
threshold (i.e., the edge weight threshold), so that the resulting network is scale-free.
By incrementally relaxing this threshold, more distant nodes become included in the final
network, as lower distance thresholds permit the presence of edges corresponding to
similar expression pairs. A search was implemented for possible networks, based on the
previous studies of Rice et al. (2005) and Angulo et al. (2017) [
33
,
34
], with a comprehensive
overview of the limitations of such approaches described elsewhere [
35
]. A user-specified
interval of thresholds was automatically explored, where a network was constructed for
each of the thresholds and was statistically evaluated for the scale-free properties. The scale-
free networks are governed by the power-law distribution of the, e.g., node degrees in our
case. This means that not all nodes are equally connected; thus, some are significantly more
central than the other ones. Furthermore, in real-life scale-free networks, the presence of
communities is often noted, because communities represent key functional aspects of a
given network. The range of thresholds initially explored spanned from the 50th to the
99th percentiles of possible distances; however, this initial threshold range did not yield
any scale-free networks and it was constrained to the final range between the 10th and
20th percentiles, which resulted in networks with distinct community structures suitable
for further analysis. Although lower thresholds could be explored to include more less-
expressed genes, the considered thresholds were within the limits of the used hardware,
as described as follows. The processor used was an Intel(R) Xeon(R) Gold 6150 CPU @
2.70 GHz (64 bit). Furthermore, the machine had 32 GB of RAM. Note that the worst-case
computational complexity of the network construction is O(|N|ˆ2), where N is the set of
network nodes. Such graphs (cliques), albeit not being present in nature, can emerge as
artefacts if too low thresholds (too many noisy genes) are considered, which we avoided
with the considered threshold sets.
Plants 2021,10, 646 4 of 18
Plants 2021, 10, x FOR PEER REVIEW 4 of 18
network reconstruction procedure was suitable for the following reasons. First, the initial
attempts with correlation-based reconstruction yielded over-saturated networks, where
too many links were created. Such networks had a negative impact on the subsequent
community detection step’s computation time. Second, the scale-free test offered an
automated procedure, which is more optimal than manual threshold identification, as
more networks are explored. The scale-free assumption, however stringent, has been
considered in the previous work [33,34] and yielded satisfactory networks. The distances
were effectively computed via the SciPy package, version 1.5.4 and Numpy package,
version 1.19.4. The storage of the networks was implemented via the NetworkX library,
version 2.5 [36].
Figure 2. Schematic overview of the network (re)construction process.
2.2. Community Enrichment
Conceptually, community-based semantic subgroup discovery (CBSSD) consists of
two main steps, i.e., community detection followed by a one-versus-all enrichment
procedure, which here, was additionally corrected for multiple hypothesis testing. We
refer the reader to Škrlj et al. (2019) [24] for a more detailed overview of the enrichment
process and for additional theoretical insights. Here, we used the method ‘as-is’, with the
default hyperparameter settings including Bonferroni’s multiple test correction and the
significance threshold of 0.05 (Fisher’s exact test). The tests were implemented via the
statsmodels library, version 0.12.1 [37]. The background knowledge, however, needed to
be specifically adapted, as grapevine is not a standard model organism, and as such, it is
not well represented in conventional ontologies, such as Gene Ontology. For this purpose,
we adopted the GoMapMan ontology, as discussed in Ramšak et al. (2014) [38].
2.2.1. Community Detection
In the first step, the state-of-the-art community detection algorithm Infomap 1.3.1
[39] was used, resulting in nonoverlapping network partitioning—the clustering of the
network nodes into units of hundreds of nodes (or more). The algorithm was run for 500
iterations with default hyperparameters to obtain stable community estimates. We
compiled the C++ version of the stable release of the codebase found at
https://github.com/mapequation/infomap (14 March 2021). The main rationale for this
Figure 2. Schematic overview of the network (re)construction process.
As soon as suitable scale-free properties were identified, the network search was termi-
nated, and this constructed network was used for further analysis. The considered network
reconstruction procedure was suitable for the following reasons. First, the initial attempts
with correlation-based reconstruction yielded over-saturated networks, where too many
links were created. Such networks had a negative impact on the subsequent community de-
tection step’s computation time. Second, the scale-free test offered an automated procedure,
which is more optimal than manual threshold identification, as more networks are ex-
plored. The scale-free assumption, however stringent, has been considered in the previous
work [
33
,
34
] and yielded satisfactory networks. The distances were effectively computed
via the SciPy package, version 1.5.4 and Numpy package, version 1.19.4. The storage of the
networks was implemented via the NetworkX library, version 2.5 [36].
2.2. Community Enrichment
Conceptually, community-based semantic subgroup discovery (CBSSD) consists of
two main steps, i.e., community detection followed by a one-versus-all enrichment pro-
cedure, which here, was additionally corrected for multiple hypothesis testing. We refer
the reader to Škrlj et al. (2019) [
24
] for a more detailed overview of the enrichment pro-
cess and for additional theoretical insights. Here, we used the method ‘as-is’, with the
default hyperparameter settings including Bonferroni’s multiple test correction and the
significance threshold of 0.05 (Fisher’s exact test). The tests were implemented via the
statsmodels library, version 0.12.1 [
37
]. The background knowledge, however, needed to
be specifically adapted, as grapevine is not a standard model organism, and as such, it is
not well represented in conventional ontologies, such as Gene Ontology. For this purpose,
we adopted the GoMapMan ontology, as discussed in Ramšak et al. (2014) [38].
2.2.1. Community Detection
In the first step, the state-of-the-art community detection algorithm Infomap 1.3.1 [
39
]
was used, resulting in nonoverlapping network partitioning—the clustering of the network
nodes into units of hundreds of nodes (or more). The algorithm was run for 500 iterations
with default hyperparameters to obtain stable community estimates. We compiled the C++
version of the stable release of the codebase found at https://github.com/mapequation/
Plants 2021,10, 646 5 of 18
infomap (14 March 2021). The main rationale for this step was that communities were
shown to correspond to functional network clusters; hence, performing the analysis at
this level has the potential to detect sets of genes that are strongly associated with a
given process. Once the communities were identified, we proceeded to work with those
comprising more than five nodes but no more than 30. The rationale behind this decision
was to emphasise communities with a significant functional annotation that could also
be inspected by a domain expert, as very large communities are not necessarily easily
inspectable and associated with specific processes. However, finding communities with
still unknown associations with specific processes was a key focus of this study.
2.2.2. Community Enrichment
Communities were subjected to standard term enrichment, where Fisher’s exact tests
were used to determine whether a given semantic term (i.e., specific process in Figure 3)
can be associated with particular communities, and not to others. The p-values obtained
from the Fisher’s exact tests were additionally corrected using Bonferroni’s multiple test
corrections, to allow for simultaneous testing of multiple hypotheses.
Plants 2021, 10, x FOR PEER REVIEW 5 of 18
step was that communities were shown to correspond to functional network clusters;
hence, performing the analysis at this level has the potential to detect sets of genes that
are strongly associated with a given process. Once the communities were identified, we
proceeded to work with those comprising more than five nodes but no more than 30. The
rationale behind this decision was to emphasise communities with a significant functional
annotation that could also be inspected by a domain expert, as very large communities
are not necessarily easily inspectable and associated with specific processes. However,
finding communities with still unknown associations with specific processes was a key
focus of this study.
2.2.2. Community Enrichment
Communities were subjected to standard term enrichment, where Fisher’s exact tests
were used to determine whether a given semantic term (i.e., specific process in Figure 3)
can be associated with particular communities, and not to others. The p-values obtained
from the Fisher’s exact tests were additionally corrected using Bonferroni’s multiple test
corrections, to allow for simultaneous testing of multiple hypotheses.
Figure 3. Overview of the community-based semantic subgroup discovery (CBSSD) methodology. Given a complex
network, CBSSD offers the functional enrichment of communities within the network. The functional enrichment
corresponds to the assignment of expert-derived process descriptions to parts of the communities.
2.3. Community Dissipation
One of the novel contributions of this work is the concept of community dissipation
(Figure 4), as the process of the loss of integrity of a given community. In this section, we
first present the formal definition of the dissipation index, followed by its generalisation
to arbitrary time series of (multiplex) networks. We next present an algorithm that
computes the proposed measure efficiently.
Figure 3.
Overview of the community-based semantic subgroup discovery (CBSSD) methodology.
Given a complex network, CBSSD offers the functional enrichment of communities within the
network. The functional enrichment corresponds to the assignment of expert-derived process
descriptions to parts of the communities.
2.3. Community Dissipation
One of the novel contributions of this work is the concept of community dissipation
(Figure 4), as the process of the loss of integrity of a given community. In this section,
we first present the formal definition of the dissipation index, followed by its generalisation
to arbitrary time series of (multiplex) networks. We next present an algorithm that computes
the proposed measure efficiently.
Plants 2021,10, 646 6 of 18
Plants 2021, 10, x FOR PEER REVIEW 6 of 18
Figure 4. Community dissipation. Community dissipation corresponds to the measurement of how
different a given community is across a pair of time points. For example, here, the brown (left)
community becomes part of the red community (right), where the yellow community with a single
node (left) disappears and becomes part of the red community (right).
Dissipation between Two Time Points
Let G1 = (N, E1) and G2 = (N, E2) represent two undirected networks that correspond
to two time points, t1 and t2. Let φ: G → P represent the mapping from a network to a
given partition of the network nodes. We refer to this mapping as community detection.
Assume P1 and P2 represent two partitions, N1 and N2, respectively. We are interested
in the elements of p ∈ P1, when observed together in P2. We propose a measure that
summarises the behaviour of all of the communities (P1) when their elements are assessed
in P2. Intuitively, the proposed community dissipation operates as follows. For each node
of a given community, p, its presence in the second network communities is recorded. For
each community of the second network that contains at least one node from the origin
community, the fraction of the covered community is recorded. We refer to this score as
the coverage (cov), defined as in Equation (1):
𝑐𝑜𝑣𝑝,𝑃2={|𝑘∩𝑝|
|𝑝|;𝑘∩𝑝≠∅}∈ (1)
We can finally compute the dissipation index (dis), as in Equation (2):
𝑑𝑖𝑠𝑝,𝑃2= −𝑙𝑛𝑚𝑎𝑥𝑐𝑜𝑣𝑝,𝑃2
|𝑐𝑜𝑣𝑝,𝑃2| (2)
This index thus results in high values if the observed community is different in the
consequent time point—either it is smaller or it is absorbed into a larger community. Both
events result in high dissipation. Similarly, low dissipation indicates a stable community
structure. The dissipation index is used alongside the enrichment analysis to assess
temporal changes at the functional level. The proposed dissipation index is
complementary to the manual inspection of communities at different time points that can
be a cost- and time-consuming process. An alternative approach to using the dissipation
index can include temporal community detection [40]; however, this method is not
necessarily suitable for the data-scarce regime with only two time points considered in
this work. The indication that the post-hoc analysis of communities is more suitable for
analysis was also recently demonstrated when studying fire events in a portion of the
Amazon basin [41].
Figure 4.
Community dissipation. Community dissipation corresponds to the measurement of how
different a given community is across a pair of time points. For example, here, the brown (
left
)
community becomes part of the red community (
right
), where the yellow community with a single
node (left) disappears and becomes part of the red community (right).
Dissipation between Two Time Points
Let G1 = (N, E1) and G2 = (N, E2) represent two undirected networks that correspond
to two time points, t1 and t2. Let
ϕ
: G
→
P represent the mapping from a network to a
given partition of the network nodes. We refer to this mapping as community detection.
Assume P1 and P2 represent two partitions, N1 and N2, respectively. We are interested
in the elements of p
∈
P1, when observed together in P2. We propose a measure that
summarises the behaviour of all of the communities (P1) when their elements are assessed
in P2. Intuitively, the proposed community dissipation operates as follows. For each node
of a given community, p, its presence in the second network communities is recorded.
For each community of the second network that contains at least one node from the origin
community, the fraction of the covered community is recorded. We refer to this score as the
coverage (cov), defined as in Equation (1):
cov(p,P2)=|k∩p|
|p|;k∩p6=∅k∈P2
(1)
We can finally compute the dissipation index (dis), as in Equation (2):
dis(p,P2)=−l nmax[cov(p,P2)]
|cov(p,P2)|(2)
This index thus results in high values if the observed community is different in the
consequent time point—either it is smaller or it is absorbed into a larger community.
Both events result in high dissipation. Similarly, low dissipation indicates a stable commu-
nity structure. The dissipation index is used alongside the enrichment analysis to assess
temporal changes at the functional level. The proposed dissipation index is complementary
to the manual inspection of communities at different time points that can be a cost- and
time-consuming process. An alternative approach to using the dissipation index can in-
clude temporal community detection [
40
]; however, this method is not necessarily suitable
for the data-scarce regime with only two time points considered in this work. The indi-
cation that the post-hoc analysis of communities is more suitable for analysis was also
recently demonstrated when studying fire events in a portion of the Amazon basin [41].
Plants 2021,10, 646 7 of 18
2.4. Applications of the Methodology
There are several distinct possible applications of this methodology. An outline of one
possible use is shown in Figure 1, and it is discussed in more detail in this section.
2.4.1. Temporal Enrichment
Given a pair of networks, the proposed methodology allows the exploration of commu-
nities within these networks that persist (i.e., have low dissipation indices) and those that
break apart (i.e., have high dissipation indices). For example, the first network corresponds
to the set of grapevine samples collected early in the growing season, and the second
network corresponds to the set of grapevine samples collected late in the growing season.
As the raw information obtained was not directly useful for domain experts, the CBSSD
step used for the enrichment of the first network offers additional functional insight that
would otherwise not be accessible. For example, the monitoring of a community that is
distinctly described using terms related to the process of photosynthesis or carbohydrate
metabolism can be assessed.
2.4.2. Phenotype Comparison
An alternative application of the proposed methodology relates to a pair of different
phenotypes at the same time, e.g., uninfected grapevine samples and grapevine samples
infected with ‘Ca. P. solani’. In this case, enrichment via CBSSD is conducted in the same
manner as for the temporal enrichment; however, the communities are compared with
respect to a given phenotype and not to time. Such comparisons can unveil communities
that are coherent in uninfected plants, but dissipated in infected plants, or vice versa.
Note that two such comparisons need to be manually inspected by the domain experts.
The code to replicate the methodology is freely available at: https://gitlab.com/skblaz/
community-enrichment-phytoplasma (accessed on 20 March 2021).
3. Evaluation of the Methodology
The proposed methodology was applied to a subset of the RNA-Seq data set of
uninfected and infected grapevine (Vitis vinifera) cv. Zweigelt samples collected from a
production vineyard. In the present study, we focused on the samples collected late in
the growing season, when symptoms of BN were prominent on the grapevines infected
with ‘Ca. P. solani’. The sample descriptions and processing are described in detail in the
Supplementary Methods. Gene set enrichment analyses, multidimensional scaling and
principal component analysis based on the same samples were performed in the frame of
study by Dermastia et al. 2021 [
42
]. In this study, normalised counts were subjected to the
proposed methodology described in Section 2.
This analysis demonstrated not only some associations that were known from previous
studies of BN, but also some new associations, which can serve for the design of novel
studies (Table 1).
Plants 2021,10, 646 8 of 18
Table 1.
Examples of the enriched communities with a direction of analysis from infected to uninfected grapevines late in
the growing season, where the dissipation index was 0.48. Each community was annotated with the members from the
GoMapMan ontology [
38
], providing direct insight into their associated processes. The analysed data were differentially
expressed genes from uninfected grapevines cv. Zweigelt and from samples infected with ‘Ca. P. solani’. For each
mRNA sequence, the differences in expression between phytoplasma-infected and -uninfected plants were calculated as
log
2
FC. Only mRNAs with a false discovery rate (FDR) adjusted p-value < 0.05 were considered as differentially expressed.
U, uninfected samples; I, samples infected with ‘Ca. P. solani’.
Community Bin Annotating the Community Gene ID RNA Description log2FC
I: U
Adjusted
p-Value (FDR)
11.1.1.1 PS.lightreaction.photosystem
II.LHC-II Vitvi07g03081
Cinnamyl alcohol dehydrogenase
8|Chr4:17855964-17857388
FORWARD LENGTH = 359|201606
−1.40 0.058
1.1.1.1 PS.lightreaction.photosystem
II.LHC-II Vitvi01g01482 BHLH transcription factor-like
protein −2.15 0.003
1.1.1.1 PS.lightreaction.photosystem
II.LHC-II Vitvi08g02107
Dihydrofolate
reductase|Chr4:12612554-12613586
FORWARD LENGTH = 261|201606
2.31 0.000
1.1.1.1 PS.lightreaction.photosystem
II.LHC-II Vitvi07g02188
Glutathione S-transferase family
protein|Chr3:23217425-23218246
REVERSE LENGTH = 219|201606 3.24 0.000
1.1.1.1 PS.lightreaction.photosystem
II.LHC-II Vitvi10g00740
Chlorophyll A/B binding protein
1|Chr1:10478071-10478874
FORWARD LENGTH = 267|201606
−2.80 0.001
1.1.1.1 PS.lightreaction.photosystem
II.LHC-II Vitvi11g00097 Unknown protein 4.36 0.000
1.1.1.1 PS.lightreaction.photosystem
II.LHC-II Vitvi03g01524
Cytochrome P450%2C family
82%2C subfamily C%2C
polypeptide 2 2.73 0.000
1.1.1.1 PS.lightreaction.photosystem
II.LHC-II Vitvi05g01860 No description 4.55 0.026
1.1.1.1 PS.lightreaction.photosystem
II.LHC-II Vitvi16g00810
Protein kinase superfamily
protein|Chr1:24961634-24963941
REVERSE LENGTH = 663|201606
−3.20 0.000
22.1.2.1 major
CHO.metabolism.synthesis.starch.AGPase
Vitvi00g01098 Leucine-rich receptor-like protein
kinase family protein|201606 1.30 0.002
2.1.2.1 major
CHO.metabolism.synthesis.starch.AGPase
Vitvi18g02758 ADPGLC-PPase large subunit 1.25 0.002
2.1.2.1 major
CHO.metabolism.synthesis.starch.AGPase
Vitvi06g00956 Aldehyde oxidase 1 −0.86 0.036
2.1.2.1 major
CHO.metabolism.synthesis.starch.AGPase
Vitvi18g00445
Ascorbate peroxidase
4|Chr4:5777502-5779064 REVERSE
LENGTH = 284|201606
−1.64 0.000
2.1.2.1 major
CHO.metabolism.synthesis.starch.AGPase
Vitvi18g02012
UDP-glucosyl transferase
88A1|Chr3:5618847-5620833
REVERSE LENGTH = 446|201606
−1.31 0.005
17.1.1.1.12 hormone
metabolism.abscisic
acid.aldehyde.oxidase
Vitvi08g01043
RING/U-box superfamily
protein|Chr5:24354298-24356706
FORWARD LENGTH = 487|201606
1.79 0.000
17.1.1.1.12 hormone
metabolism.abscisic
acid.aldehyde.oxidase
Vitvi18g02758
ADPGLC-PPase large
subunit|Chr1:9631630-9634450
FORWARD LENGTH = 518|201606
1.25 0.002
17.1.1.1.12 hormone
metabolism.abscisic
acid.aldehyde.oxidase
Vitvi06g00956
Aldehyde oxidase
1|Chr5:7116783-7122338
FORWARD LENGTH =
1368|201606
−0.86 0.036
17.1.1.1.12 hormone
metabolism.abscisic
acid.aldehyde.oxidase
Vitvi18g00445
Ascorbate peroxidase
4|Chr4:5777502-5779064 REVERSE
LENGTH = 284|201606
−1.64 0.000
17.1.1.1.12 hormone
metabolism.abscisic
acid.aldehyde.oxidase
Vitvi18g02012
UDP-glucosyl transferase
88A1|Chr3:5618847-5620833
REVERSE LENGTH = 446|201606
−1.31 0.005
21.2.1 redox.ascorbate and
glutathione.ascorbate Vitvi00g01098 Leucine-rich receptor-like protein
kinase family protein|201606 1.30 0.002
21.2.1 redox.ascorbate and
glutathione.ascorbate Vitvi08g01043
RING/U-box superfamily
protein|Chr5:24354298-24356706
FORWARD LENGTH = 487|201606
1.79 0.000
Plants 2021,10, 646 9 of 18
Table 1. Cont.
Community Bin Annotating the Community Gene ID RNA Description log2FC
I: U
Adjusted
p-Value
(FDR)
21.2.1 redox.ascorbate and
glutathione.ascorbate
Vitvi18g02758
ADPGLC-PPase large
subunit|Chr1:9631630-9634450
FORWARD LENGTH =
518|201606
1.25 0.002
21.2.1 redox.ascorbate and
glutathione.ascorbate
Vitvi06g00956
Aldehyde oxidase
1|Chr5:7116783-7122338
FORWARD LENGTH =
1368|201606
−8.86 0.036
21.2.1 redox.ascorbate and
glutathione.ascorbate
Vitvi18g00445
Ascorbate peroxidase
4|Chr4:5777502-5779064
REVERSE LENGTH =
284|201606
−1.64 0.000
21.2.1 redox.ascorbate and
glutathione.ascorbate
Vitvi18g02012
UDP-glucosyl transferase
88A1|Chr3:5618847-5620833
REVERSE LENGTH =
446|201606
−1.31 0.005
3.1. Recovery of Empirically Validated Community Information
After the application of a new modelling method to the transcriptional data of these
samples, several communities of genes from different metabolic pathways were formed
and visualised with Py3plex [43] (Figure 5).
Plants 2021, 10, x FOR PEER REVIEW 9 of 18
21.2.1 redox.ascorbate and gluta-
thione.ascorbate Vitvi08g01043
RING/U-box superfamily pro-
tein|Chr5:24354298-24356706 FOR-
WARD LENGTH = 487|201606
1.79 0.000
21.2.1 redox.ascorbate and gluta-
thione.ascorbate Vitvi18g02758
ADPGLC-PPase large subu-
nit|Chr1:9631630-9634450 FOR-
WARD LENGTH = 518|201606
1.25 0.002
21.2.1 redox.ascorbate and gluta-
thione.ascorbate Vitvi06g00956
Aldehyde oxidase 1|Chr5:7116783-
7122338 FORWARD LENGTH =
1368|201606
−8.86 0.036
21.2.1 redox.ascorbate and gluta-
thione.ascorbate Vitvi18g00445
Ascorbate peroxidase
4|Chr4:5777502-5779064 REVERSE
LENGTH = 284|201606
−1.64 0.000
21.2.1 redox.ascorbate and gluta-
thione.ascorbate Vitvi18g02012
UDP-glucosyl transferase
88A1|Chr3:5618847-5620833 RE-
VERSE LENGTH = 446|201606
−1.31 0.005
3.1. Recovery of Empirically Validated Community Information
After the application of a new modelling method to the transcriptional data of these
samples, several communities of genes from different metabolic pathways were formed
and visualised with Py3plex [43] (Figure 5).
Figure 5. Reconstructed network where several communities of genes from different metabolic
pathways are formed. These were visualised with Py3plex and are shown in different colours.
There is growing evidence that the phytoplasma infection of grapevines is
characterised by severely affected photosynthesis and carbohydrate metabolism
pathways [16,38]. In good correlation with these data, the present modelling of the data
from the samples infected with ‘Ca. P. solani’ late in the growing season revealed two
main communities that were associated with these two pathways. At the late growing
season time point, these communities significantly disintegrated with a dissipation index
of 0.48 (highest 30% of all dissipations) for the uninfected samples (Table 1). The
communities revealed comprised some genes associated with photosynthesis or
carbohydrate metabolism that have been detected before in phytoplasma-infected plants.
Their re-discovery by the applied model assured us that the method is relevant for such
analysis, and that new genes that have not yet been associated with these processes and
were involved in these communities might also have biologically meaningful functions.
Moreover, they may present a new reference framework for further research of
phytoplasma-infected plants.
Figure 5.
Reconstructed network where several communities of genes from different metabolic
pathways are formed. These were visualised with Py3plex and are shown in different colours.
There is growing evidence that the phytoplasma infection of grapevines is charac-
terised by severely affected photosynthesis and carbohydrate metabolism pathways [
16
,
38
].
In good correlation with these data, the present modelling of the data from the samples
infected with ‘Ca. P. solani’ late in the growing season revealed two main communities
that were associated with these two pathways. At the late growing season time point,
these communities significantly disintegrated with a dissipation index of 0.48 (highest 30%
of all dissipations) for the uninfected samples (Table 1). The communities revealed com-
prised some genes associated with photosynthesis or carbohydrate metabolism that have
been detected before in phytoplasma-infected plants. Their re-discovery by the applied
Plants 2021,10, 646 10 of 18
model assured us that the method is relevant for such analysis, and that new genes that
have not yet been associated with these processes and were involved in these communities
might also have biologically meaningful functions. Moreover, they may present a new
reference framework for further research of phytoplasma-infected plants.
3.1.1. A Community of Genes Associated with Photosystem II
In previous studies of phytoplasma diseases in general, and of BN in particular, it has
been shown that several genes or their protein products involved in different steps of pho-
tosynthesis were down-regulated upon phytoplasma infection [
10
,
12
,
44
–
46
]. Among these,
the transcript levels of 11 genes that encode some of the most abundant proteins of the
chloroplasts, chlorophylls a/b binding proteins in photosystem II, were significantly de-
creased in the leaves of grapevine cv. Chardonnay infected with ‘Ca. P. solani’ [
10
]. Some of
the genes that encode chlorophyll a/b binding proteins were also down-regulated in
phytoplasma-infected coconut, and the chlorophyll a/b binding protein 5 protein levels
were lower in phytoplasma-infected Paulownia fortunei [
47
,
48
]. Consistent with previous
results is the detected significant down-regulation of Vitvi10g00740, which encodes chloro-
phyll a/b binding protein 1, in grapevine cv. Zweigelt infected with ‘Ca. P. solani’ in
comparison with the uninfected grapevines (Table 1).
Another gene detected in the community was Vitvi11g00097, which encodes a MYB
domain transcription factor, as one of several regulators of the general branch and differ-
ent branches of flavonoid biosynthesis. Its transcript levels significantly increased in the
grapevines infected with ‘Ca. P. solani’, compared to the uninfected grapevines (Table 1).
It can be noted that several genes involved in flavonoid biosynthesis (as well as their prod-
ucts) have been shown to be up-regulated in grapevines infected with ‘Ca. P. solani’ [
9
,
16
].
While Vitvi11g00097 has never been associated with phytoplasma infections of grapevine
before, it shows large divergent changes in its transcript levels during grapevine berry
development and under different light-exposure treatments of the grapevines [
49
]. Its high
increase in expression suggests an important role in phytoplasma pathogenicity. A signif-
icant up-regulation was also detected for a gene from the cytochrome P450 superfamily
(Table 1). This is an ancient superfamily that has been identified in all domains of organ-
isms [
50
]. Its members are involved in multiple metabolic pathways with distinct and
complex functions, and they have important roles in a vast array of reactions. As a result,
in plants, numerous secondary metabolites are synthesised that function as growth and de-
velopmental signals, or that can protect plants from various biotic and abiotic stresses [
50
].
The expression of the cytochrome P450 genes shows temporal variation [
51
]. In association
with phytoplasmas, they have been shown to have a role in the ‘Ca. P. ulmi’-infected
leafhopper Amplicephalus curtulus [52].
The ubiquitous enzyme dihydrofolate reductase is a key enzyme in the folate biosyn-
thetic pathway [
53
], and it has an essential role in the synthesis of DNA precursors and
some amino acids [
54
]. Despite its importance, information about plant dihydrofolate
reductase is scarce [
53
]. On the other hand, it is known that humans and other animals can-
not synthesise folates de novo, and thus rely on their diets for folate intake [
55
]. Moreover,
the completely sequenced phytoplasma genomes [
56
] provide evidence that phytoplasmas
are experiencing an ongoing evolutionary process, whereby they are losing the ability to
synthesise folate, and consequently, they must rely on their host for folate repletion [
57
].
How the increase in dihydrofolate reductase gene transcript levels in infected grapevines
compared to uninfected grapevines (Table 1) are related to the requirement of phytoplasma
to obtain folate is an interesting point for further research. In addition, the role of the
significantly up-regulated gene Vitvi05g01860 (Table 1) is currently unknown.
3.1.2. A Community of Genes Associated with Pathways That Are Usually Not Considered
to Interact Directly
The second community detected by this modelling is more complex (Table 1, Figure 6),
and comprises several genes that are associated with different metabolic pathways, includ-
ing starch biosynthesis, abscisic acid synthesis/degradation, ascorbate, and glutathione.
Plants 2021,10, 646 11 of 18
As a proof of concept, in this community, the modelling revealed the up-regulation of the
gene that encodes a large subunit of ADP-glucose pyrophosphorylase (AGPase) (Table 1).
This transcript is involved in starch biosynthesis, and its increase has been shown before
in grapevines infected with phytoplasmas. The increased expression of this AGPase gene
was detected in grapevine cv. Chardonnay infected with ‘Ca. P. solani’ [
20
], as well as in
cv. ‘Modra frankinja’ (syn. ‘Blaufränkisch’) infected with phytoplasma Flavescence dorée,
where the activity of its corresponding enzyme was also detected, together with increased
starch concentrations [
58
]. Similarly, a role for ascorbate peroxidase (Table 1) in grapevine
phytoplasma infections has been shown before [59–62].
Plants 2021, 10, x FOR PEER REVIEW 11 of 18
3.1.2. A Community of Genes Associated with Pathways That Are Usually not
Considered to Interact Directly
The second community detected by this modelling is more complex (Table 1, Figure
6), and comprises several genes that are associated with different metabolic pathways,
including starch biosynthesis, abscisic acid synthesis/degradation, ascorbate, and
glutathione. As a proof of concept, in this community, the modelling revealed the up-
regulation of the gene that encodes a large subunit of ADP-glucose pyrophosphorylase
(AGPase) (Table 1). This transcript is involved in starch biosynthesis, and its increase has
been shown before in grapevines infected with phytoplasmas. The increased expression
of this AGPase gene was detected in grapevine cv. Chardonnay infected with ‘Ca. P.
solani’ [20], as well as in cv. ‘Modra frankinja’ (syn. ‘Blaufränkisch’) infected with
phytoplasma Flavescence dorée, where the activity of its corresponding enzyme was also
detected, together with increased starch concentrations [58]. Similarly, a role for ascorbate
peroxidase (Table 1) in grapevine phytoplasma infections has been shown before [59–62].
Figure 6. A community of six genes associated with three different metabolic processes correspond to Community 2 in
Table 1. The coloured squares indicate gene-process associations.
Additional genes revealed by the applied method in the second community have not
been considered in plants infected with phytoplasmas nor in metabolic processes that
interact directly. A significantly up-regulated gene in this community encodes for a
protein from the RING/U-box superfamily (Table 1). In Arabidopsis thaliana, this gene was
reported to be an effector of abscisic acid accumulation after induced drought [63].
Therefore, the association of the RING/U-box superfamily protein with the abscisic acid
pathway in grapevines infected with ‘Ca. P. solani’ seen here is of importance. Previously,
there were only two studies of phytoplasma-infected paulownia plants that identified
some differentially expressed genes or proteins involved in abscisic acid metabolism,
which were based on high-throughput RNA-Seq and proteomic analysis, although these
provided no verification of their clear function [64,65].
In all living organisms, many cellular signal transduction pathways are mediated by
receptor-like kinases. The largest group of plant receptor-like kinases is the leucine-rich
repeat receptor-like kinases [66], which have roles in plant development and the defence
against pathogens [67–69]. The leucine-rich repeat receptor-like kinase family protein was
Figure 6.
A community of six genes associated with three different metabolic processes correspond to Community 2 in
Table 1. The coloured squares indicate gene-process associations.
Additional genes revealed by the applied method in the second community have
not been considered in plants infected with phytoplasmas nor in metabolic processes
that interact directly. A significantly up-regulated gene in this community encodes for
a protein from the RING/U-box superfamily (Table 1). In Arabidopsis thaliana, this gene
was reported to be an effector of abscisic acid accumulation after induced drought [
63
].
Therefore, the association of the RING/U-box superfamily protein with the abscisic acid
pathway in grapevines infected with ‘Ca. P. solani’ seen here is of importance. Previously,
there were only two studies of phytoplasma-infected paulownia plants that identified some
differentially expressed genes or proteins involved in abscisic acid metabolism, which were
based on high-throughput RNA-Seq and proteomic analysis, although these provided no
verification of their clear function [64,65].
In all living organisms, many cellular signal transduction pathways are mediated by
receptor-like kinases. The largest group of plant receptor-like kinases is the leucine-rich
repeat receptor-like kinases [
66
], which have roles in plant development and the defence
against pathogens [
67
–
69
]. The leucine-rich repeat receptor-like kinase family protein was
up-regulated in the infected grapevine samples in the present study (Table 1). However,
the detection in this community and the biological function here are currently not clear.
Plants 2021,10, 646 12 of 18
3.1.3. Community Enrichment and the Classical Differential Expression Analysis
The proposed methodology operates at the level of individual community-term as-
sociations. To further study the relation between the conventional enrichment and the
enriched communities, we performed additional analyses discussed next.
First, we compared the community-based results to those obtained by a differen-
tial expression analysis (detailed in Supplemental Table S1). To see whether our com-
munity approach captures a higher-than-expected number of differentially expressed
genes, a
2×2
contingency table was prepared to compare differentially and nondifferen-
tially expressed genes present in the gene list that we obtain from a differential expres-
sion analysis (
Supplemental Table S1A
) and genes present in our calculated communities
(
Supplemental Table S1B,C
). Prior to that, genes from communities of both directions
(
Table S1B
, uninfected vs. infected; Table S1C; infected vs. infected) were merged into a
single list (as differential expression analysis detects differentially expressed genes in both
directions as well; Table S1A) and analysed as a whole. Fisher’s Exact Test for Count Data
was used, where we observed that there are significantly more differentially expressed
genes present in the final set of communities when compared to the remaining set of genes
from a differential expression analysis (corrected p-value of 2.084
×
10
−14
). This result
indicates that differentially expressed genes are more likely to be present in multi-gene
communities than not, where they potentially act as key nodes that maintain a given
community’s structure. Although communities cover a smaller amount of genes (310;
Table S1B,C
) compared to the full set of differentially expressed genes (FDR p-value
≤
0.05;
6115 differentially expressed genes; Table S1A), it represents a complementary method
from the interpretation standpoint, similarly to the, e.g., gene set enrichment analysis.
In the second step, we aimed to observe the proportion of differentially expressed and
differentially nonexpressed genes in each community separately (
Supplemental Table S1B,C
).
Results show that the method is not only able to capture communities with a high number
of differentially expressed genes (14 out of 39, where >75% of genes in the community
are differentially expressed), but it also captures many (7 out of 39) that do not contain a
single differentially expressed gene (Figure 7a). In addition, while the method does result
in communities of varying sizes, the proportion of differentially expressed genes does rise
with respect to the detected community size.
Plants 2021, 10, x FOR PEER REVIEW 12 of 18
up-regulated in the infected grapevine samples in the present study (Table 1). However,
the detection in this community and the biological function here are currently not clear.
3.1.3. Community Enrichment and the Classical Differential Expression Analysis
The proposed methodology operates at the level of individual community-term
associations. To further study the relation between the conventional enrichment and the
enriched communities, we performed additional analyses discussed next.
First, we compared the community-based results to those obtained by a differential
expression analysis (detailed in Supplemental Table S1). To see whether our community
approach captures a higher-than-expected number of differentially expressed genes, a 2 ×
2 contingency table was prepared to compare differentially and nondifferentially
expressed genes present in the gene list that we obtain from a differential expression
analysis (Supplemental Table S1A) and genes present in our calculated communities
(Supplemental Table S1B,C). Prior to that, genes from communities of both directions
(Table S1B, uninfected vs. infected; Table S1C; infected vs. infected) were merged into a
single list (as differential expression analysis detects differentially expressed genes in both
directions as well; Table S1A) and analysed as a whole. Fisher’s Exact Test for Count Data
was used, where we observed that there are significantly more differentially expressed
genes present in the final set of communities when compared to the remaining set of genes
from a differential expression analysis (corrected p-value of 2.084 × 10−14). This result
indicates that differentially expressed genes are more likely to be present in multi-gene
communities than not, where they potentially act as key nodes that maintain a given
community’s structure. Although communities cover a smaller amount of genes (310;
Table S1B,C) compared to the full set of differentially expressed genes (FDR p-value ≤ 0.05;
6115 differentially expressed genes; Table S1A), it represents a complementary method
from the interpretation standpoint, similarly to the, e.g., gene set enrichment analysis.
In the second step, we aimed to observe the proportion of differentially expressed
and differentially nonexpressed genes in each community separately (Supplemental Table
S1B,C). Results show that the method is not only able to capture communities with a high
number of differentially expressed genes (14 out of 39, where >75% of genes in the
community are differentially expressed), but it also captures many (7 out of 39) that do
not contain a single differentially expressed gene (Figure 7a). In addition, while the
method does result in communities of varying sizes, the proportion of differentially
expressed genes does rise with respect to the detected community size.
Figure 7. (a) Relationship between the community size and number of differentially expressed (DE) genes in that
community (Supplemental Table S1B,C). (b) Histogram of the distribution of the proportions of differentially expressed
genes (number of bins: 20. The ‘Frequency’ corresponds to the density estimated via the seaborn package).
Figure 7.
(
a
) Relationship between the community size and number of differentially expressed (DE) genes in that community
(Supplemental Table S1B,C). (
b
) Histogram of the distribution of the proportions of differentially expressed genes (number
of bins: 20. The ‘Frequency’ corresponds to the density estimated via the seaborn package).
Plants 2021,10, 646 13 of 18
The results of the additional statistical analysis presented in this section indicate that
the community structure indeed entails many significantly expressed genes, and as such,
offers an additional layer of information that potentially facilitates the final interpretation
of the gene roles and facilitates the search of novel hypotheses (possible interactions).
As such, it has the potential to notably reduce the amount of manual work (and hence time)
of a domain expert to identify potentially interesting relations between genes, and as such,
represents a viable complementary method to conventional analysis.
4. Validation of the Results
To experimentally validate the obtained results, we have chosen a glutathione-S-
transferase gene. It was associated with a community that included genes related to
photosystem II of photosynthesis and was strongly transcriptionally activated by ‘Ca. P.
solani’ infection (Table 1). Increases in the transcript of a glutathione-S-transferase gene
and its protein product have already been shown to occur during phytoplasma infection
of the Chinese jujube, which results in jujube witches’ broom disease [
70
]. The plant
glutathione S-transferases comprise a large family of ubiquitous multifunction proteins
that contribute to the detoxification of endogenous or xenobiotic compounds and oxidative
stress metabolism. They are induced under several stress conditions, including microbial
infections [
71
]. However, their role associated with photosynthesis has not been shown
before in phytoplasma-infected plants.
Therefore, the transcript increase in the glutathione S-transferase gene was additionally
independently empirically validated by measurement of the enzymatic activity of the
glutathione S-transferase protein (Supplementary Methods).
A higher enzyme activity was seen for these grapevines infected with ‘Ca. P. solani’
(Figure 8), which is in agreement with the transcriptional activation of the glutathione-
S-transferase gene upon infection (Table 1). The combined results suggest an important
role for glutathione-S-transferase in impaired photosynthesis shown before in grapevines
infected with ‘Ca. P. solani’ [
10
,
12
,
44
–
46
]. This novel actor may open new routes for the
research of phytoplasmas.
Plants 2021, 10, x FOR PEER REVIEW 13 of 18
The results of the additional statistical analysis presented in this section indicate that
the community structure indeed entails many significantly expressed genes, and as such,
offers an additional layer of information that potentially facilitates the final interpretation
of the gene roles and facilitates the search of novel hypotheses (possible interactions). As
such, it has the potential to notably reduce the amount of manual work (and hence time)
of a domain expert to identify potentially interesting relations between genes, and as such,
represents a viable complementary method to conventional analysis.
4. Validation of the Results
To experimentally validate the obtained results, we have chosen a glutathione-S-
transferase gene. It was associated with a community that included genes related to
photosystem II of photosynthesis and was strongly transcriptionally activated by ‘Ca. P.
solani’ infection (Table 1). Increases in the transcript of a glutathione-S-transferase gene
and its protein product have already been shown to occur during phytoplasma infection
of the Chinese jujube, which results in jujube witches’ broom disease [70]. The plant
glutathione S-transferases comprise a large family of ubiquitous multifunction proteins
that contribute to the detoxification of endogenous or xenobiotic compounds and
oxidative stress metabolism. They are induced under several stress conditions, including
microbial infections [71]. However, their role associated with photosynthesis has not been
shown before in phytoplasma-infected plants.
Therefore, the transcript increase in the glutathione S-transferase gene was
additionally independently empirically validated by measurement of the enzymatic
activity of the glutathione S-transferase protein (Supplementary Methods).
A higher enzyme activity was seen for these grapevines infected with ‘Ca. P. solani’
(Figure 8), which is in agreement with the transcriptional activation of the glutathione-S-
transferase gene upon infection (Table 1). The combined results suggest an important role
for glutathione-S-transferase in impaired photosynthesis shown before in grapevines
infected with ‘Ca. P. solani’ [10,12,44–46]. This novel actor may open new routes for the
research of phytoplasmas.
Figure 8. Empirical validation of the glutathione S-transferase activity in uninfected and infected
with ‘Ca. P. solani’ grapevines cv. Zweigelt in the late growing season. FW, fresh weight; the data
refer to means ± standard deviation. * p < 0.05 (Mann-Whitney test).
5. Discussion
Here, we have proposed a methodology for network enrichment based directly on
RNA-Seq data. The methodology was used to model BN phytoplasma-infected
grapevines to provide novel insights into the biochemical mechanisms that potentially
differentiate healthy grapevines from infected grapevines. The proposed methodology
offers both the confirmation of existing empirical data and potentially interesting novel
candidates that appear to be associated with the processes studied. The methodology is
Figure 8.
Empirical validation of the glutathione S-transferase activity in uninfected and infected
with ‘Ca. P. solani’ grapevines cv. Zweigelt in the late growing season. FW, fresh weight; the data
refer to means ±standard deviation. * p< 0.05 (Mann-Whitney test).
5. Discussion
Here, we have proposed a methodology for network enrichment based directly on
RNA-Seq data. The methodology was used to model BN phytoplasma-infected grapevines
to provide novel insights into the biochemical mechanisms that potentially differentiate
healthy grapevines from infected grapevines. The proposed methodology offers both
the confirmation of existing empirical data and potentially interesting novel candidates
that appear to be associated with the processes studied. The methodology is one of the
first direct approaches that join network reconstruction with network enrichment, and it
Plants 2021,10, 646 14 of 18
provides the potential for end-to-end analysis at the network level, directly from high-
throughput RNA-Seq data.
Although the method indeed offers empirically provable results, it still has its limi-
tations. The initial step of network reconstruction is computationally feasible under the
following assumptions. The Euclidean distances between the expression vectors should be
representative for the modelling of real relations between the genes. Furthermore, the key
assumption that offers the filtering of potentially interesting networks from those that
appear to not be interesting is that the networks are scale-free. As the statistical test used
to assess the scale-free properties is rapid, many candidate networks can be inspected.
The resulting networks are at least approximately scale-free (if no real scale-free network is
found); however, the phenomenon studied might not give rise to scale-free networks [
72
],
in which case the proposed methodology will not retrieve the best candidates, although it
will still offer feasible candidates.
The proposed methodology, albeit based on many necessary assumptions, offers in-
sights into the interactions between the genes. As such, it transcends the independence
assumption commonly adopted in classical analysis, and it offers insights into poten-
tially more interesting regulatory mechanisms. The proposed work demonstrates that
reconstruction-based enrichment can offer novel candidate genes that were also shown to
behave accordingly when tested
in vitro
. When comparing the community-based analysis
used in this work with the conventional gene set enrichment analysis that determines
whether an a priori defined set of genes shows statistically significant, concordant differ-
ences between two biological states, we observed that many genes, present in the enriched
communities, were in fact not individually enriched via the conventional analysis, where ex-
pression vectors are compared individually relative to the others. For this observation,
there can be multiple explanations, including the following ones. First, given that the com-
munity enrichment considers only the counts of a particular annotation within/outside a
given community, this step is highly dependent on the structure of the considered network.
As the networks are derived via the threshold-based filtering procedure, for which it is
known that small changes in the threshold can have a great impact on the network itself,
the network generation process can have a substantial effect on the results of enrichment.
This is the trade-off when comparing the proposed method to conventional enrichment,
which is structure-independent, whilst simultaneously being unable to operate in the
space of interactions. The second main reason for the observed discrepancy could be the
impact of the type of test and the corresponding multiple test correction used. Although
the type of p-value correction is a free parameter of the method, we believe that more
involved correction schemes beyond the Bonferroni correction could be explored to further
assess the consistency of the results. We are by no means claiming that the hyperpa-
rameter setting, which yielded promising results in this study, generalises to unknown
reconstruction/enrichment problems; individual expression-based problems are most prob-
ably subject to different underlying regulatory structures, which should be explored on a
per-problem basis.
Note, however, that although the proposed methodology is capable of operating with
a potentially interesting space of e.g., gene–gene interactions, this does not guarantee the
causality of such interactions. As long as the distance-based reconstruction of the regulatory
networks is considered without additional constraints based on, e.g., existing empirical
evidence (measured interactions), the methodology effectively serves as a hypothesis gener-
ation engine, offering insight into structures that emerge from the reconstructed regulatory
networks, albeit some of them being artefacts of the method. For example, the commu-
nity detection method employed can have different sensitivities (resolution limits) when
considering smaller communities. We attempted to overcome this issue by selecting a
method which is known to perform well in such settings; however, the considered method
(InfoMap) could be unsuitable for a related domain where a different type of community
detection could be more useful.
Plants 2021,10, 646 15 of 18
Finally, we consider the developed methodology as complementary to many other
established approaches based on frequentist statistics, as the network-based approach
can unveil novel hypotheses that are not reachable via, e.g., individual-level enrichment
analysis. Further work also includes extending the network generation process with
measures of network segmentation such as the clustering coefficient, which could further
improve the quality of the reconstructed networks.
Supplementary Materials:
Supplementary materials can be found at https://www.mdpi.com/
article/10.3390/plants10040646/s1. Supplementary Methods. Supplementary Table S1.
Author Contributions:
Conceptualization, B.Š., M.P.N., K.G. and M.D.; methodology, B.Š., M.P.N.,
K.G., J.K. and N.L.; data curation, Ž.R.; validation, T.R. and B.A.; visualization, B.Š. and A.K.;
resources, G.B.; writing—original draft preparation, B.Š.; writing—review and editing, M.D., M.P.N.,
Ž.R., G.B., K.G. and N.L.; project administration, M.D.; funding acquisition, M.D. and G.B. All authors
have read and agreed to the published version of the manuscript.
Funding:
This research was funded by the Slovenian Research Agency (ARRS), grant numbers
J1-7151, N2-0078, and P2-0103, and young researcher grant of B.Š.; and the Austrian Science Fund
(FWF), grant number I 2763-B29.
Data Availability Statement:
The source data for this study have been deposited in the European
Nucleotide Archive (ENA) in the frame of another study [Dermastia et al., 2021 (submitted)] in
fastq format under project accession PRJEB42777 and samples accessions: ERS5673304, ERS5673305,
ERS5673306, ERS5673307, ERS5673308, ERS5673309, ERS5673310, and ERS5673311. Code to repli-
cate the methodology is freely available at: https://gitlab.com/skblaz/community-enrichment-
phytoplasma (accessed on 20 March 2021).
Acknowledgments:
The authors thank Aleš Kladnik for help with the data presentation and Christo-
pher Berrie for his linguistic touch.
Conflicts of Interest: The authors declare that they have no conflict of interest.
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