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B I O D I V E R S I T A S
ISSN: 1412-033X
Volume 26, Number 1, January 2025 E-ISSN: 2085-4722
Pages: 434-443 DOI: 10.13057/biodiv/d260143
Metabolite profiles and biomarkers of three Selaginella
(Selaginellaceae) medicinal plant species in Java Island, Indonesia
AMANDA KHOIRUNISA, TATIK CHIKMAWATI, GIRI NUGROHO, MIFTAHUDIN
Department of Biology, Faculty of Mathematics and Natural Sciences, Institut Pertanian Bogor. Jl. Agatis, Dramaga Campus, Bogor 16680, West Java,
Indonesia. Tel.: +62-251-8622833, email: tatikch@apps.ipb.ac.id
Manuscript received: 10 November 2024. Revision accepted: 25 January 2025.
Abstract. Khoirunisa A, Chikmawati T, Nugroho G, Miftahudin. 2025. Metabolite profiles and biomarkers of three Selaginella
(Selaginellaceae) medicinal plant species in Java Island, Indonesia. Biodiversitas 26: 434-443. Metabolite content in plants is an
important taxonomic marker that facilitates the realistic delimitation of species. Substantial improvement is needed for the metabolomic
data of several fern species, including Selaginella, which is widely used as a medicinal plant on Java Island. Therefore, this research
aimed to profile metabolite compounds in Selaginella ornata, S. plana, and S. willdenowii, and identify biomarkers for species
differentiation. Metabolite content and data were determined with Liquid Chromatography-Mass Spectrometry (LC-MS) and MZmine
3.1.0 beta software, respectively. Meanwhile, metabolite profiling, heatmap clusters, and cluster analysis were carried out using
MetaboAnalyst 5.0. A total of 113 metabolites were detected in three Selaginella species observed. Based on metabolite characteristics,
cluster analysis categorized all individuals into three groups, showing that individuals from the same species were more similar than
others, with S. ornata metabolites appearing more similar to S. willdenowii than to S. plana. Three species had similarities in the
compounds 1,3,5-benzenetricarbonitrile, 2-hydroxyisocaproic acid, 3-furoic acid, 3-methyl-2-oxovaleric acid, amentoflavone, avobenzone,
ibuprofen, kojic acid, and skyrin. Metabolites only possessed by each species of S. plana, S. ornata, and S. willdenowii included 4-
vinylphenol, velutin, and axahine B, respectively. This research reported for the first time several low-weight secondary metabolites
with potential application as biomarkers to differentiate three species.
Keywords: Biomarkers, cluster analysis, heatmap, LC-MS/MS, low-weight secondary metabolites
INTRODUCTION
Biochemical characters have been used as taxonomic
evidence for several hundred years. The chemical
compounds are used extensively in plant systematics to
analyze infraspecific variation, understand the evolutionary
relationships of various taxa categories, and solve
taxonomic problems. Leading chemical compounds that are
systematically useful include secondary metabolites, such
as alkaloid, phenolic, betalain, anthocyanin, terpenoid, and
flavonoid (Singh 2016). Moreover, several metabolomics
research have analyzed plant diversity with low molecular
weight compounds, including secondary metabolites obtained
from living organisms (Shen et al. 2023; Quiros-Guerrero
et al. 2024). In recent years, a key technology for
metabolomics research is Liquid Chromatography-Mass
Spectrometry (LC-MS), which combines physical separation
capabilities with mass analysis capabilities. LC-MS often
produces larger data volumes and is superior in sensitivity,
speed, and efficiency, compared to Gas Chromatography-
Mass Spectrometry (GC-MS) (Wu et al. 2021). In this
context, the profile data obtained is used to group plant
species based on similarities and differences in the content of
metabolite compounds, while each species has a particular
structure, product, and metabolite biosynthesis pathway
(Arbona et al. 2015; Singh 2016). The resulting grouping will
be consistent with previously known plant evolutionary
relationships (Liu et al. 2017; Freitas et al. 2021). Previous
research reported that metabolite-based classification
successfully differentiated Citrus spp. and determined the
species-specific metabolites (Peng et al. 2021). Using
untargeted metabolomics data on mosses has promoted a
more realistic delimitation of species (Peters et al. 2023).
In addition to taxonomic purposes, low-weight secondary
metabolite data can be used to estimate the potential future
application of plants as a source of herbal-based cosmetic
ingredients, herbal nutritional supplements, or medicinal
ingredients (Salem et al. 2020). Plants have been used in
treating health problems for thousands of years by many
ethnic groups, including Indonesia, and are considered a
source of various modern medicinal ingredients.
Approximately 30% of drugs sold worldwide are reported
to contain compounds derived from plant materials
(Calixto 2019; Salmerón-Manzano et al. 2020). Many
people use herbal drugs because of the belief that medicinal
plants have advantages over drugs derived from chemicals,
including lower side effects, relatively affordable prices,
easy accessibility, safety, and effectiveness (Nimesh et al.
2020). Currently, herbal drugs are being used by various
populations worldwide, both in developing and industrialized
countries (Tangkiatkumjai et al. 2020). Approximately
50,000-80,000 species have been used as medicinal plants
in the world (Pimm et al. 2014), and around 6000 in
Indonesia are known to contain bioactive compounds, with
16.7% of the species being used as traditional medicinal
plants (Elfahmi et al. 2014). However, the potential of
KHOIRUNISA et al. – Metabolite profiles of Selaginella spp. from Java, Indonesia
435
various plant ingredients has yet to be well documented for
development into drugs.
Selaginella is a genus of pteridophytes used to produce
drugs, which belongs to the Selaginellaceae family with
high species diversity (Zhou et al. 2016). The number of
Selaginella in the world is around 800 species spread
across all continents except Antarctica, with the highest
diversity in tropical and subtropical regions (Zhou and
Zhang 2015). Between 1994-2014, 39 species were found
distributed across nine islands in Indonesia, with Java Island
containing the most significant number (22) (Wijayanto
2014). Furthermore, Selaginella is easily distinguished
from other pteridophytes by several characteristics, such as
small leaves (microphylls) arranged opposite each other in
four rows on the stem, with two of these rows consisting of
larger leaves arranged laterally and the remaining two
comprising smaller median leaves on branches facing
forward. This species is heterosporous with mega- and
microsporangia surrounded by sporophylls and arranged in
strobili at the tips of branches (Valdespino et al. 2015;
Weststrand and Korall 2016).
Selaginella ornata (Hook & Grev.) Spring, S. plana
(Desv. ex Poir.) Hieron., and S. willdenowii (Desv.) Baker
abundant on Java Island have been widely used as
medicinal plants by residents in several areas in West Java
Province, mainly for post-natal care and broken bone
treatment (Chikmawati et al. 2009). Miftahudin et al.
(2019) have also reported that these three species have
antioxidant activity. This ability is probably due to the
three species containing alkaloids, flavonoids, saponins,
tannins, and steroids (Chikmawati et al. 2012). Limited
information is available regarding the primary chemical
compounds in the species, particularly S. ornata and S.
plana, leading to a need for more investigation that can
contribute significantly to the fields of taxonomy, pharmacy,
and health. Therefore, this research aimed to analyze non-
targeted metabolite compounds in extracts of S. ornata, S.
plana, and S. willdenowii with medicinal potential and
determine the differentiating compounds between species
using LC-MS/MS-based metabolomics method.
MATERIALS AND METHODS
Sample preparation and extraction
Plant samples for S. plana and S. willdenowii were
collected from IPB Univesity green area (-6.55500,
106.72159), Dramaga Campus, Bogor, while S. ornata was
obtained from Curug Nangka area (-6.66886, 106.72641),
Bogor, Indonesia (Supplementary Files-1). The samples
were collected in June 2022 from forested areas or cliffs
near irrigation canals that have high humidity. Rhizome
drying and extraction were performed at the Plant Resources
and Ecology Laboratory, Department of Biology, IPB
University, and the Analytical Chemistry Research
Laboratory, Department of Chemistry, IPB University.
Untargeted metabolites were analyzed at the Advance
Research Laboratory of IPB University.
The collected Selaginella samples were washed using
tap water, drained, and dried in the oven at 40-50°C for 72 h,
then the dried samples (simplisia) were ground using a
blender. Sample extraction followed the procedure by
Gayathri et al. (2005), which was modified on the duration
of maceration, and conducted in three replications for each
species. An amount of 5 g of simplisia from each
Selaginella species was extracted using 100 mL of 70%
ethanol or with the simplisia to solvent ratio of 1:20 (w/v)
and stirred for 5 hours. After incubation at room
temperature for 24 hours, the filtrate was separated using
Whatman No.1 filter paper. A total of 50 mL filtrate was
concentrated on a rotary evaporator at 50°C to form a
paste.
Metabolite analysis
Separation and identification of metabolite components
of Selaginella extract were performed under the following
conditions: An amount of 2 mg paste sample was analyzed.
The extracts were filtered using a 0.2 μm PFTE filter, and
2.5 μL of sample was injected. The analysis was performed
using the UHPLC Vanquish Tandem Q Exactive Plus
Orbitrap HRMS (ThermoScientific, USA). The column
used was Accucore C18 (1.5μm 2.1×100 mm) with
temperature condition at 30°C. The mobile phase consisted
of water + 0.1% Formic Acid (A) and Acetonitrile + 0.1%
Formic Acid (B), with a flow rate of 0.2 mL/min over 30-
minute gradient. The gradient program was as follows, i.e.
0-1 min (95% A + 5% B), 1-25 min (5-95% B), 25-28 min
(95% B), and 28-30 min (95% A + 5% B). The mass
spectrometry was performed using electrospray ionization
in positive and negative mode, with a scan range of 100-
1500 m/z. The system was set with the following conditions,
i.e. capillary temperature at 320°C, sheath gas flow at 15
μL/min, Aux gas flow at 3 L/min, spray voltage at 3.8 kV
and S-lens RF level at 50.
LC-MS/MS data analysis
The data obtained in a complex raw format from LC-
MS/MS instruments were processed using MZmine 3.1.0
beta software to identify metabolite compounds (Schmid et
al. 2023). The identification step consisted of mass detection,
ADAP chromatogram builder, chromatogram deconvolution,
isotopic grouper, alignment, and identification.
Mass detection
The raw data method and features detection menu were
selected after entering the raw data into MZmine software.
The mass detection was set with the parameters, including
scan number (1), Retention Time (RT) (0.00-30.00 min),
MS level (1), scan types (all scan types), mass detector
(centroid), and noise level (1.0E5). In the next stage, peak
detection was used to identify and measure the signal
intensity corresponding to the sample molecules.
ADAP chromatogram builder
ADAP chromatogram was built with the following
parameters, RT (0.00-30.00 min), MS level (1), min group
size in # of scans (5), group intensity threshold (3.0E0),
min highest intensity (1.0E5), scan to scan accuracy (0.002
m/z or 10 ppm), and suffix (chromatogram). This stage
B I O D I V E R S I T A S
26 (1): 434-443, January 2025
436
aimed to produce a chromatogram from the extracted ions
and detected chromatogram peaks.
Chromatogram deconvolution
Chromatogram deconvolution was carried out using
parameters, including suffix (resolved), original feature list
(remove), dimension (RT), chromatographic threshold (50%),
min search range RT/mobility (0.05), min relative height
(0.1%), min absolute height (1.0E5), min ratio of peak
top/edge (2), peak duration range (0-5), and min # of data
points (5). This stage targeted to combine chromatograms
near each other to form a definite m/z value.
Isotopic grouping
The grouping stage helped to arrange monoisotopic
peaks with corresponding isotope and chromatogram peaks
possessing similar isotope patterns. This stape applied
parameters, including name suffix (deisotoped), m/z tolerance
(0.01 m/z or 5 ppm), RT tolerance (0.03), monotonic shape
(selected), maximum charge (2), and representative isotope
(most intense).
Alignment
The alignment stage combined and compared metabolites
between samples according to m/z and RT. The data were
aligned using several parameters, including features list
name (aligned features list), m/z tolerance (0.001 m/z or 5
ppm), RT tolerance (0.01), RT tolerance after correction
(1), min number of points (25%), and threshold value (2).
Identification
The identification stage compared the detected m/z
values with online data sources or data present in literature
reviews (Höcker et al. 2021). The following parameters
were chosen to annotate the data, namely MS level (2),
precursor m/z tolerance (0.001 m/z or 5 ppm), CCS tolerance
(5%), min ion intensity (0), crop spectra to m/z overlap (√),
spectral m/z tolerance (0.0015 m/z or 10 ppm), and min
matched signals (4). Compounds in Selaginella extract
were identified by comparing the precise mass values of
the detected peaks with the accurate mass values of
metabolites in PubChem library (https://pubchem.ncbi.
nlm.nih.gov/) and the spectrum library in MassBank of
North America (MONA) database (https://mona.fiehnlab.
ucdavis.edu), GNPS (https://gnps.ucsd.edu), dan HMDB
(https://hmdb.ca/). The identification stage compared the
detected m/z values with online data sources or data in
literature reviews (Höcker et al. 2021). The matching
parameters used was min cosine similarities = 0.8 (80%)
with confidence level of 2 (putative metabolites).
Heatmap and cluster analyses
MetaboAnalyst 5.0 software was used for statistical
analysis to construct a clustered heatmap and a dendrogram.
This applied the complete grouping method with the
Euclidean dissimilarity index (Pang et al. 2021).
RESULTS AND DISCUSSION
Profile of Selaginella metabolite compounds
The profile of metabolite compounds was described by
metabolomics analysis, which aimed to collect various
compounds in cells and tissues (Van Dam and Bouwmeester
2016; Wuolikainen et al. 2016). Estimating metabolites in
Selaginella extracts was performed by matching m/z values
with the compound patterns in the MONA database and
online data sources in PubChem. The results obtained from
several stages of analysis were in the form of a table
containing compound identity, RT, mass of detected peaks,
chromatogram image with normalized peaks, and other
information.
Chromatograms produced by the ethanol extracts of
three Selaginella species required an elution time of
approximately 30 minutes. They generated nearly the same
peaks for each replicate but different peaks between species
(Supplementary Files-2). The Chromatogram profile of S.
plana extract comprised more peaks than S. ornata and S.
willdenowii. These results showed that the number and type
of compounds detected in individuals of one species are
almost the same, but different in individuals from various
species. Selaginella plana contained more metabolites than
the other two species. The metabolite components identified
from the extracts of three Selaginella species amounted to
113 essential compounds. The number of compounds in
each species replication was calculated based on the type of
compound detected with different m/z values and times.
The number of metabolites found in each replication varied
in the three species investigated. The variation in the
number of metabolites per replication in S. plana was
higher than in the other two species (Figure 1). The
distribution of metabolite compounds identified in three
Selaginella species showed that S. plana had the highest
metabolite content (Figure 2). This result is based on the
chromatogram description of S. plana, which has the
highest number of peaks. According to the results of
metabolomic analysis, the metabolite compound groups of
three Selaginella species consisted of amino acid, fatty
acid, carboxylic acid, phenols, flavonoid, alkaloid, terpenoid,
and other compound groups (Figure 3). The most
significant number of compounds identified was the group
of amino acids from the total identified metabolite
compounds.
Figure 1. Number of metabolites in three Selaginella species
KHOIRUNISA et al. – Metabolite profiles of Selaginella spp. from Java, Indonesia
437
Figure 2. The proportion of total metabolites detected in S. plana,
S. ornata, and S. willdenowii plants
Figure 3. The proportion of each group of chemical compounds
detected in S. plana, S.ornata, and S. willdenowii
Amino acid
Amino acid is an essential organic compound acting as
a building block for protein, leading to an extensive
investigation for use in medicine (Parthasarathy et al.
2021). The group of amino acid compounds in Selaginella
varies between species, 14% in S. plana, 6% in S. ornata,
and 16% in S. willdenowii (Figure 3). Amino acid groups
detected in S. plana included gabapentin, with m/z value of
187.10 at 1.52 minutes (Table 1). The gabapentin compound
can be used as a seizure reliever for people living with
epilepsy and tends to relieve nerve pain, leading to the
inclusion in the class of anti-seizure drugs (Rocha et al.
2019). S. plana and S. willdenowii have the same 4 amino
acid compounds, namely deferrioxamine e, diprotin b, l-2-
aminoadipic acid, l-pyroglutamic acid, and n-acetyl-l-
leucine. The amino acid compound only detected in S.
ornata was aspartic acid, while axahine B was found in S.
willdenowii. Aspartic acid is the key compound in the
amino acid metabolism, serving as a precursor for basic
amino acids formation and enhancing heat stress tolerance
(Lei et al. 2022). Aspartic acid compounds exist in L- and
D-isoforms (L-Asp and D-Asp ), with L-Asp playing a role
in the pathogenesis of psychiatric and neurological
disorders and changes in BCAA levels in diabetes and
hyperammonemia. D-Asp has a role in brain development
and hypothalamic regulation (Holeček 2023).
Kapahine B, isolated from the marine sponge
Cribrochalina olemda (Nakao et al. 1995), showed anticancer
activity against P-388 murine leukemia cells with an IC50
value of 5.0 μg/mL (Gul and Hamann 2005). Based on the
pharmacological properties of the phytochemical compounds,
S. willdenowii leaf extract is reportedly used as an
anticancer, antioxidant, anti-inflammatory, antitumor, and
antimicrobial drug (Susilo and Wardhani 2023). The
extract of Selaginella also contains L-tyrosine that plays
crucial role as antioxidant, attractants, and plant defense to
environmental stress (Schenck and Maeda 2018).
Fatty acid
Fatty acid compounds detected in three Selaginella
species were nearly the same amount, 7%, 6%, and 7% in
S. plana, S. ornata, and S. willdenowii, respectively (Figure
3). A fatty acid found in three Selaginella species was
gamma-linolenic acid with m/z value of 277.22 at 24.2
minutes (Table 2). This compound is an unsaturated fatty
acid that can act as an anti-inflammatory drug (Sergeant et
al. 2016). Palmitic acid is a saturated fatty acid compound
detected in S. plana with m/z value of 255.23 at 19.26
minutes. Palmitic acid can interact with DNA topoisomerase
to induce apoptosis in MOLT-4 leukemia cancer cells
(Kwan et al. 2014). Palmitic acid could inhibit plant
pathogens in soil and have a positive effect on rhizosphere
conditions (Ma et al. 2021).
Table 1. Amino acid metabolite compounds detected in three Selaginella species
Compounds
Formula
m/z
RT (minute)
Species
Aspartic acid
C4H7NO4
134,05
1.3
S. ornata
Deferrioxamine E
C27H48N6O9
639,31
9.62
S. plana, S. willdenowii
Diprotin B
C16H29N3O4
327,22
11.45
S. plana, S. willdenowii
Gabapentin
C9H17NO2
171,21
1.52
S. plana
Kapakahine B
C49H52N8O6
849,41
15
S. willdenowii
L-2-Aminoadipic acid
C6H11NO4
160,06
1.43
S. plana, S. willdenowii
L-glutamine
C5H10N2O3
130,05
1.59
S. plana
L-Pyroglutamic acid
C5H7NO3
128,04
2.27
S. plana, S. willdenowii
L-Tyrosine
C9H11NO3
180,07
1.54
S. plana
N-Acetyl-L-Leucine
C8H15NO3
172,10
6.75
S. plana, S. willdenowii
Notes: m/z: mass-to-charge ratio value; RT: Retention Time
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Table 2. Fatty acid metabolite compounds detected in three Selaginella species
Compounds
Formula
m/z
RT (minute)
Species
3,5-Dihydroxydecanoic acid
C10H20O4
227.13
13.3
S. ornata
Dihydrojasmonic acid
C12H20O3
213.15
13.37
S. willdenowii
Gamma-Linolenic acid
C18H30O2
277.22
24.2
S. ornata, S. plana, S. willdenowii
Isopalmitic acid
C16H32O2
295.23
20.03
S. plana, S. willdenowii
Linoleic acid
C18H32O2
279.23
25.74
S. willdenowii
Lipoic acid
C8H16O2S2
207.05
1.49
S. plana
Palmitic acid
C16H32O2
255.23
19.26
S. plana
Notes: m/z: mass-to-charge ratio value; RT: Retention Time
In addition, the lipoic acid compound (m/z value 207.05
and RT 1.49 minutes) was detected in S. plana, which
could help reduce skin aging due to its antioxidant
properties (Kim et al. 2021). Compounds only detected in
S. willdenowii included dihydrojasmonic acid and linoleic
acid (or omega-6), often found in skincare ingredients. This
compound is among the essential fatty acids needed to
work optimally, but the human body cannot produce it.
Linoleic acid is required as an ingredient for ceramide,
which protects and maintains skin moisture. A body that
lacks linoleic acid can experience dry skin, hair loss easily,
slower wound healing, and decreased cell regeneration.
Conjugated linolenic acid, an omega-5 fatty acid, comprises
antioxidant and anti-inflammatory properties and has been
shown to stimulate keratinocyte proliferation and epidermal
regeneration. Conjugated linolenic acid can shorten
recovery time after fractionated ablative laser resurfacing
of the face (Wu and Goldman 2017). Dihydrojasmonic acid
compound, also known as dihydrojasmonate, possesses
antioxidant properties similar to the isopalmitic acid
detected in S. plana and S. willdenowii.
Phenol
Phenolic compounds are secondary metabolites of
phenol groups containing hydroxyl on one or more
aromatic benzene rings (de M. Castro and Demarco 2008).
According to Figure 3, most of these compounds (13%)
were detected in S. ornata extracts, such as catechol, 2-
hydroxybenzaldehyde, and aspalathin (Table 3). Catechol
was found in S. ornata with an m/z value of 109.03 at RT
of 1.22 minutes and reported by Lim et al. (2016) to be
capable of inhibiting lung cancer growth. Catechol has also
been reported to induce root elongation, but inhibited the
root hair elongation (Wang et al. 2016). 2-hydroxycinnamic
acid was detected in three species at 8.05 minutes with an
m/z value of 165.05. This compound can be used as an
ingredient in cosmetic products due to its antioxidant and
anti-aging potential (Taofiq et al. 2017). 4-vinylphenol and
3,4-dimethoxycinnamic acid were only detected in S. plana,
with 4-vinylphenol showing potential as an anti-breast
cancer agent due to inhibiting metastasis and stemness of
CSC-enriched breast cancer cells (Leung et al. 2018).
Based on the compounds, three Selaginella species have the
potential to be antioxidant, anti-aging agents, and anticancer
agents. Phenylacetic acid is an auxin compound, regulates
plant growth through cell expansion and key compound in
plant interaction with soil-microbial (Cook 2019).
Flavonoid
Approximately 11%, 12%, and 4% of flavonoid secondary
metabolites were respectively identified in S. plana, S.
ornata, and S. willdenowii (Figure 3). These include
amentoflavone detected in three species at 13.85 minutes
with an m/z value of 539.10 and is a biflavonoid derivative
with many health benefits (Table 4). The result is based on
research by Chikmawati et al. (2012), which identified an
amentoflavone compound in S. willdenowii from Java
Island. Leaf extract of S. willdenowii contains three known
biflavones, namely 4′,7″-di-O-methyl-amentoflavone,
isocryptomerin, and 7″-O-methylrobustaflavone, reported
to effectively inhibit tumor cell growth (Silva et al. 1995).
Additionally, amentoflavone can reduce inflammatory
activity in brain microglial cells, serve as an anti-
inflammatory drug, and induce the apoptotic activity of
cervical cancer cells (Lee et al. 2011). In Chinese medicine,
S. willdenowii is used for cardiovascular diseases and the
treatment of nose, liver, throat, and lung cancer (Shumon and
Ashrafuzzaman 2021).
Other flavonoid compounds include naringin, with an
m/z value of 579.18 identified at 9.32 minutes, and can be
used as anti-diabetic type 2 (Ahmed et al. 2012). Rhoifoilin
detected at 10.38 minutes had an m/z value of 577.15 and
antioxidant, anti-inflammatory, antimicrobial, and anticancer
benefits (Refaat et al. 2015). In the plant, rhoifolin plays
scavenging reactive oxygene species in plant cell (Peng et
al. 2020). Aloeresin A was detected at 9.32 minutes with an
m/z value of 541.18 and anti-inflammatory properties
(Mwale and Masika 2010). Compounds found in S. ornata
only were gardenin A and velutin, while those observed in
S. plana were isoschaftoside, isovitexin 2''-O-arabinoside,
kaempferol-7-neohesperidoside, and vitexin. Isoschaftoside
is a C-glycosyl flavonoid identified at 7.16 minutes with an
m/z value of 565.16 and has neuroprotective effects by
reducing oxidative stress (Guan et al. 2022). Isovitexin 2''-
O-arabinoside also possesses antioxidant activity with an
m/z value of 564.49 and an RT of 7 minutes (Shao et al.
2019; Li et al. 2021). Recent research showed that bioactive
compounds from S. plana could inhibit the growth of a
fungal cell causing candidiasis (Candida albicans), through
the disruption of ergosterol biosynthesis (Warella et al.
2023).
Alkaloid
Alkaloid is a secondary metabolite produced from the
biosynthesis of shikimic acid in tryptophan and is included
KHOIRUNISA et al. – Metabolite profiles of Selaginella spp. from Java, Indonesia
439
in the class of compounds formed from molecules
containing amine groups. This metabolite protects plants
from disease, and the toxic agent components prevent
herbivore attacks (Matsuura and Fett-Neto 2015). Ergonovine
maleate is a group of alkaloid compounds detected in S.
willdenowii with an m/z value of 343.21 at 11.85 minutes,
which functions as a drug to help reduce bleeding after
child delivery (McEvoy 2014) (Table 5). Additionally,
xanthosine was identified with an m/z value of 285.08, RT
of 1.1 minutes, and the ability to increase milk production
and the number of mammary gland stem cells in cows
(Choudhary et al. 2018).
Terpenoid
Terpenoid is a secondary metabolite produced through
the mevalonic acid biosynthesis pathway, with functions
including growth regulation and stimulation, as well as the
protection of plants from microbes and insects (Tholl
2015). Tryptophenolide was detected from the terpenoid
group at 12.91 minutes in three Selaginella species with an
m/z value of 311.17 (Table 6). He et al. (2016) reported
that this compound could act as an anti-androgen,
inhibiting prostate cancer cell growth. Additionally, geranic
acid detected at 12 minutes with an m/z value of 169.12 often
produces a distinctive odor, facilitating the application as a
perfume ingredient (Jaworska et al. 2015).
Clustering Selaginella species based on metabolite
compound content
Chemotaxonomy includes using differences in the content
of metabolite compounds as taxonomic evidence to distinguish
species. This method is used to differentiate between plant
species that have high morphological similarities (Singh
2016). An example of chemotaxonomy is the differentiation
of several Rhodiola species with various classes of phenolic
and flavonoid compounds due to their similar morphological
characteristics (Liu et al. 2013). The analysis results of
metabolite compounds showed a clear group division between
three Selaginella species observed and presented on the
heatmap combined with hierarchical clusters (Figure 4).
Table 3. Phenol group metabolite compounds detected in three Selaginella species
Compounds
Formula
m/z
RT
(minute)
Species
2-Hydroxybenzaldehyde
C7H6O2
121.03
21.73
S. ornata
2-Hydroxycinnamic acid
C9H8O3
165.05
8.05
S. ornata, S. plana, S. willdenowii
2-Hydroxyphenylacetic acid
C8H8O3
151.04
1.41
S. ornata, S. willdenowii
3,4-Dimethoxycinnamic acid
C11H12O4
209.08
7.17
S. plana
4-Vinylphenol
C8H8O
119.05
7.39
S. plana
Aspalathin
C21H24O11
451.12
1.11
S. ornata
Catechol
C6H6O2
109.03
1.22
S. ornata
Phenylacetic acid
C8H8O2
135.04
6.42
S. plana, S. ornata
Notes: m/z: mass-to-charge ratio value; RT: Retention Time
Table 4. Flavonoid metabolite compounds detected in three Selaginella species
Compounds
Formula
m/z
RT
(minute)
Species
Amentoflavone
C30H18O10
539.10
13.85
S. ornata, S. plana, S. willdenowii
Aloeresin A
C28H28O11
541.18
9.32
S. ornata, S. plana
Gardenin A
C21H22O9
313.07
8.26
S. ornata
Isoschaftoside
C26H28O14
565.16
7.16
S. plana
Isovitexin 2''-O-arabinoside
C26H28O14
564.49
7
S. plana
Kaempferol-7-neohesperidoside
C27H30O15
593.15
6.01
S. plana
Naringin
C27H32O14
579.18
9.32
S. ornata, S. plana
Rhoifolin
C27H30O14
577.15
10.38
S. ornata, S. plana
Velutin
C17H14O6
313.07
8.25
S. ornata
Vitexin
C21H20O10
433.12
9.53
S. plana
Notes: m/z: mass-to-charge ratio value; RT: Retention Time
Table 5. Alkaloid metabolite compounds detected in three Selaginella species
Compounds
Formula
m/z
RT
(minute)
Species
Ergonovine maleate
C19H23N3O2
343.21
11.85
S. willdenowii
Thalsimine
C38H40N2O7
637.29
9.42
S. plana, S. willdenowii
Thymine
C5H6N2O2
127.05
2.73
S. plana
Xanthosine
C10H12N4O6
285.08
1.11
S. willdenowii
Notes: m/z: mass to charge ratio value; RT: Retention Time
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26 (1): 434-443, January 2025
440
Figure 4. Heatmap clusters based on metabolite compounds contained in Selaginella plana, S. ornata, and S. willdenowii
KHOIRUNISA et al. – Metabolite profiles of Selaginella spp. from Java, Indonesia
441
Table 6. Terpenoid metabolite compounds detected in three Selaginella species
Compounds
Formula
m/z
RT
(minute)
Species
Benzoyltaxol
C54H55NO15
957.36
15.69
S. willdenowii
Geranic acid
C10H16O2
169.12
12
S. willdenowii
Murolic acid
C21H36O5
401.29
25.5
S. plana
Triptophenolide
C20H24O3
311.17
12.91
S. ornata, S. plana, S. willdenowii
Notes: m/z: mass-to-charge ratio value; RT: Retention Time
Table 7. Candidate characteristic compounds for Selaginella ornata, S. plana, and S. willdenowii
Species
Biomarker candidates
S. plana
Aloeresin A, Naringin, Rhoifolin, Isoschaftoside, Isovitexin 2''-O-arabinoside, 4-vinylphenol, and Isofraxidin.
S. ornata
Velutin, Aspalathin, Gardenin A, and N-(1,1-dimethyl-3-oxobutyl)acrylamide.
S. willdenowii
Kapakahine B, Dihydrojasmonic Acid, Linoleic Acid, Alantoic Acid, Menaquinone, and Nandrolone.
Figure 5. Dendrogram of three Selaginella species based on
metabolite content analyzed using the complete grouping method
with the Euclidean dissimilarity index
Heatmap data visualization is widely used in
metabolomics research because it can describe several
metabolite data and the differences in sample groups. The
color rows on the heatmap represent the various metabolite
compounds, while the columns are the species analyzed.
Based on the heatmap formed from 80 chemical compounds,
metabolites with a deep red color possess higher concentrations.
Amentoflavone and skyrin had high concentrations in all
individuals of three species and were highest in S. ornata
replicate 2. The concentrations of Aloeresin A, Umbelliferone,
Naringin, Rhoifolin, Isoschaftoside, and Isovitexin 2”-O-A
were the highest in S. plana.
Metabolite profiles in plants can be used as candidate
compound markers (biomarkers) of a species. Candidate
characteristic compounds are determined from compounds
only detected in one species and not found in others
belonging to the same genus or group (Kim et al. 2017).
These include 4-vinylphenol, velutin, and axahine B
identified in S. plana, S. ornata, and S. willdenowii,
respectively. All candidate characteristic compounds with
the highest concentrations selected for each species group
based on the heatmap are shown in Table 7.
Grouping between species was conducted through
cluster analysis in the form of a dendrogram based on the
level of Euclidean dissimilarity using the MetaboAnalyst
5.0 program. The results based on metabolite characteristics
showed that Selaginella species in this research formed
three groups (Figure 5).
Each group consists of individuals from the same species
showing higher similarities in metabolite compounds than
those from different species. However, all species have
similar chemical compounds that facilitate the categorization
into the same genus. The similarities in the compound
content of the three species include 1,3,5-
benzenetricarbonitrile, 2-Hydroxyisocaproic acid, 3-furoic
acid, 3-methyl-2-oxovaleric acid, amentoflavone, avobenzone,
ibuprofen, kojic acid, and skyrin. Groups 2 and 3 have a
higher level of similarity due to containing 20 identical
metabolite compounds, namely 2-hydroxyphenylacetic acid,
laccaic acid a, histidinol, triethylamine hydro-chloride,
methoxychlor, menaquinone-4, taurodehydro-cholic acid,
oxytetracycline, maleic hydrazide diethanolamine, allantoic
acid, azo-mustard, biflavonoid-flavone base + 3o,
cephalochromin, cis-aconitate, mesaconic acid, bilobalide,
cladribine, guanosine, fibracillin, and pretilachlor. Group 1
is on a different branch because it does not comprise some
of the compounds present in the other two groups. The
difference between Group 1 and the other groups depends
on 4-chlorobenzophenone, 4-hydroxy-6-methyl-2-pyrone,
4-vinylphenol, and 5-hydroxyindole-3-acetic acid.
Three Selaginella species investigated vary in types and
concentrations of low-density chemical compounds. S.
plana has the most significant types and several
compounds with higher concentrations than the other two
species. Individuals from the same species possess higher
metabolite similarity than those from different species.
These results show that different Selaginella originating
from the same location comprise various metabolite contents;
hence, the types of metabolites possessed can be used as
biochemical markers to differentiate between species. All
Selaginella species contained metabolite compounds with
antioxidant, anti-tumor, anti-cancer, anti-seizure, anti-
inflammatory, anti-microbial, anti-aging, and anti-androgen
activities. Therefore, three species have prospects to be
B I O D I V E R S I T A S
26 (1): 434-443, January 2025
442
developed as ingredients for drugs, cosmetics, and perfumes
in manufacturing.
In conclusion, this research showed that metabolite
profiling of three Selaginella species through an untargeted
metabolomics method using LC-MS/MS identified 113
essential metabolite compounds. Furthermore, amino acids,
flavonoids, phenol, fatty acids, carboxylic acid, alkaloids,
and terpenoids were detected in high quantities.
Metabolites found in three species observed were 1,3,5-
benzenetricarbonitrile, 2-hydroxyisocaproic acid, 3-furoic
acid, 3-methyl-2-oxovaleric acid, amentoflavone, avobenzone,
ibuprofen, kojic acid, and skyrin. Metabolites only
possessed by each of S. plana, S. ornata, and S. willdenowii
were 4-vinylphenol, velutin, and axahine B, respectively.
Heatmap and dendrogram analyses showed that three species
could be grouped based on metabolite characteristics, with
S. ornata and S. willdenowii comprising high metabolite
content similarity. According to the results, individuals
from the same species had higher similarities in chemical
compound characteristics than those from different species,
signifying the potential to use metabolomics data as
taxonomic markers in Selaginella.
ACKNOWLEDGEMENTS
The research was funded independently by the
corresponding author. The analysis of metabolites was
carried out at the Advance Research Laboratory, IPB
University, Bogor, Indonesia.
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