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Article
Transcriptomics of the Rooibos (Aspalathus linearis)
Species Complex
Emily Amor Stander 1, Wesley Williams 1,2, Yamkela Mgwatyu 1, Peter van Heusden 1,
Fanie Rautenbach 3, Jeanine Marnewick 3, Marilize Le Roes-Hill 3and Uljana Hesse 1,2,4,*
1South African Medical Research Council Bioinformatics Unit, South African National
Bioinformatics Institute, University of the Western Cape, Bellville 7535, South Africa;
emily.amor.stander@gmail.com (E.A.S.); wesleywt@gmail.com (W.W.); yamkelamgwatyu@gmail.com (Y.M.);
pvh@sanbi.ac.za (P.v.H.)
2Institute for Microbial Biotechnology and Metagenomics, University of the Western Cape,
Bellville 7535, South Africa
3Applied Microbial and Health Biotechnology Institute, Cape Peninsula University of Technology,
Bellville 7535, South Africa; rautenbachf@cput.ac.za (F.R.); marnewickJ@cput.ac.za (J.M.);
LeRoesM@cput.ac.za (M.L.R.-H.)
4Department of Biotechnology, University of the Western Cape, Bellville 7535, South Africa
*Correspondence: uhesse@uwc.ac.za
Received: 24 June 2020; Accepted: 4 August 2020; Published: 23 September 2020
Abstract:
Rooibos (Aspalathus linearis), widely known as a herbal tea, is endemic to the Cape Floristic
Region of South Africa (SA). It produces a wide range of phenolic compounds that have been associated
with diverse health promoting properties of the plant. The species comprises several growth forms
that differ in their morphology and biochemical composition, only one of which is cultivated and
used commercially. Here, we established methodologies for non-invasive transcriptome research of
wild-growing South African plant species, including (1) harvesting and transport of plant material
suitable for RNA sequencing; (2) inexpensive, high-throughput biochemical sample screening;
(3) extraction of high-quality RNA from recalcitrant, polysaccharide- and polyphenol rich plant
material; and (4) biocomputational analysis of Illumina sequencing data, together with the evaluation
of programs for transcriptome assembly (Trinity, IDBA-Trans, SOAPdenovo-Trans, CLC), protein
prediction, as well as functional and taxonomic transcript annotation. In the process, we established
a biochemically characterized sample pool from 44 distinct rooibos ecotypes (1–5 harvests) and
generated four in-depth annotated transcriptomes (each comprising on average
≈
86,000 transcripts)
from rooibos plants that represent distinct growth forms and differ in their biochemical profiles.
These resources will serve future rooibos research and plant breeding endeavours.
Keywords:
rooibos; Aspalathus linearis; medicinal plants; non-model organism; transcriptomics;
method evaluation; NGS; RNA-Seq; biochemical screening; bioinformatics
1. Introduction
Rooibos (Aspalathus linearis) is an indigenous South African shrub widely used to brew the popular
rooibos herbal tea. The genus Aspalathus (Fabaceae) includes more than 270 species, most of which
are endemic to the Cape Floristic Region of South Africa. Eight distinct A. linearis growth types have
been described [
1
], which vary in their geographic distribution as well as in their morphological,
chemical and genetic characteristics [
2
–
4
]. The Southern and Northern sprouters are prostrate shrublets
(max 50 cm high). The Grey sprouters, Nieuwoudtville sprouters, and Wupperthal type plants are
medium sized densely branched shrubs. The Red type, Black type, and Tree type plants are erect,
slender bushes that can reach up to 2 m in height. The rooibos growth types can be further categorized
BioTech 2020,9, 19; doi:10.3390/biotech9040019 www.mdpi.com/journal/biotech
BioTech 2020,9, 19 2 of 23
based on their fire survival strategies: sprouters regrow after fire from an underground lignotuber
while seeders are destroyed by veld fires and repopulate from seeds [
3
,
5
,
6
]. The commercially
cultivated rooibos plants (Nortier/Rocklands type) descend from successively selected wild Red type
plants originally collected from the northern parts of the Cederberg Mountains and the Pakhuis
Pass areas [
1
]. An increasing body of literature provides scientific evidence for beneficial health
effects of rooibos, including anti-inflammatory [
7
–
10
], cardioprotective [
11
–
16
], anti-diabetic [
17
–
20
],
and anti-obesity [
21
–
23
] properties (for reviews see [
24
–
27
]). These bio-activities have been associated
with the antioxidant properties of diverse phenolic compounds produced by the rooibos plants. Rooibos
herbal tea is caffeine-free, low in tannins, high in volatile compounds, and rich in a unique combination
of polyphenols [
28
]. The primary phenolic compound found in commercial unprocessed/green rooibos
plant material is a C-glucosyl dihydrochalcone known as aspalathin. In commercial rooibos plants,
it represents 4–12% of the plant dry weight [
29
–
31
]. Other major rooibos compounds include the
flavones iso-orientin and orientin (the two oxidation products of aspalathin), luteolin, and chrysoeriol;
as well as the flavonols rutin, hyperoside, iso-quercetin, and quercetin [
32
–
34
]. Wild rooibos plants
were shown to have distinct chemical profiles, which differed between populations but were very
similar within the same population. Some wild rooibos plants were found not to produce aspalathin at
all. In these plants, orientin, iso-orientin, and rutin were found to be the main phenolic compounds [
2
].
The biosynthesis pathway for rooibos dihydrochalcones and flavonoids has recently been proposed [
4
].
The field of plant transcriptomics has been revolutionized by Next Generation Sequencing (NGS)
technologies [
35
,
36
]. Transcriptomics provides a wealth of information on plant genes without prior
knowledge on the underlying genome sequence, which greatly facilitates research on non-model
organisms (such as rooibos). Medicinal plants usually belong to taxonomic groups that do not have
high quality reference genomes [
37
]. In most cases, research focuses exclusively on identification of
genes involved in secondary metabolite biosynthesis. On average, plants encode between 20,000 to
60,000 genes, of which only 15–25% contribute to secondary metabolite production. For these plant
species, RNA-Seq is considered the method of choice to gather information on secondary metabolism
associated plant genes, as whole genome analysis is considered redundant [
38
]. Knowledge on the
genes and biosynthetic pathways involved in the production of economically important metabolites is
increasingly exploited in synthetic biology and genetic engineering programs. Transcriptome data
of non-model plants are already employed to optimize
in vitro
and
in vivo
biosynthesis of medicinal
compounds [39–41], biodiesel feedstock’s [42–44], and essential oils [45–47].
South Africa is home to the unique Cape Floristic Region, where more than 70% of the plants are
endemic [
48
]. The country has a rich traditional history in the application of diverse plant species for
medicinal purposes. Yet, agricultural production systems for the nearly 3000 plant species used for
traditional medicine are all but missing, and most plants are collected from the wild [
49
]. To promote
research on the endemic medicinal plants of South Africa, this study aimed to locally establish all
procedures essential for plant transcriptome analysis, including sample collection and biochemical
screening methods geared at identifying interesting ecotypes from distant geographic locations, as well
as laboratorial procedures and biocomputational methods for plant transcriptome analysis. The second
aim of this study was to generate rooibos transcriptomes that would allow research on genes and
biosynthetic pathways associated with diverse important plant traits (e.g., medicinal compound
production, stress tolerance, growth form). To facilitate gene discovery, transcriptomes were sequenced
from four rooibos plants that represent different growth types with distinct morphological traits and
contrasting biochemical profiles (including aspalathin producers and non-producers).
2. Materials and Methods
2.1. Reagents and Materials
All solvents (analytical grade) were acquired from Merck (Kenilworth, NJ, USA). Aspalathin was
obtained from Chromadex Chemicals (Los Angeles, CA, USA). Rutin, quercetin, luteolin, catechin,
BioTech 2020,9, 19 3 of 23
4-dimethylaminocinnamaldehyde, and p-anisaldehyde (4-methoxybenzaldehyde) were purchased
from Sigma-Aldrich (Johannesburg, South Africa), while orientin, iso-orientin, vitexin, iso-vitexin,
hyperoside, and chrysoeriol were purchased from Extrasynthese (Genay, France).
2.2. Plant Sampling
Morphologically distinct rooibos ecotypes, including commercially farmed and wild rooibos plants
representing five A. linearis growth forms, were sampled in the Clanwilliam (Western Cape), Wupperthal
(Western Cape), and Nieuwoudtville (Northern Cape) regions in the Cederberg Mountains, South Africa.
Classification of wild rooibos growth types were verified by Prof Van Wyk (University of Johannesburg)
and the accurate locality was recorded by GPS. QGIS (Version 3.2.1-Bonn, https://www.qgis.org/en/site/)
was used to construct the map of sampling locations using the GPS coordinates. Sampling took place
in October 2016 and in February 2017. Up to 100 g of rooibos leaves and stems were collected from
four sides of each plant, flash frozen in the field with liquid nitrogen, transported in a liquid nitrogen
Dewar (for RNA analysis) or on dry ice (for biochemical screening), and stored at −80 ◦C.
2.3. Biochemical Analyses
To prepare plant extracts, frozen plant samples were first freeze dried and ground to a fine powder.
Then, 50 mg of plant material was extracted with 5 mL methanol. All samples were mixed at 40 rpm
on a Benchmark rotating mixer (Benchmark Scientific, Edison, NJ, USA) at 4 ◦C for 24 h.
Thin-layer chromatography was performed on all collected plant samples as described in [
50
].
Four plants that differed in their morphological characteristics and in their thin-layer chromatography
(TLC) profiles were selected for further biochemical characterization. The October samples of these
plants were analysed using HPLC. HPLC analysis of rooibos methanol extracts was conducted
following the method described in [
51
] using a 15 cm
×
4.6 mm Nucleosil 120-5C18 column (5
µ
M,
Sigma Aldrich) on an Agilent 1200 series HPLC system coupled to a diode array detector (DAD)
(Santa Clara, CA, USA). Column temperature was maintained at 21
◦
C. Mobile phase A was 300
µ
L/L
trifluoracetic acid in water and mobile phase B was 300
µ
L/L trifluoracetic acid in methanol. A sample
volume of 20
µ
L was injected. A constant flow rate of 1 mL/min was maintained and the gradient
elution was performed as follows: 5% B for 5 min, linear increase to 80% B over 20 min, decrease to
35% B over 3 min, 35% B for 2 min, and re-equilibration to 5% B. Acquisition was set at 287 nm for
aspalathin detection and 360 nm for the other polyphenols. Calibration curves were prepared from
standards (aspalathin, rutin, quercetin, luteolin, catechin, orientin, iso-orientin, vitexin, iso-vitexin,
hyperoside, and chrysoeriol) ranging in concentrations from 5 to 100 mg/L. The absorbance of each
standard increased linearly over the concentration range. Peaks were identified by comparison to
retention times and UV spectra of standards. The October samples from all four selected rooibos plants
were chosen for transcriptome sequencing.
2.4. RNA Extraction
RNA was extracted using a modified CTAB method [
52
]. In brief, 250 mg of plant material was
powdered in liquid nitrogen and 100 mg polyvinylpolypyrrolidone (PVPP). The powder was added
to 1.5 mL RNA extraction buffer (100 mM Tris-HCL, pH 8.0; 25 mM ethylenediaminetetraacetic acid
(EDTA); 2 M NaCl; 2% (w/v) polyvinylpyrrolidone (PVP); and 2% (v/v)
β
-mercaptoethanol), vortexed
and incubated at 65
◦
C for 30 min with intermittent vortexing. Samples were then centrifuged for
10 min at room temperature. All centrifugation steps were performed at 16,000
×
g. The supernatant,
containing nucleic acids, was extracted twice with equal volumes of chloroform:isoamyl alcohol (24:1
v/v) and centrifuged for 10 min at 4
◦
C. Thereafter, the supernatant was transferred to a new RNAse-free
microcentrifuge tube and LiCl was added to a final concentration of 2M. RNA was precipitated
overnight at 4
◦
C and pelleted after centrifugation at 4
◦
C for 1h. The RNA pellet was gently rinsed
with 500
µ
L 70% (v/v) ethanol, air dried, and dissolved in 30
µ
L DEPC-treated water. RNA purity was
determined spectrophotometrically at 230, 260, and 280 nm with a NanoDrop
™
(ND1000, Thermo
BioTech 2020,9, 19 4 of 23
Fisher Scientific, Waltham, MA, USA) by analyzing the A260:A280 and A260:A230 ratios. Additionally,
RNA concentration was measured with the Qubit Fluorometer (Thermo Fisher Scientific) and Qubit
RNA BR Assay Kit (Thermo Fisher Scientific). RNA integrity was evaluated by visualization of rRNA
bands on a 1.2% (w/v) denaturing agarose gel.
2.5. Illumina Sequencing
RNA-seq library preparation and sequencing were performed at the UKHC Genomics Core
Laboratory (UK Chandler Hospital Lexington, KY, USA). Libraries were prepared with an average
insert size of ~450 bp according to the Illumina TruSeq RNA Library Prep Kit v2 (Illumina, San Diego,
CA, USA). Sequencing was performed on two lanes of the Illumina HiSeq 2500 platform (Illumina,
San Diego, CA, USA), generating between 26.68 and 66.46 million read pairs (2
×
151 nt) according to
the manufacturer’s protocols.
2.6. RNA-Seq Read Quality Control and Preprocessing
The supplied sequencing data had been preprocessed by the service provider, who used
the Illumina bcl2FASTQ2 Conversion Software v2.20 (Illumina, San Diego, CA, USA) to perform
format conversion, demultiplexing, and adapter trimming, as well as removal of PhiX reads and
reads that did not contain the expected index (undetermined reads). To improve data quality,
reads were further processed using Trimmomatic (v 0.36, [
53
]) with the following parameters:
ILLUMINACLIP:TruSeq3-PE-2.fa:2:30:10 CROP:140 HEADCROP:13 MINLEN:30. Data quality was
assessed before and after quality trimming using FastQC (v 0.11.4, http://www.bioinformatics.babraham.
ac.uk/projects/fastqc/).
2.7. De novo Transcriptome Assemblies
The quality processed sequencing data from one or the four sequenced rooibos samples (plant C)
was used to evaluate the performance of four transcriptome assemblers: Trinity (v 2.5.1, [
54
]),
SOAPdenovo-Trans-127mer (v 1.03, [
55
]), IDBA-tran (v 1.1.1, [
56
]), and CLC Genomics Workbench
(v 7.5.1, Qiagen). Trinity was performed using default parameters (25-mer). Only one k-mer (25-mer)
was chosen for the SOAPdenovo-Trans-127mer, run using the following configuration: map_len =32,
asm_flags =3, reverse_seq =0. IDBA-tran was used to assemble k-mer sizes of 25–71 with a step size
of 10. The CLC Genomics Workbench was used with default parameters. Transcriptome completeness
was assessed using Benchmarking Universal Single-Copy Orthologs (BUSCO v0.2, [
57
,
58
]) with
parameter -m tran and the embryophyta_odb9 database (accessed 2 August 2018). The read alignment
rate was determined using Bowtie2 (v2.2.3, [59]) in end-to-end mode and with a “sensitive” setting.
2.8. Protein Prediction
TransDecoder (v5.2.0, https://github.com/TransDecoder/TransDecoder/wiki), GenemarkS-T
(v 5.1, [
60
]) and ANGEL (v 2.4, https://github.com/PacificBiosciences/ANGEL) were used to identify
candidate coding regions from the assembled transcripts. For TransDecoder, homology searches were
included as open reading frame retention criteria. Transcripts with homology to known proteins were
identified with (1) BlastP (v 2.2.31) against the uniref90 database (accessed 15 June 2018) with an
E-value cutoffof 10
−5
, and (2) HMMER (v 3.1b2) queried against the Pfam-A database (accessed 15 June
2018). GenemarkS-T was used with a Markov chain order of 5 and default parameters. The following
ANGEL scripts and parameters were employed: (1) dumb_predict.py set to “use_rev_strand”,
(2) angel_make_training_set.py set to “random”, (3) angel_train.py with default parameters, and
(4) angel_predict.py set to “use_rev_strand” and “output_mode =best”.
BioTech 2020,9, 19 5 of 23
2.9. Functional and Taxonomic Annotation
To identify orthologous sequence, OrthoFinder (v2.2.6, [
61
]) was run using the predicted proteins
from the rooibos transcriptomes and the protein sequences for Arabidopsis thaliana (Arabidopsis
information Resource, TAIR; accessed 25 October 2018), Medicago truncatula Mt4.0v2 (http://www.
medicagogenome.org; [
62
]; accessed 25 October 2018), Oryza sativa V7 ([
63
]; accessed 25 October
2018), Lotus japonicus V3.0 (miyakogusa.jp 3.0 database: http://www.kazusa.or.jp/lotus/; [
64
]; accessed
25 October 2018), and Lupinus angustifolius V1.0 (http://www.lupinexpress.org/; [
65
]; accessed 27
January 2019]) using DIAMOND V0.9.21 [
66
] all-versus-all. The transcripts were further annotated using
DIAMOND BLASTX against the nrNCBI database (accessed 10 August 2018) with the maximum E-value
set to 1E-5. In addition, all transcripts larger than 1000 bp were assigned K-numbers (KEGG Orthology
identifiers) using the web-based automatic annotation server KAAS [
67
] capturing the single-directional
best hit (SBH analysis) via BLASTX search against the complete plant gene data set. Independently, all
predicted rooibos protein sequences were also annotated with K-numbers using eggNOG-mapper
(http://eggnogdb.embl.de/#/app/downloads; [
68
]; accessed 23 November 2018). After removing
duplicated K-numbers from the KAAS and the eggNOG datasets, respectively, the resulting two
lists were submitted to KEGG mapper (https://www.genome.jp/kegg/tool/map_module.html) for
reconstruction of KEGG modules. Subsequently, the modules were manually filtered to represent only
the 171 plant-specific modules currently available at KEGG. Finally, HMMER (V 3.1b2) was used to
annotate pfam domains [
69
] by searching the predicted proteins against the Pfam-A database with the
E-value set to 1E-5. For taxonomic assignment, transcript sequences were classified using Kraken2
v2.0.7 [
70
]. In addition, taxonomic ranks were assigned based on the DIAMOND BLASTX output of
the transcripts using a local tool (https://github.com/pvanheus/diamond_add_taxonomy).
3. Results
3.1. Plant Sample Collection
The first aim of this study was to obtain plant material from a large number of diverse rooibos
plants that would allow selection of morphologically and biochemically distinct ecotypes for subsequent
transcriptome analyses. In total, 44 plants were sampled in spring (October 2016), when plants are
actively growing. Of those, 37 plants survived the summer and were resampled in early autumn
(February 2017), just before the harvest for herbal tea production. The plants represented five A. linearis
growth types, including 34 Red type ecotypes (26 commercial, 4 escaped commercial, and 4 wild),
as well as two Black type, two Nardouwsberg type, two Grey sprouter and four Nieuwoudtville
sprouter ecotypes. The plants originated from 16 geographically distant locations in and around the
Cederberg Mountains (Supplementary Figure S1). The southern-most samples were collected in the
Piketberg region (153 m above sea level), and the northern-most samples originated from the wider
Nieuwoudtville area (817 m above sea level). Most wild rooibos plants sampled in this study were part
of a larger population of plants of the respective growth type; usually one growth type per location.
Only the Black type plants were found to co-occur as single plants with other growth types.
3.2. Biochemical Screening of Plant Material
To visually explore the biochemical variability between the sampled rooibos plants, thin-layer
chromatography (TLC) was used. Figure 1provides a representative example of observed banding
patterns. Band 3 represents aspalathin. Bands 1–6 were present in most of the commercial and
wild rooibos plant samples independent of the harvest but varied in intensity depending on
the TLC run. Bands A and B were only present in the autumn samples (February 2017) of the
Nieuwoudtville sprouters.
BioTech 2020,9, 19 6 of 23
High-Throughput 2020, 9, x 6 of 23
Figure 1. Diversity of thin-layer chromatography (TLC) banding patterns in the rooibos samples.
Bands 1–6 were commonly observed in samples from commercial and wild rooibos plants; bands A
and B were only found in selected plants. S: solvent front.
For transcriptome sequencing, sample selection focused on plants with contrasting growth
characteristics and distinct biochemical profiles. Based on the TLC results and the ancillary
information (growth type, growth characteristics, plant health, and geographic origin), four rooibos
plants were selected for comprehensive biochemical characterization using HPLC-DAD. These
included one commercial Red type plant, one Black type plant, one Grey sprouter, and one
Nieuwoudtville sprouter (Table 1). The selected plants varied greatly in their morphological
characteristics, representing both seeders and sprouters, and showed significant differences in their
biochemical profiles (Table 2). The commercial plant produced significantly more aspalathin,
orientin, iso-orientin, vitexin, iso-vitexin, and hyperoside than the wild ecotypes. Aspalathin was
never detected in samples of the selected Black type and Grey sprouter ecotypes. In contrast, luteolin
concentrations were higher in the wild ecotype samples, and quercetin content was elevated in the
Grey sprouter sample, though concentrations were still only at trace levels. All four plants were
selected for transcriptome sequencing analyses.
Table 3 summarizes results pertaining to RNA extraction and Illumina sequencing of the rooibos
samples. Despite high concentrations in polyphenols, the modified CTAB method was found to be
effective for the extraction of high-quality RNA from the rooibos plant samples, and satisfactory RNA
concentrations (46–59 ng/μL) and RNA integrity number (RIN) values (7.1–8.3) were achieved. On
average, 48 Mio read pairs with an average insert size of 450 bp were generated. Sequencing quality
was high, as more than 99% of the read pairs and 83.7% or the bases were retained after quality
trimming, although the average read length was somewhat reduced to 126.8 nt.
Figure 1.
Diversity of thin-layer chromatography (TLC) banding patterns in the rooibos samples.
Bands 1–6 were commonly observed in samples from commercial and wild rooibos plants; bands A
and B were only found in selected plants. S: solvent front.
For transcriptome sequencing, sample selection focused on plants with contrasting growth
characteristics and distinct biochemical profiles. Based on the TLC results and the ancillary information
(growth type, growth characteristics, plant health, and geographic origin), four rooibos plants were
selected for comprehensive biochemical characterization using HPLC-DAD. These included one
commercial Red type plant, one Black type plant, one Grey sprouter, and one Nieuwoudtville sprouter
(Table 1). The selected plants varied greatly in their morphological characteristics, representing both
seeders and sprouters, and showed significant differences in their biochemical profiles (Table 2).
The commercial plant produced significantly more aspalathin, orientin, iso-orientin, vitexin, iso-vitexin,
and hyperoside than the wild ecotypes. Aspalathin was never detected in samples of the selected Black
type and Grey sprouter ecotypes. In contrast, luteolin concentrations were higher in the wild ecotype
samples, and quercetin content was elevated in the Grey sprouter sample, though concentrations were
still only at trace levels. All four plants were selected for transcriptome sequencing analyses.
Table 3summarizes results pertaining to RNA extraction and Illumina sequencing of the rooibos
samples. Despite high concentrations in polyphenols, the modified CTAB method was found to be
effective for the extraction of high-quality RNA from the rooibos plant samples, and satisfactory
RNA concentrations (46–59 ng/
µ
L) and RNA integrity number (RIN) values (7.1–8.3) were achieved.
On average, 48 Mio read pairs with an average insert size of 450 bp were generated. Sequencing
quality was high, as more than 99% of the read pairs and 83.7% or the bases were retained after quality
trimming, although the average read length was somewhat reduced to 126.8 nt.
BioTech 2020,9, 19 7 of 23
Table 1. Botanical and growth characteristics of rooibos plants selected for transcriptome analyses.
Plant Growth Type Seeder/Sprouter Growth Form Location
ARed type (commercial) seeder upright, densely branched bush S 031◦4301800
E 019◦0703200
B
Nieuwoudtville sprouter
sprouter low-growing, densely branched bush S 031◦4504800
E 019◦0705400
CBlack type seeder tall, slender shrub S 031◦5902100
E 018◦5003500
DGrey sprouter sprouter upright, sparsely branched bush S 032◦3701700
E 019◦0302400
Table 2. Biochemical characterization of rooibos plants selected for transcriptome analyses (October 2016 samples).
Plant Aspalathin Orientin Iso-Orientin Iso-Vitexin Vitexin Hyperoside Luteolin Quercetin
A66.52 ±0.68 a4.70 ±0.19 a6.94 ±0.05 a2.18 ±0.07 a1.98 ±0.10 a2.06 ±0.09 a0.01 ±0.00 a0.01 ±0.00 a
B9.24 ±0.17 b1.56 ±0.04 c1.50 ±0.01 c0.17 ±0.00 c0.49 ±0.01 b0.41 ±0.01 b0.21 ±0.00 b0.01 ±0.00 a
C0.00 ±0.00 c1.82 ±0.08 c1.71 ±0.03 c0.17 ±0.00 c0.23 ±0.01 c0.32 ±0.01 b,c 0.59 ±0.01 c0.01 ±0.00 a
D0.00 ±0.00 c2.29 ±0.06 b2.85 ±0.08 b0.69 ±0.01 b0.25 ±0.00 c0.17 ±0.00 c0.58 ±0.01 c0.05 ±0.00 b
* Concentrations of major flavonoids are provided in mg/100 g dry weight
±
STD (n=3). Biochemical analyses were conducted using HPLC-DAD. Different letters indicate significant
differences as verified using the Tukey test.
BioTech 2020,9, 19 8 of 23
Table 3.
Summary of the sequencing data generated from theOctober 2017 samples of four rooibos ecotypes.
Plant A B C D
RNA yield (ng/µL) 53.0 59.0 58.0 46.0
RNA integrity number (RIN) 8.3 7.1 7.9 8.1
Library insert size (bp) 437 448 479 452
# read pairs (in Mio) 54.7 66.5 26.7 44.1
# read pairs after quality processing (in Mio) 54.6 65.8 26.5 43.8
% read pairs remained after quality processing 99.9 99.1 99.2 99.2
% bases remained after quality processing 83.6 83.7 83.8 83.7
Length after trimming (bp) 30–127 30–127 30–127 30–127
3.3. Assessment of Transcriptome Assembly Programs
Using the reads from sample C as test data, four assembly programs, namely Trinity, IDBA-Trans,
SOAPdenovo-Trans and CLC, were evaluated for their performance in assembly of rooibos RNA
sequencing data (Table 4). For Trinity, two parameter settings were investigated: Trinity_all represents
all assembled transcripts including different isoforms of the same gene, and Trinity_longest comprises
only the longest transcripts per gene/isogroup. Trinity_all produced the highest number of transcripts
(100,778
≥
300 bp), surpassing the other programs in all length categories. The Trinity_all assembly also
showed the highest read realignment rate (97%) and the highest number of matches to the 1440 plant
BUSCOs, identifying in total 92%. However, when this dataset was filtered for the longest transcript per
isoform (Trinity_longest), half of the total number of transcripts (including two-thirds of the transcripts
>1 kb) were lost, as they represented different versions of the same gene/isogroup. Transcript filtering
resulted in a very low proportion of duplicated BUSCO matches in the Trinity_longest assembly (2.5%),
but also reduced informative content and assembly accuracy of the transcriptome (the proportion
of total BUSCO matches dropped to 86% and the number of fragmented BUSCO matches doubled).
IDBA-Trans assembled a substantially lower number of transcripts than Trinity_all, specifically in
the smaller length categories (
≤
1 kb). However, the number of transcripts longer than 1 kb (
≈
41,000)
was comparable to the one obtained with Trinity_all (
≈
44,000), and 90% of the plant BUSCOs were
reassembled. The IDBA-Trans assembly had the highest proportion of duplicated BUSCOs (indicating
redundancy), as well as the lowest number of fragmented BUSCOs and the highest proportion of
transcripts that matched a BUSCO (indicating assembly accuracy). SOAPdenovo-Trans produced
the lowest total number of transcripts (50,503), approximately half of which were longer than 1 kb.
It was the only assembler that produced transcripts longer than 10 kb. However, the concordant
read alignment rate was very low (58%) and the proportions of fragmented (16%) and missing (13%)
BUSCOs were high, indicating low assembly accuracy. CLC assembled a large number of smaller
transcripts (
≈
45,000
≤
1 kb); only 14,169 transcripts were longer than 1 kb. Consequently, this assembly
matched the least number of BUSCOs, and many of the transcripts that did match a BUSCO were
fragmented. The above analyses indicated that IDBA-Trans produced a comprehensive, comparatively
accurate assembly with the lowest proportion of short and/or misassembled fragments. It was therefore
chosen for subsequent assembly of the remaining rooibos transcriptomes.
BioTech 2020,9, 19 9 of 23
Table 4. Comparison of four different de novo transcriptome assemblies, assembled from the processed reads from sample C.
Trinity_all Trinity_longest IDBA_Trans SOAPdenovo_Trans CLC
Assembler running time (h) 22 22 4 1 4
# of Transcripts (≥300 nt) 100,778 53,363 76,784 50,503 59,716
300–500 bp: 28,701 22,049 15,941 11,203 27,416
501–1000 bp: 27,747 14,995 19,885 13,139 18,131
1001–5000 bp: 43,922 16,145 40,701 25,795 14,046
5001–10,000 bp: 408 174 257 364 123
>10,000 bp: 0 0 0 2 0
Overall read alignment rate (%) 97.0 82.9 89.1 76.8 78.7
Read pairs aligned concordantly (%) 82.0 66.0 75.9 58.1 59.8
Complete BUSCOs (C) 1258 1092 1230 1019 923
Single-copy BUSCOs (S) 721 1061 374 825 870
Duplicated BUSCOs (D) 537 31 856 194 53
Fragmented BUSCOs (F) 73 152 59 229 244
Missing BUSCOs (M) 109 196 151 192 273
# of transcripts that hit a BUSCO 2065 1278 2561 1477 1223
% of transcripts that hit a BUSCO 2.0 2.4 3.3 2.9 2.0
BioTech 2020,9, 19 10 of 23
3.4. Assessment of ORF Prediction Tools
Diverse downstream annotation operations can be sped up or only be conducted when using
protein sequences (e.g., BlastX vs. BlastP; pfam annotations). To identify the most suitable program for
prediction of open reading frames (ORFs) on transcripts, the IDBA-Trans assembly of sample C was
used to compare performances of ANGEL, GenemarkS-T, and TransDecoder (Table 5). The highest
number of predicted ORFs were obtained when using ANGEL, which identified ORFs on 93% of the
76,784 transcripts. GenemarkS-T and TransDecoder predicted ORFs on only 71% of the sequences.
While ANGEL and GenemarkS-T could predict multiple ORFs per transcript, TransDecoder predicted
only one. Comparisons with the BUSCO annotations of the transcriptome showed that ORF prediction
resulted in a slight reduction of total BUSCO hits, ranging between 7 (ANGLE) and 19 (GenemarkS-T)
missed BUSCO sequences. For complete BUSCOs, ORF prediction increased the number of single-copy
BUSCOs and reduced the number of duplicated BUSCOs, indicating that a number of nucleotide
sequences that had matched a BUSCO were misassembled and did not encode ORFs. Transcript
misassemblies leading to ORF truncations could also explain the somewhat higher numbers of
fragmented BUSCOs in the predicted ORF datasets. Based on the above results, ANGEL was chosen
for downstream analyses.
Table 5. Comparison of gene-finding algorithms GenemarkS-T, TransDecoder, and Angel.
Transcriptome (76,784) Angel GenemarkS-T TransDecoder
# of predicted proteins - 74,767 58,284 54,205
# of transcripts with ORFs - 71,791 54,754 54,205
ORFs/transcript (mean) - 1.04 ±0.21 1.06 ±0.26 1.00 ±0.00
Complete BUSCOs 1230 1211 1202 1200
Single-copy BUSCOs 374 396 394 390
Duplicated BUSCOs 856 815 808 810
Fragmented BUSCOs 59 71 68 74
Missing BUSCOs 151 158 170 166
3.5. Rooibos Transcriptome Assemblies
IDBA-Trans was used for de novo assembly of the four sequenced transcriptomes (Table 6).
Depending on the sample, the program generated between 76,784 and 96,865 transcripts
≥
300 bp.
Approximately 50% of the transcripts were longer than 1 kb, and few transcripts were longer than 5 kb.
Read usage across all transcriptomes was >66%, with 51.94–75.86% of reads aligning concordantly to
the transcripts. ORFs were predicted on 93–98% of the transcripts. On average, the transcriptomes
comprised 1246 complete and 59 fragmented BUSCOs. RIN values, but not read numbers, appeared
to have an effect on BUSCO statistics. Across the four transcriptomes, we observed that lower RIN
values were associated with higher numbers of missed and fragmented BUSCOs. We also noted that
the number of BUSCO hits did not substantially change if read numbers were increased by 2
×
–2.5
×
,
as seen for sample C vs. samples A and B.
BioTech 2020,9, 19 11 of 23
Table 6. Rooibos transcriptomes.
A B C D
Total Mbps 121.87 122.11 98.02 102.83
Transcripts 91,171 96,865 76,784 80,456
300–500 bp 20,986 24,955 15,941 18,460
501–1000 bp 22,161 24,463 19,885 20,767
1001–5000 bp 47,231 46,736 40,701 40,674
5001–10 000 bp 793 707 257 547
>10,000 bp 0 4 0 8
Predicted ORFs 85,234 91,301 75,426 79,234
Overall read alignment rate (%) 77.11 66.75 89.05 81.11
Read pairs aligned concordantly ≥1×(%) 60.63 51.94 75.86 63.66
Read pairs aligned discordantly (%) 7.63 5.60 8.74 8.57
RIN 8.3 7.1 7.9 8.1
Complete BUSCOs (C) 1291 1221 1230 1242
Complete and single-copy BUSCOs (S) 383 335 374 391
Complete and duplicated BUSCOs (D) 908 886 856 851
Fragmented BUSCOs (F) 48 65 59 62
Missing BUSCOs (M) 101 154 151 136
3.6. Rooibos Transcriptome Annotation: Comparative Genomics
Analysis of orthologous relationships between proteins from different plant species allows transfer
of functional annotations to new protein sequences. In this study, we compared the four rooibos
transcriptomes to the proteomes of three legumes (L. angustifolius, L. japonicus, and M. truncatula) and
the model plants A. thaliana and O. sativa (Supplementary Table S1). Of the 331,195 rooibos proteins,
85% were assigned to orthologous groups (OGs), i.e., were partnered with at least one other sequence
from the investigated datasets. In total, 43,543 OGs included rooibos protein sequences. The number
of OGs per rooibos transcriptome ranged between 26,334 and 29,535 OGs. Of those, 14,682 OGs appear
to represent gene families that are highly conserved among the rooibos growth types as they included
sequences from all four transcriptomes (Figure 2). In this subset, the proportion of rooibos-specific OGs
that did not include proteins from any of the investigated outgroup species (L. angustifolius,L. japonicus,
M. truncatula,A. thaliana, and/or O. sativa) was only 12%. This was in stark contrast to OGs where one
or more rooibos transcriptomes were missing; in these subsets, the proportion of rooibos-specific OGs
ranged between 73% and 90%. While it is possible that some of the unassigned rooibos proteins that did
not group with any other sequence may represent ecotype- or growth form-specific genes, transcript
misassembly or fragmentation is a more likely cause for low sequence similarity to other proteins.
High-Throughput 2020, 9, x 12 of 23
Figure 2. Distribution of rooibos-specific and general plant orthologous groups (OGs) across the
transcriptomes.
3.7. Rooibos Transcriptome Annotation: Taxonomic Transcript Classification
The taxonomic annotations from Kraken2 and NCBI (NR) allowed prediction of the organism
from which the transcripts were derived (Table 7). Kraken2 yielded 17,604 more taxonomic transcript
annotations than sequence comparisons to the NCBI (NR) database, but the latter permitted deeper
taxonomic classification. Both analyses confirmed that the absolute majority of the annotated
transcripts (94.9–99.9%) were of plant origin, most matching proteins from leguminous plants. The
transcriptomes from the wild rooibos plants (B, C, and D) also contained a considerable number of
fungal transcripts. All three samples harbored sequences from Dothistroma septosporum, Ascochyta
rabiei, and Alternaria alternata. In addition, the transcriptomes from plants B and C shared a
considerable number of sequences that matched Baudoinia panamericana, Hortaea werneckii, and Elsinoe
australis. For several transcripts, Kraken2 annotations indicated bacterial origin, but this was not
confirmed by the DIAMOND-NCBI(NR) analysis, which annotated most of them as plant transcripts.
Table 7. Taxonomic transcript annotations using DIAMOND-NCBI(NR) and Kraken2.
A (91,171) B (96,865) C (76,784) D (80,456)
NCBI Nr Taxonomic
category Transcripts % Transcripts % Transcripts % Transcripts %
Fabaceae 62,368 68.41 63,097 65.14 57,558 74.96 57,878 71.94
Other Plants 2905 3.19 3036 3.13 2324 3.03 2566 3.19
Fungi 4 0 3140 3.24 661 0.86 664 0.83
Bacteria 28 0.03 38 0.04 24 0.03 82 0.1
Viruses 15 0.02 14 0.01 4 0.01 8 0.01
Other Eukaryotes 16 0.02 333 0.34 199 0.26 0 0
Total: 65,336 71.66 69,658 71.91 60,770 79.14 61,198 76.06
Kraken2 classification transcripts % transcripts % transcripts % transcripts %
Plant 72,016 78.99 72,771 75.13 62,359 81.21 63,673 79.14
Bacteria 387 0.42 493 0.51 340 0.44 344 0.43
Fungi 78 0.09 1467 1.51 345 0.45 293 0.36
Total: 72,481 79.5 74,731 77.15 63,044 82.11 64,310 79.93
Figure 2.
Distribution of rooibos-specific and general plant orthologous groups (OGs) across
the transcriptomes.
BioTech 2020,9, 19 12 of 23
3.7. Rooibos Transcriptome Annotation: Taxonomic Transcript Classification
The taxonomic annotations from Kraken2 and NCBI (NR) allowed prediction of the organism
from which the transcripts were derived (Table 7). Kraken2 yielded 17,604 more taxonomic transcript
annotations than sequence comparisons to the NCBI (NR) database, but the latter permitted deeper
taxonomic classification. Both analyses confirmed that the absolute majority of the annotated transcripts
(94.9–99.9%) were of plant origin, most matching proteins from leguminous plants. The transcriptomes
from the wild rooibos plants (B, C, and D) also contained a considerable number of fungal transcripts. All
three samples harbored sequences from Dothistroma septosporum,Ascochyta rabiei, and Alternaria alternata.
In addition, the transcriptomes from plants B and C shared a considerable number of sequences that
matched Baudoinia panamericana,Hortaea werneckii, and Elsinoe australis. For several transcripts, Kraken2
annotations indicated bacterial origin, but this was not confirmed by the DIAMOND-NCBI(NR)
analysis, which annotated most of them as plant transcripts.
Table 7. Taxonomic transcript annotations using DIAMOND-NCBI(NR) and Kraken2.
A (91,171) B (96,865) C (76,784) D (80,456)
NCBI Nr
Taxonomic
category
Transcripts % Transcripts % Transcripts % Transcripts %
Fabaceae 62,368 68.41 63,097 65.14 57,558 74.96 57,878 71.94
Other Plants 2905 3.19 3036 3.13 2324 3.03 2566 3.19
Fungi 4 0 3140 3.24 661 0.86 664 0.83
Bacteria 28 0.03 38 0.04 24 0.03 82 0.1
Viruses 15 0.02 14 0.01 4 0.01 8 0.01
Other Eukaryotes 16 0.02 333 0.34 199 0.26 0 0
Total: 65,336 71.66 69,658 71.91 60,770 79.14 61,198 76.06
Kraken2
classification transcripts % transcripts % transcripts % transcripts %
Plant 72,016 78.99 72,771 75.13 62,359 81.21 63,673 79.14
Bacteria 387 0.42 493 0.51 340 0.44 344 0.43
Fungi 78 0.09 1467 1.51 345 0.45 293 0.36
Total: 72,481 79.5 74,731 77.15 63,044 82.11 64,310 79.93
3.8. Rooibos Transcriptome Annotation: Functional Annotation
Functional annotations for the rooibos transcript and protein sequences are summarized in
Table 8. Across the four rooibos transcriptomes, functional annotations were generated for 256,962
transcripts (74%) and 209,529 predicted proteins (63%). Most annotations were obtained through the
DIAMOND-NCBI (NR) analysis, as 72–79% of the rooibos transcripts larger than 300 bp matched a
protein. The KEGG BLAST server KAAS, which only analyses sequences larger than 1 kb, provided
K-numbers and enzyme identifiers for 23% and 13% of all transcripts, respectively. EggNOG yielded
functional annotations for 63% of the protein sequences and K-numbers for 32% of the protein
sequences, substantially outperforming KAAS in the assignment of K-numbers (both, total and unique).
Furthermore, half of the predicted rooibos proteins had Pfam-A annotations.
Table 8. Number of transcript and protein sequences annotated in each rooibos transcriptome.
A B C D
Total transcripts (>300 bp) 91,171 96,865 76,784 80,456
Transcripts >300 bp annotated using NCBI(NR) 65,336 69,658 60,770 61,198
Total transcripts (>1000 bp) 48,024 47,447 40,958 40,958
Transcripts >1000 bp annotated using KEGG 21,040 21,126 19,210 18,646
Total protein sequences 85,234 91,301 75,426 79,234
Proteins annotated using eggNOG: eggNOG annotations
52,688 56,090 50,413 50,338
Proteins annotated using eggNOG: KO annotations 26,011 28,187 25,244 24,894
Proteins annotated using Pfam-A 44,390 46,868 42,244 42,258
BioTech 2020,9, 19 13 of 23
The K-numbers provided by KAAS and eggNOG were used to link the transcripts and proteins
to KEGG modules. Currently, the KEGG database contains 171 plant-specific modules (PSMs).
When combining all rooibos data, 150 PSMs were annotated to completion (120 by KAAS and 148
using eggNOG; Figure 3). When compared to eggNOG, KAAS failed to complete annotation of six
carbohydrate metabolism, six energy metabolism, and 18 genetic information processing modules, but
completed annotation of two modules associated with metabolism of cofactors and vitamins that were
annotated incompletely by eggNOG. Both annotation procedures missed the same six plant-specific
modules. Four of these have only been annotated in algae (M00338 M00185 M00616 M00741) and one
is specific to monocotyledonous plants (M00369). A major signature module that appears to be missing
in both annotation sets of the rooibos transcriptomes was the biosynthetic pathway for “Oxygenic
photosynthesis in plants and cyanobacteria” (M00611). This pathway is present in 39% of the plant
species currently stored in the KEGG database, including model organisms such as A. thaliana and
O. sativa as well as in legumes, such as Glycine max, L. japonicas, and Phaseolus vulgaris.
High-Throughput 2020, 9, x 14 of 23
Figure 3. KAAS vs. eggNOG-mapper in terms of annotated complete plant-specific KEGG modules
covered. The number of KEGG database plant-specific modules that were annotated to completion
by rooibos transcripts using KAAS (blue) or rooibos proteins using eggNOG (orange) are compared.
The total number of each plant specific module in the KEGG database is depicted in brackets.
4. Discussion
4.1. High-Throughput Screening of Wild Plants for Transcriptome Analyses
The first aim of the study was to identify rooibos plants of interest for transcriptome sequencing.
To study rooibos-specific biosynthetic pathways involved in the production of medicinally/bio-active
polyphenols (such as aspalathin), it was essential to find plants with contrasting biochemical profiles.
So far, few studies have focused on the variability of polyphenol profiles in rooibos plants [2,4].
Aspalathin non-producers have only been reported among the wild growing Black type plants and
Grey sprouters [2]. The sampling pool established in this study therefore comprised 44 rooibos
ecotypes from distant geographical locations that span the complete rooibos production region and
included 26 commercial plants and 18 wild rooibos ecotypes representing five rooibos growth types.
Biochemical screening of plant samples for specific compounds of interest can represent a cost-
limiting factor, since many of these compounds can only be detected using expensive technologies
such as HPLC, Near Infrared Spectroscopy (NIS) or Raman Spectroscopy (RS). In this study, high-
throughput biochemical screening was completed using thin layer chromatography (TLC) analyses
that permitted detection of aspalathin and identification of plant-specific fingerprints [50]. TLC is a
simple and easily scalable method for detection of diverse compounds in plant extracts, herbal
products, and foods [66]. When paired with chromatogram densitometry or image analysis it even
permits compound quantification, providing a cost-effective alternative to other detection methods
(e.g., HPLC, NIS, RS). TLC has been extensively used in plant research to generate biochemical
fingerprints that differentiate between species, subspecies, and even ecotypes [71]. It has also been
found useful for screening samples for marker compounds, that facilitate species authentication and
quality control of plant and herbal product samples [72].
Figure 3.
KAAS vs. eggNOG-mapper in terms of annotated complete plant-specific KEGG modules
covered. The number of KEGG database plant-specific modules that were annotated to completion
by rooibos transcripts using KAAS (blue) or rooibos proteins using eggNOG (orange) are compared.
The total number of each plant specific module in the KEGG database is depicted in brackets.
The eggNOG annotation set contained three complete non-plant modules (M00133, M00393,
and M00516), one of which (M00516: SLN1-YPD1-SSK1/SKN7 (osmosensing) two-component
regulatory system) is specific to fungi. This module was annotated to completion in transcriptome
B, the dataset that had the highest number of fungal transcripts. However, the specific transcripts
that encoded these enzymes were all classified as plant transcripts by both, DIAMOND-NCBI (NR)
and Kraken2. The KAAS annotation set contained only one complete non-plant module: M00116 is
specific to bacteria and is associated with the biosynthesis of menaquinone through the chorismate =>
BioTech 2020,9, 19 14 of 23
menaquinol pathway. Only the transcriptomes B and D contained the complete module. Again, the
transcripts that encoded these enzymes were classified as plant transcripts by DIAMOND-NCBI (NR)
and Kraken2.
4. Discussion
4.1. High-Throughput Screening of Wild Plants for Transcriptome Analyses
The first aim of the study was to identify rooibos plants of interest for transcriptome sequencing.
To study rooibos-specific biosynthetic pathways involved in the production of medicinally/bio-active
polyphenols (such as aspalathin), it was essential to find plants with contrasting biochemical profiles.
So far, few studies have focused on the variability of polyphenol profiles in rooibos plants [
2
,
4
].
Aspalathin non-producers have only been reported among the wild growing Black type plants and
Grey sprouters [
2
]. The sampling pool established in this study therefore comprised 44 rooibos ecotypes
from distant geographical locations that span the complete rooibos production region and included
26commercial plants and 18 wild rooibos ecotypes representing five rooibos growth types.
Biochemical screening of plant samples for specific compounds of interest can represent a
cost-limiting factor, since many of these compounds can only be detected using expensive technologies
such as HPLC, Near Infrared Spectroscopy (NIS) or Raman Spectroscopy (RS). In this study,
high-throughput biochemical screening was completed using thin layer chromatography (TLC)
analyses that permitted detection of aspalathin and identification of plant-specific fingerprints [
50
].
TLC is a simple and easily scalable method for detection of diverse compounds in plant extracts,
herbal products, and foods [
66
]. When paired with chromatogram densitometry or image analysis
it even permits compound quantification, providing a cost-effective alternative to other detection
methods (e.g., HPLC, NIS, RS). TLC has been extensively used in plant research to generate biochemical
fingerprints that differentiate between species, subspecies, and even ecotypes [
71
]. It has also been
found useful for screening samples for marker compounds, that facilitate species authentication and
quality control of plant and herbal product samples [72].
Based on morphological plant characteristics and biochemical profiles, four rooibos plants were
selected for transcriptome sequencing. The selected plants represented different growth types (Red
type, Black type, Nieuwoudtville sprouter, and Grey sprouter), and therefore included rooibos
plants that differ in their drought adaptation and fire survival strategies (seeders and sprouters). The
Nieuwoudtville sprouter is an extreme example; it was never observed to produce flowers (as monitored
each spring and autumn for a period of three years). Two of the selected plants did not produce
aspalathin (the Black type and the Grey spouter). The commercial rooibos plant displayed a typical
“super plant” phenotype; it was one of the largest plants in the field on both harvest occasions, having
survived the dry summer months unaffected, and consistently produced high amounts of polyphenols.
4.2. Establishing Biocomputational Procedures for Non-Model Plant Transcriptome Analyses
Plant transcriptomics is on the rise as Next Generation Sequencing technologies provide
cost-effective means to access this exceptional genomic resource. However, biocomputational
procedures must be adapted to address specific problems associated with plant transcriptome
sequencing data. Compared to other eukaryotes, plants have larger gene families, larger amounts of
highly expressed transposable elements [
55
] and highly variable expression levels, found to span up to
five orders of magnitude [
73
,
74
]. Therefore, recent scientific publications focus on the optimization of
strategies for the assembly and annotation of plant transcriptomes [75–77].
In 2017, Wang and Gribskov [
75
] investigated the effectiveness of eight transcriptome assembly
programs (BinPacker, Bridger, IDBA-Trans, Oases-Velvet, SOAPdenovo-Trans, SSP, Trans-ABySS, and
Trinity) for their ability to accurately reassemble RNA-seq data from the model plant A. thaliana.
The authors identified SOAPdenovo-Trans as the most suitable assembly program based on the base
coverages of the genome (portion of bases from the reference genome covered by the transcriptome)
BioTech 2020,9, 19 15 of 23
and the transcriptome (portion of bases on the transcriptome covered by the reference genome), as well
as the read realignment rates. Assembly success can be affected by a diverse number of factors, such as
tissue type, RNA quality, sequencing platform, sequencing depth, and data pre-processing, to name
but a few [
78
,
79
]. It is therefore advisable to re-evaluate assembler performance for new plant RNA
sequencing data, in particular if it was derived from a non-model organism where the genome sequence
is not available for transcript assembly verification. In light of the increasing number of sequencing
projects that focus on non-model organisms, new metrics for quality assessment of transcriptome
assemblies are essential. A rational approach is the analysis of newly assembled transcriptomes for
sequences that show significant similarity to well described, highly conserved genes. Benchmarking
Universal Single-Copy Orthologs (BUSCO; [
57
]) aims to standardize this metric by constructing a
reference database composed of single-copy orthologs that are present in at least 90% of plant species
from OrthoDB (www.orthodb.org).
In this study, five assemblers were assessed for their ability to correctly reconstruct a rooibos
transcriptome. Trinity can be run with two settings either providing multiple isoforms per isogroup or
filtering for the longest isoform per isogroup. The unfiltered Trinity_all assembly produced the highest
number of transcripts (>100,000) and consequently had the highest rate of re-aligned reads (97%). It
also had the highest number of BUSCO hits; however, a large proportion of the transcripts (43%) was
duplicated. Filtering for the longest transcript per isogroup reduced the dataset by half, effectively
removing duplicates (the proportion of duplicated BUSCOs was reduced to 3%). However, the longest
transcript did not always represent the best assembled isoform per isogroup as indicated by substantial
reductions of BUSCO hits. IDBA-Trans produced the second-largest transcriptome (~77,000 transcripts),
which matched a similar number of BUSCOs as the transcripts from the Trinity_all assembly despite
the substantially lower number of transcripts. The high proportion of duplicated BUSCOs (66%)
indicated that the IDBA-Trans dataset was redundant (i.e., contained multiple transcripts per gene).
Since plant genes often encode multiple splice variants [
80
], this dataset represented a more realistic
reconstruction of the rooibos transcriptome than the Trinity_longest assembly. SOAPdenovo-Trans
produced fewer transcripts (~50,000) tending to assemble longer sequences. It was the only assembler
that produced two transcripts >10 kb (which were assembled correctly both matching the SYD-like
chromatin structure-remodeling complex protein from L. angustifolius). However, this assembly was
less informative and more fragmented than the IDBA-Trans assembly as indicated by the BUSCO
analysis results. CLC was outperformed by the other assemblers in all assessed parameters. The
results of this study therefore contrasted with the findings by Wang and Gribskov (2017) [
75
] that
SOAPdenovo-Trans is the most suitable assembler for plant data analyses, indicating that IDBA-Trans
may be a better alternative for some datasets. Identification of ORFs and subsequent protein prediction
facilitates functional annotation of transcripts permitting the use of protein-based annotation programs
such as eggNOG-mapper and HMMER-Pfam. Therefore, three protein prediction algorithms (ANGEL
GenemarkS-T and TransDecoder) were compared for their accuracy in ORF prediction using the
IDBA-Trans assembly of the transcriptome from plant C. The highest number of ORFs was identified by
ANGEL resulting in somewhat better BUSCO annotations. It was therefore considered most suitable
for ORF and protein prediction analyses.
4.3. The Rooibos Transcriptomes
The four transcriptomes yielded on average approximately 86,000 transcripts per transcriptome.
Over 90% of these transcripts were predicted to encode proteins. Combined, these transcriptomes
matched over 90% of the 1440 plant BUSCO sequences indicating that the datasets are comprehensive
in terms of functionally meaningful sequences. Orthology analyses, which included the predicted
proteins from the four rooibos transcriptomes and the complete protein datasets from the sequenced
genomes of three legumes (L. angustifolius,L. japonicus, and M. truncatula) as well as distantly related
model plants (A. thaliana and O. sativa) supported this assumption. The number of OGs shared between
the protein datasets from already sequenced plant genomes (10,685–13,719 OGs) was comparable to
BioTech 2020,9, 19 16 of 23
the number of OGs shared between the respective rooibos transcriptomes and the other plant species
(10,873–13,546 OGs), implying that most common plant gene families were present in the rooibos
datasets. Surprisingly, the rooibos transcriptomes shared the highest numbers of OGs with the protein
dataset from L. japonicas although A. linearis is much closer related to L. angustifolius. However, the
protein dataset from L. angustifolius was notably smaller than the ones from the other two legumes,
which may explain the smaller number of OGs shared with the rooibos transcriptomes. M. truncatula is
taxonomically just as distantly related to rooibos as L. japonicus and the protein dataset was larger, yet
on average it shared approximately 107 fewer OGs with the rooibos datasets. First screens indicated
that many of the OGs missing in M. truncatula appear to encode proteins involved in signal transduction.
Future investigations are necessary to explain this observation. Only 15% of the rooibos proteins (av.
12,828 per transcriptome) could not be assigned to an OG, i.e., they did not have counterparts in the
rooibos transcriptomes or in any of the protein datasets from sequenced plant genomes. This number
is comparable to results obtained for other recently sequenced non-model plant datasets (e.g., ~24,000
in Zingiberales: [
81
]; 15,296 in geophytes: [
82
]). These sequences may represent rooibos-specific genes,
but also chimeras and/or truncated ORFs.
Taxonomic classification of transcripts is often omitted in transcriptome analyses. However, it
represents an essential procedure if one wants to avoid contamination of databases and subsequent
propagation of misannotations. In this study two approaches were investigated: Kraken2 and
DIAMOND-searches against NCBI(NR). For the four rooibos transcriptomes, Kraken2 provided
taxonomic affiliations for a higher number of transcripts, annotating between 77% and 82% of
the transcript sequences, as compared to DIAMOND-NCBI(NR), which classified 69% to 78% of
the transcripts. Kraken2 is a “fast” sequence analysis program primarily designed for taxonomic
classification of sequences in metagenomic datasets, which often consist of millions to billions of reads.
Therefore, there is a trade-offbetween sensitivity, accuracy, performance, speed, and computational
requirements. Although these types of programs are useful in performing “first-pass” taxonomic
analyses it is crucial that their assignments are validated using BLAST-based procedures [
83
]. The
absolute majority of the transcripts were of plant origin, most hitting legume sequences as identified
by DIAMOND-NCBI(NR) analysis. However, bacterial and fungal sequences were also predicted. In
this study, bacterial sequences should have been very rare if not completely absent from the datasets:
prior to sequencing, RNA samples had been enriched for mRNA using oligo(dT) beads; bacterial RNA
is not polyadenylated and should not have been captured. However, Kraken2 annotated 1564 (0.5%)
of the transcripts as bacterial. For most of them, the DIAMOND-NCBI(NR) annotations indicated
plant origin, suggesting that in these cases the Kraken2 annotations were incorrect. This bias towards
bacterial sequence annotations may be associated with the Kraken2 database; it contains only the
NCBI(RefSeq) proteins derived from fully sequenced genomes. Since the number of sequenced
bacterial genomes is far higher than the numbers of fungal and plant genomes, annotation biases
are inevitable. However, even the DIAMOND-NCBI(NR) analysis indicated bacterial origin for 172
transcripts (0.05%). Further investigations showed that some of these transcripts encoded phage
proteins (Replication-associated protein A) and transposases, which are subject to horizontal gene
transfer and therefore difficult to taxonomically classify. Some transcripts encoded cell wall associated
hydrolases and acyl-CoA-binding proteins. These enzymes can be found in both plants and bacteria,
and misclassification due to sequence similarity is likely. For other transcripts, the annotation was
based on the shorter ORF, and the longer ORF of the same transcript would encode a plant protein. We
conclude that while it is not impossible that some of the above transcripts originated from bacteria, the
absolute majority of them were misclassified. Notable numbers of fungal transcripts were identified
only in the transcriptomes from the wild rooibos plants B, C, and D although all appeared healthy
at harvest time. The highest proportion of fungal transcripts were found in the transcriptome of
plant B (3% as per DIAMOND-NCBI(NR) analysis). Most of these transcripts matched the plant
pathogenic fungi D. septosporum, known to cause red band needle blight in conifers [
84
], and A. rabiei,
the causative agent of ascochyta blight in diverse plant species [
85
]. In transcriptome D the majority
BioTech 2020,9, 19 17 of 23
of fungal transcripts apparently originated from A. alternata, another well-known plant pathogen.
These transcripts indicate the presence of low levels of fungal infections in the wild rooibos plants.
Commercial rooibos plants are often treated with fungicides and pesticides which may explain the near
absence of fungal transcripts in the investigated transcriptome from plant A. Of interest was also the
finding that plants B and C, though separated by a distance of 37 km, both appeared to host the same
two black yeast species. B. panamericana has not yet been described in plants but rather as a fungus
that grows on walls, roof tiles, and on vegetation around distilleries, giving it the more common name
“whisky fungus” [
86
]. H. werneckii is a halotolerant yeast that was recently found to endophytically
colonize the Chinese medicinal plant Aegiceras comiculatum [
87
]. These findings emphasize the power of
transcriptome analysis as a tool for investigating symbiotic relationships with diverse microorganisms
that populate plants, provided that database biases are taken into consideration.
Up to 256,962 rooibos transcripts (74%) and 209,529 (63%) predicted protein sequences could be
functionally annotated, showing significant matches to sequences and/or protein domain profiles in
the NCBI(NR), KEGG, eggNOG, and/or Pfam-A databases. Most annotations were derived through
the DIAMOND-NCBI(NR) analysis (74%). Though informative, these annotations are difficult to
summarize. KEGG and eggNOG database searches provided K-numbers which linked the rooibos
transcripts to biosynthetic pathway modules (KEGG modules) and allowed assignment of enzyme
numbers and gene ontology terms. The eggNOG-mapper provided more plant-specific K-number
assignments than the KEGG Automatic Annotation Server, KAAS (4163 versus 3736 K-numbers).
The KAAS online server accepts only a limited number of sequences requiring batch-analysis;
therefore, only transcripts larger than 1 kb were selected for analyses. This not only reduced the total
number of K-numbers identified by KAAS but also the number of unique K-numbers for the rooibos
transcriptome datasets.
KEGG represents biosynthetic pathways as separate building blocks. The smallest units are the
enzymes that have unique identifiers—the K-numbers. Enzymes that catalyze a specific reaction are
grouped into blocks, which themselves are arranged into biosynthetic pathways—the KEGG pathway
modules. A given KEGG pathway module is considered complete if all blocks are represented by at
least one K-number. KEGG also provides a similar hierarchical architecture for structural complexes,
functional sets, and signature modules. Currently the KEGG database contains 171 plant-specific KEGG
modules. The KAAS annotations completed 70%, and the eggNOG-mapper annotations completed
87% of the plant-specific modules. Only six plant-specific KEGG modules were missing in both
datasets. Four of the missing modules are specific to algae and would therefore not be expected to be
present in rooibos. One important plant-specific module missing in both datasets was the pathway
module M00611 (oxygenic photosynthesis in plants and cyanobacteria), which is present in 39% of all
plant KEGG entries. This module comprises three submodules: photosystem II (M00161), photosystem
I (M00163), and Reductive pentose phosphate cycle (Calvin cycle) (M00165). It is important to note that
separately these three modules are NOT specific to plants but are also found in protists and bacteria.
However, these three modules together form the supermodule M00161, which is plant-specific. Upon
further inspection, it was found that both the KAAS and the eggNOG datasets contained the complete
submodule M00165. The KAAS dataset lacked several K-numbers from modules M00161 and M00163
(containing five of the six K-numbers for photosystem I and four of the six K-numbers for photosystem
II). The three missing K-numbers in the KAAS dataset were entries for proteins that are smaller than
354 amino acids (psbD: 353 amino acids psbF: 39 amino acids psaC: 81 amino acids). Therefore, their
transcript sequences did not pass the minimum transcript length cutoff(1kb threshold) set for KAAS
server analyses. The eggNOG dataset was more complete lacking only one K-number (k02708) for
photosystem II, which may have been missed due to low transcript expression levels or transcript
misassembly. These results exemplify that a sequence length cutoffof 1kb is not suitable for functional
annotation of plant transcriptomes and that KEGG annotations should preferably be conducted using
eggNOG on a local cluster.
BioTech 2020,9, 19 18 of 23
The above analyses allowed the establishment of a biocomputational pipeline for comprehensive,
high-throughput plant transcriptome analysis (Figure 4).
High-Throughput 2020, 9, x 18 of 23
171 plant-specific KEGG modules. The KAAS annotations completed 70%, and the eggNOG-mapper
annotations completed 87% of the plant-specific modules. Only six plant-specific KEGG modules
were missing in both datasets. Four of the missing modules are specific to algae and would therefore
not be expected to be present in rooibos. One important plant-specific module missing in both
datasets was the pathway module M00611 (oxygenic photosynthesis in plants and cyanobacteria),
which is present in 39% of all plant KEGG entries. This module comprises three submodules:
photosystem II (M00161), photosystem I (M00163), and Reductive pentose phosphate cycle (Calvin
cycle) (M00165). It is important to note that separately these three modules are NOT specific to plants
but are also found in protists and bacteria. However, these three modules together form the
supermodule M00161, which is plant-specific. Upon further inspection, it was found that both the
KAAS and the eggNOG datasets contained the complete submodule M00165. The KAAS dataset
lacked several K-numbers from modules M00161 and M00163 (containing five of the six K-numbers
for photosystem I and four of the six K-numbers for photosystem II). The three missing K-numbers
in the KAAS dataset were entries for proteins that are smaller than 354 amino acids (psbD: 353 amino
acids psbF: 39 amino acids psaC: 81 amino acids). Therefore, their transcript sequences did not pass
the minimum transcript length cutoff (1kb threshold) set for KAAS server analyses. The eggNOG
dataset was more complete lacking only one K-number (k02708) for photosystem II, which may have
been missed due to low transcript expression levels or transcript misassembly. These results
exemplify that a sequence length cutoff of 1kb is not suitable for functional annotation of plant
transcriptomes and that KEGG annotations should preferably be conducted using eggNOG on a local
cluster.
The above analyses allowed the establishment of a biocomputational pipeline for
comprehensive, high-throughput plant transcriptome analysis (Figure 4).
Figure 4. Established biocomputational pipeline for high-throughput comprehensive plant
transcriptome analysis.
5. Conclusions
This study represents a first undertaking to investigate the genomic background of an endemic
South African medicinal plant species. The rooibos transcriptomes provide a first extensive dataset
that can be mined for genes involved in diverse biosynthetic pathways of interest: medicinal
compound production, stress response, morphological characteristics, and plant-microbe
interactions to name but a few. The methods established for biocomputational sequencing data
analyses are applicable to a wide range of other non-model plant species.
Figure 4.
Established biocomputational pipeline for high-throughput comprehensive plant
transcriptome analysis.
5. Conclusions
This study represents a first undertaking to investigate the genomic background of an endemic
South African medicinal plant species. The rooibos transcriptomes provide a first extensive dataset that
can be mined for genes involved in diverse biosynthetic pathways of interest: medicinal compound
production, stress response, morphological characteristics, and plant-microbe interactions to name but
a few. The methods established for biocomputational sequencing data analyses are applicable to a
wide range of other non-model plant species.
Supplementary Materials:
The following are available online at http://www.mdpi.com//9/4/19/s1, Figure S1:
Distribution of the collected rooibos ecotypes sampled for this study, Table S1: Number of overlapping orthogroups
predicted by OrthoFinder.
Author Contributions:
Conceptualization, E.A.S., F.R., J.M., M.L.R.-H., and U.H.; methodology, E.A.S., F.R.,
P.V.H., J.M., M.L.R.-H., U.H., and W.W.; software, E.S. and P.V.H.; validation, E.A.S., F.R., W.W., Y.M., and U.H.;
formal analysis, E.A.S., F.R., W.W., Y.M., and U.H.; investigation, E.A.S., F.R., W.W., Y.M., and U.H.; resources, J.M.,
M.L.R.-H., and U.H.; data curation, E.A.S., F.R., P.V.H., W.W., and Y.M.; writing—original draft preparation, E.A.S.
and U.H.; writing—review and editing, E.A.S. and U.H.; visualization, E.A.S. and U.H.; supervision, J.M., F.R.,
M.L.R.-H., and U.H.; project administration, U.H., J.M., and M.L.R.-H.; and funding acquisition, U.H., J.M., and
M.L.R.-H. All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by the National Research Foundation (NRF), grant numbers RTF150421117446
and CSUR150714125961. Opinions expressed, and conclusions arrived at, are those of the authors and are not
necessarily to be attributed to the NRF.
Acknowledgments:
For assistance in plant collection we herewith would like to sincerely thank Rooibos LTD,
specifically Johan Brand and Jan Perang; the Fair Wupperthal Rooibos Cooperative, specifically Edgar Valentyn;
Darren Engelbrecht and Ruth Cuttings from the Department of Agriculture, Land Reform, and Rural Development
(Northern Cape); as well as the following rooibos farmers: Adam and Johan Christiaans, Annes and Johan Brand,
Carina Marais, Chrisjan Fortuin, Collin Fredericks, Danie Carstens, Davie Kritzinger, Deon Lambrechts, Edgar
Valentyn, Frances Nel, Frans Du Plessis, Frans Kotze, Fritz Hanekam, Gerrie Olivier, Ian Brand, Johan and Rita
Bruwer, Niklaas Kupido, and Willie Nel. We are sincerely grateful to Ben-Erik van Wyk for assistance in the
classification of the wild rooibos plants, to Jennifer Webb for constructing and sequencing the genomic library, and
to Eugene de Beste for support on the High-Performance Computing Cluster and the installation and maintenance
of various software tools.
BioTech 2020,9, 19 19 of 23
Conflicts of Interest: The authors declare no conflict of interest.
Data Availability:
The datasets presented in this article will be made available by the authors for research purposes.
Requests to access the datasets should be directed to the corresponding author (Uljana Hesse; uhesse@uwc.ac.za).
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