Abstract
Background
Herbal remedies of Echinacea purpurea tinctures are widely used today
to reduce common cold respiratory tract infections.
Methods
Transcriptome, epigenome and kinome profiling allowed a systems biology
level characterisation of genomewide immunomodulatory effects of a
standardized Echinacea purpurea (L.) Moench extract in THP1 monocytes.
Results
Gene expression and DNA methylation analysis revealed that Echinaforce®
treatment triggers antiviral innate immunity pathways, involving tonic
IFN signaling, activation of pattern recognition receptors, chemotaxis
and immunometabolism. Furthermore, phosphopeptide based kinome activity
profiling and pharmacological inhibitor experiments with filgotinib
confirm a key role for Janus Kinase (JAK)-1 dependent gene expression
changes in innate immune signaling. Finally, Echinaforce® treatment
induces DNA hypermethylation at intergenic CpG, long/short interspersed
nuclear DNA repeat elements (LINE, SINE) or long termininal DNA repeats
(LTR). This changes transcription of flanking endogenous retroviral
sequences (HERVs), involved in an evolutionary conserved (epi) genomic
protective response against viral infections.
Conclusions
Altogether, our results suggest that Echinaforce® phytochemicals
strengthen antiviral innate immunity through tonic IFN regulation of
pattern recognition and chemokine gene expression and DNA repeat
hypermethylated silencing of HERVs in monocytes. These results suggest
that immunomodulation by Echinaforce® treatment holds promise to reduce
symptoms and duration of infection episodes of common cold corona
viruses (CoV), Severe Acute Respiratory Syndrome (SARS)-CoV, and new
occurring strains such as SARS-CoV-2, with strongly impaired interferon
(IFN) response and weak innate antiviral defense.
Supplementary Information
The online version contains supplementary material available at
10.1186/s12906-021-03310-5.
Background
Distinct species of the plant genus Echinacea have traditionally been
used in North America against infectious diseases and wounds [[35]1,
[36]2]. Currently, a wide variety of Echinacea preparations are used
world-wide as complementary herbal remedy to improve the immune
response to protect against common cold symptoms and influenza
infections. Of all Echinacea species, Echinacea purpurea (purple
coneflower) is the most popular variety used in Western countries.
Different Echinacea purpurea extracts (different species, plant parts,
manufacturing) or derived compounds showed antioxidant, antibacterial,
antifungal, antiviral and mosquitocidal activities in cell culture
experiments [[37]3], although absolute comparisons between studies with
different preparations remain difficult [[38]4, [39]5]. Complex
immunomodulatory actions of Echinacea have been described including
both pro- and anti-inflammatory effects [[40]2, [41]3, [42]6]. The
compounds that contribute to these activities are alkylamides,
glycoproteins, polysaccharides and caffeic acid derivates that may act
independently or in synergy [[43]1, [44]3, [45]7–[46]9].
In this study, we evaluated Echinaforce®, a commercially registered
herbal medicinal tincture of Echinacea purpurea (L.) Moench (A.Vogel
Bioforce, Switzerland) in several European countries including
Switzerland, Austria, UK, Spain, Netherland, Denmark, Finland, Sweden,
Slovenia, as well as Canada. The tincture contains 5% root extract and
95% herb extract following extraction with 65% ethanol V/V.
Echinaforce® phytochemicals reveal immune modulatory,
anti-inflammatory, anti-bacterial, anti-viral and anti-parasitic
activity [[47]9–[48]21]. Clinical efficacy could be shown with
different batches in acute treatment [[49]22] or for prevention
[[50]23] of respiratory tract infections. A 4-month randomized, double
blind, placebo-controlled study (n = 755 subjects, of which 376
received placebo) on the safety and efficacy of Echinaforce® to prevent
common cold symptoms, showed significantly less cold episodes and of
shorter duration as well as lower infection recurrence rate in the
Echinaforce® treated versus placebo treated group [[51]23]. Moreover,
no differences between placebo and Echinaforce® group were reported in
relation to health risk and safety [[52]23]. Despite the promising
immune potentiating properties of Echinaforce®, the responsible
molecular targets have only partially been identified, such as the
cannabinoid receptor 2 (CB2) [[53]7, [54]19, [55]24], the cAMP,
p38/MAPK and JNK signaling pathways, as well as NF-κB and ATF2/CREB1
transcription factors. To further clarify its mode of action, we
applied a system biology approach by integrating genomewide
transcriptome, epigenome and kinome signaling profiles of THP1
monocytes treated with Echinaforce®.
Methods
Cell lines and treatments
Echinaforce® (batch nr. 040070, A. Vogel Bioforce AG, Roggwil,
Switzerland) is a standardized preparation obtained by ethanol
extraction of freshly harvested Echinacea purpurea herb and roots
(95:5). The extract Echinaforce® itself is strictly produced under GMP
conditions and tested therefore on different levels (seed, plant,
extract, tablet, etc.) thoroughly in the same manner as an allopathic
remedy since it is a registered product in Europe. The plant has been
identified taxiconomically and also with a DNA test. The same seeds of
this plant have been used for more than 50 years (since 1955) in the
company A. Vogel to produce the standardized test item Echinaforce®.
This means that end of year a part of the Echinacea plant is used to
take the seeds in late autumn to use them for plantation next spring
then. Since the majority of the Echinacea cultivation is in the
vicinity of A. Vogel AG in Roggwil (Switzerland) no adulteration with
other plants takes place. According to Good Agricultural Practice, for
every batch used for for production of the standardized extract, the
plant species is visually verified by an expert before it is released
for production of the registered medicinal herbal extracts. The main
basis for releases of any batch of Echinaforce® is the HPLC
fingerprint, TLCs and a minimum amount of the alklymide tetraen as a
marker substance. The composition of marker compounds like alkylamides
(i.e. those compounds known to characterize this species of Echinacea)
was described previously [[56]3, [57]6, [58]25]. With this strategy, A.
Vogel can guarantee that every batch is similar in its constituents and
its activity profile. Extended research on pharmacological activity
with different batches have been carried out by the company showing
consistent activity in in vitro settings (antiviral, immunemodulatory
activity). In contrast to pressed juice extracts, Echinaforce® extract
does not contain polysaccharides which are known to stimulate the
immune system nonspecifically [[59]26–[60]29]. The alcohol
concentration of Echinaforce® tincture extract was 65% v/v and solvent
controls have been included in all experimental in vitro experiments to
rule out nonspecific effects. In addition, the preparation was free of
detectable endotoxin as determined by means of a commercial assay kit
with a lower limit of detection 0.1 unit/ml (Lonza Walkersville Inc.,
MD).
THP1 cells were grown in RPMI-1640 medium supplemented with glutamine,
10% heat inactivated Fetal Bovine Serum, 50 IU/mL Penicillin, 50 μg/mL
Streptomycin, 10 mM HEPES and 0.05 mM β-mercaptoethanol. Cells were
treated with 1% Echinaforce® tincture versus ethanol solvent control.
Each treatment condition consisted of six biological replicates.
Genome-wide gene expression analysis
Sample preparation and microarray processing
THP1 cells were treated for 48 h with 1% Echinaforce® or ethanol
solvent control. RNA was isolated using the RNeasy mini kit (Qiagen)
according to manufacturer’s instructions. RNA concentration and purity
was measured using the Nanodrop 1000 spectrophotometer (ThermoFischer,
CA, USA). RNA integrity of each sample was checked using using the
Experion Automated Electrophoresis System (Bio-Rad, MO, USA). Total RNA
(500 ng) was amplified using the Illumina TotalPrep RNA Amplification
kit (Life Technologies, Carlsbad, CA, USA). Briefly, RNA was reverse
transcribed using T7 oligo (dT) primers, after which biotinylated
complementary or anti-sense RNA (cRNA) was synthesized through an in
vitro transcription reaction. Then, 750 ng of amplified cRNA was
hybridized to a HumanHT12 beadchip array (Illumina, San Diego, CA, USA)
and further incubated for 18 h at 58 °C in a hybridization oven under
continuous rocking. After several consecutive washing steps, bead
intensities were read on an Illumina iScan. Microarray data and raw
gene expression intensities were preprocessed and analyzed using the
beadarray R package [[61]30]. Intensities were quantile normalized and
log[2] transformed. Raw and normalized array data were uploaded to the
Gene Expression Omnibus (GEO) database and have accession number:
[62]GSE117904. Probes with a P-detection value higher than 0.05 in at
least six samples were removed. Also, probes annotated as “bad” and “no
match” as described before [[63]31] were not kept for further analysis.
Differentially gene expression was performed using the limma R package
[[64]32]. P-values were corrected for multiple testing using the method
of Benjamini and Hochberg. Probes with a log2 fold change higher than
0.4 and an adjusted p-value less than 0.05 were defined as significant
and kept for further analysis [[65]33]. The probes were annotated with
gene information using the illuminaHumanv4.db annotation dataset
[[66]34]. The gene IDs of the significant Illumina expression probes
were uploaded into the IPA software (Ingenuity® Systems,
[67]www.ingenuity.com, Redwood City, CA, USA) to find enriched
biological pathways, diseases and networks [[68]35]. Fischer ‘s exact
test was used to calculate a p-value determining the probability that
each biological function and/or disease assigned to that data set is
due to chance alone. Metascape systems biology freeware
([69]https://metascape.org/) was used for correlating the
transcriptomic profile data [[70]36]. For each given gene list, pathway
and process enrichment analysis has been carried out with the following
ontology sources: KEGG Pathway, GO Biological Processes, Reactome Gene
Sets, Canonical Pathways, CORUM, TRRUST, DisGeNET, PaGenBase,
Transcription Factor Targets, WikiPathways, PANTHER Pathway and COVID.
All genes in the genome have been used as the enrichment background.
Terms with a p-value < 0.01, a minimum count of 3, and an enrichment
factor > 1.5 (the enrichment factor is the ratio between the observed
counts and the counts expected by chance) are collected and grouped
into clusters based on their membership similarities. More
specifically, p-values are calculated based on the accumulative
hypergeometric distribution, and q-values are calculated using the
Banjamini-Hochberg procedure to account for multiple testings. Kappa
scores are used as the similarity metric when performing hierachical
clustering on the enriched terms, and sub-trees with a similarity of
> 0.3 are considered a cluster. The most statistically significant term
within a cluster is chosen to represent the cluster. Heatmaps show
Metascape enrichment analysis of all statistically enriched ontology
terms (GO/KEGG terms, canonical pathways, hall mark gene sets).
Accumulative hypergeometric p-values and enrichment factors are
calculated and used for filtering. Remaining significant terms are then
hierarchically clustered into a tree dendrogram based on
Kappa-statistical similarities among their gene memberships. The term
with the best p-value are selected within each cluster as a
representative term to be displayed in a hierarchical tree dendrogram.
The heatmap cells are colored by their p-values (see color legend).
Along the same line, Metascape enrichment analysis of all statistically
enriched TF-target interaction networks is dermined by the TRRUST
database [[71]37]. Protein-protein interactions (PPI) among all input
gene lists are extracted from PPI data source to form a PPI network
(interactome). GO enrichment analysis is applied to the network to
assign biological “meanings” of sub-protein networks. GO enrichment
analysis is applied to each MCODE network to assign “meanings” to the
network component, where top three best p-value terms were retained.
MCODE components were identified from the merged network. Each MCODE
network is assigned a unique color. For each given gene list,
protein-protein interaction enrichment analysis has been carried out
with the following databases: STRING, BioGrid, OmniPath, InWeb_IM. Only
physical interactions in STRING (physical score > 0.132) and BioGrid
are used. The resultant network contains the subset of proteins that
form physical interactions with at least one other member in the list.
If the network contains between 3 and 500 proteins, the Molecular
Complex Detection (MCODE) algorithm [[72]38] has been applied to
identify densely connected network components. The MCODE networks
identified for individual gene lists have been gathered and are
summarized in the MCODE subnetwork figure. Pathway and process
enrichment analysis has been applied to each MCODE component
independently, and the three best-scoring terms by p-value have been
retained as the functional description of the corresponding MCODE
components. Coronascape is a Metascape data hub including public
available COVID-19 research related omics data sets. It includes more
than 200 processed gene lists for SARS-CoV-2 retrieved from more than
20 published studies. These gene lists were generated using several
omics technologies, including transcriptome (RNA-Seq and scRNASeq),
proteome, phosphoproteome, ubiquitome, and interactome, providing a
comprehensive picture of SARS-CoV-2 infection in various host cell and
tissue types.
Quantitative realtime PCR
To validate microarray data, THP1 cells were treated with 1%
Echinaforce® or Solvent for the indicated time-points (3, 6, 12, 24 and
48 h) in three independent experiments. The effect of JAK1 inhibition
was determined by treating the cells with 1 μM JAK1 inhibitor
Filgotinib (GLPG0634, Selleckchem) for 30 min before adding
Echinaforce®. Total RNA was isolated using the RNeasy mini kit (Qiagen,
Hilden, Germany) including a DNAse treatment step as suggested by the
manufacturer. Then 750 ng RNA was reverse transcribed into cDNA using
oligo dT (Invitrogen), M-MLV reverse transcriptase (Promega, Wisconsin
USA), 2.5 mM dNTPs and RNaseOUT (Invitrogen). Samples were incubated on
42 °C for 60 min and 75 °C for 15 min. For the HERV genes, cDNA
synthesis was performed using random primers (Invitrogen) and
incubation of the samples at 37 °C for 60 min and 75 °C for 15 min.
qPCR was performed using the GoTaq qPCR Master Mix (Promega, Wisconsin
USA) on a StepOnePlus Real-Time PCR machine (Applied Biosystems).
Following primers were used: MX1 forward primer
5′-GTTTCCGAAGTGGACATCGCA-3′, MX1 reverse primer
5′-CTGCACAGGTTGTTCTCAGC-3′ ([73]NM_001144925), IFITM1 forward primer
5′-CCAAGGTCCACCGTGATTAAC-3′, IFITM1 reverse primer
5′-ACCAGTTCAAGAAGAGGGTGTT-3′ ([74]NM_003641), STAT1 forward primer 5′-
CCATCCTTTGGTACAACATGC-3′, STAT1 reverse primer
5′-TGCACATGGTGGAGTCAGG-3′ ([75]NM_007315), IL8 forward primer
5′-GCTCTCTTGGCAGCCTTCCTGA-3′, IL8 reverse primer
5′-ACAATAATTTCTGTGTTGGCGC-3′ ([76]NM_000584), CXCL10 forward primer
5′-GAAAGCAGTTAGCAAGGAAAGGT-3′, CXLC10 reverse primer
5′-GACATATACTCCATGTAGGGAAGTGA-3′ ([77]NM_001565), ACTB forward primer
5′-CTGGAACGGTGAAGGTGACA-3′, and ACTB reverse primer 5′-
AAGGGACTTCCTGTAACAATGCA-3′ ([78]NM_001101). Primer sequences for HERVs
were derived from [[79]39]. Each sample was ran in triplicate and the
median Ct-values between each replicate group was selected. Ct-values
were normalized using ACTB housekeeping gene. The ddCt-values or log
fold changes (logFC) were calculated using the solvent control as
reference sample. A paired t-test t-test was used to determine the
significance of the differences between Echinaforce® and solvent
expression levels.
Kinase activity profiling
Sample preparation
THP1 cells were treated with 1% Echinaforce® or ethanol solvent control
for 15 min. Cell lysates were prepared according to manufacturer’s
instructions. In short, cells were washed twice with cold 1X PBS and
lysed with lysis buffer (1:100 dilution of Halt Phosphatase Inhibitor
Cocktail and Halt Protease Inhibitor Cocktail EDTA free in M-PER
Mammalian Extraction Buffer (ThermoFisher Scientific™, Rockford, USA)
at a ratio of 100 μl buffer per 1 × 10^6 cells. Lysates were then
incubated on ice for 15 min and centrifuged for 15 min at 16000 x g at
4 °C. Protein concentration was quantified using the Pierce BCA Protein
Assay Kit (ThermoFisher Scientific™, Rockford, USA).
Serine/threonine kinases (STK) and tyrosine kinase (PTK) pamgene assay and
data analysis
Kinase activity profiling was performed PamChip® preprocessing and
kinase activity profiling was performed according to manufacturer’s
instructions (PamGene International BV, ‘s-Hertogenbosch, The
Netherlands). The first part of the protocol consisted in the blocking
of the arrays with 2% BSA followed by several washing steps. Then
0.5 μg for STK and 5 μg for PTK assays together with the correspondent
reaction mixes (purchased from the Pamgene) were loaded onto the arrays
and incubated in the microarray system PamStation® 12 instrument
(PamGene International, Den Bosch, The Netherlands). In this step, the
ATP contained in the mix leads to the activation of the kinases in the
lysate which will result in the phosphorylation of the peptides on the
array. Peptide phosphorylation intensities are then detected with the
primary STK antibody mix and FITC-labeled antibody for STK assay and
with the FITC-labelled PTK antibody (PTK assay). Images are then taken
by the CCD camera in the PamStation®12 and processed by the
Bionavigator software. Peptide intensities data were log[2] transformed
and differences in phosphorylation between Echinaforce® treated and
control cultures were determined by using an univariate student t-test
analysis corrected for multiple testing using the Benjamini and
Hochberg method [[80]33].
To identify potentially activated or inhibited kinases we used the STK
or PTK Upstream Kinase analysis PamApp from the Bionavigator Software.
The analysis is based on “in silico predictions” for the upstream
kinases of phosphorylation sites in the human proteome that are
retrieved from the phosphoNET database [[81]40]. In short, a prediction
algorithm is derived from known interactions between kinases and
phosphorylation sites. The prediction algorithm is then used to predict
the strength of undocumented interactions. The Bionavigator application
uses PhosphoNet database to map putative kinases upstream of the
phospho-peptides (a kinase can have multiple possible phosphosites, and
a single site can be phosphorylated by different kinases). For each set
of peptides mapped to a specific kinase, a “difference statistics” is
calculated (=normalized kinase statistics) using following formula:
[MATH:
τ=1n∑i=1np¯i1<
mo>−p¯i2<
/mrow>si1
2+si2<
/mn>2 :MATH]
with
[MATH: p¯ij
:MATH]
and
[MATH: s¯ij
:MATH]
as the sample mean and variance of the intensity of peptide i in group
j, respectively, whereas n is the number of peptides linked with a
specific kinase. A positive kinase statistic means that the kinase is
activated, while a negative statistic means the kinase is inactivated
compared to the control group. The kinases are subsequently ranked
based on a specificity and significance score which are calculated
using permutation of the peptides and samples, respectively. Following
formula is used:
[MATH:
Q=−log10maxmM1
M :MATH]
, where m is the number of times out of M permutations that
|τ[p]| > |τ|, where τ[p] is the value of the difference statistic
obtained after permutation of the samples or peptides. The significance
score represents the magnitude of the change represented by the
normalized kinase statistic. The specificity score represents the
specificity of the of normalized kinase statistic in terms of the set
of peptides used for the corresponding kinase. The higher the score the
less likely it is that the observed normalized kinase statistics could
have been obtained using a random set of peptides from the data set.
The sum of the significance and specificity score is used to rank the
kinases [[82]41].
Genome-wide DNA methylation analysis
Sample preparation
THP1 cells were cultured for 48 h with 1% Echinaforce® or ethanol
solvent control. Corresponding cellular genomic DNA was isolated using
the DNeasy Blood & Tissue kit (Qiagen, Hilden, Germany) according to
manufacturer’s instructions. DNA concentration and purity was measured
using the Nanodrop 100 spectrophotomer and 1 μg of DNA was used for
bisulfite conversion using the EZ DNA methylation Kit of Zymo Research
according to manufacturer’s instructions. Successful bisulfite
conversion was checked using a methylation-specific PCR in a region of
the SALL3 gene (see [[83]42] for primer sequences).
EPIC DNA methylation array
The Infinium HumanMethylationEPIC BeadChip array (Illumina, San Diego,
CA, USA) was used to measure genome-wide DNA methylation. Four μL of
bisulfite-converted DNA from each sample was amplified, fragmented,
precipitated, resuspended and subsequently hybridized onto the
BeadChips. After overnight incubation of the BeadChips, unhybridized
fragments were washed away, while hybridized fragments were extended
using fluorescent nucleotide bases. Finally, the BeadChips were scanned
using the Illumina iScan system to obtain raw methylation intensities
of each probe.
EPIC DNA methylation data preprocessing and analysis
The R package RnBeads was used to preprocess the Illumina 450 K
methylation data [[84]43]. CpG-probes were filtered before
normalization based on following criteria: probes containing a SNP
within 3 bp of the analyzed CpG site, bad quality probes based on an
iterative greedycut algorithm with a detection p-value threshold of
0.01, and probes with missing values in at least one sample. After
filtering these CpG-probes, methylation values were within-array
normalized using the beta mixture quantile dilation (BMIQ) method
[[85]44]. Another filtering step was performed after normalization
based on following criteria: probes measuring methylation not at CpG
sites (CC, CAG, CAH, …) and probes on sex chromosomes.
The methylation beta-values were transformed to M-values
(M = log[2](β/(1-β))) prior to further analyses. The moderated t-test
incorporated in the limma R package [[86]32] was used to calculate the
statistics and p-values of the methylation differences between
Echinaforce®- and solvent-treated samples. Significant differentially
methylated probes (DMPs) were selected based on a false discovery rate
(FDR) < 0.1 and a difference in beta-value of at least 0.05. The DMPs
were annotated with gene information using the
IlluminaHumanMethylationEPICmanifest R package [[87]45]. Further gene
information was retrieved from the UCSC genome browser (human hg19).
Enrichment of genomic regions was calculated using the Fisher’s exact
test. Pathway analysis of the genes harboring a DMP was performed using
the Ingenuity Pathway Analysis (IPA) software. Raw and normalized array
data were uploaded to the Gene Expression Omnibus (GEO) database and
have accession number: [88]GSE117904.
Protein expression of MX1, STAT1 and IFITM1 proteins using western blotting
Protein expression levels of MX1, STAT1 and IFTIM1 were determined in
THP1 cells treated with 1% Echinaforce® or ethanol solvent control for
48 h, as explained before. Then, cells were washed and incubated 15 min
on ice in lysis buffer containing: 150 mM NaCl, 1 mM EGTA, 1 mM EDTA,
1 mM ß-glycerolphosphate, 1% Triton X-100 (w/v), 20 mM Tris HCl,
pH = 7.5 and proteinase inhibitor (Complete™, EDTA-free Protease
Inhibitor Cocktail, Sigma-Aldrich, USA) plus PhosphataseArrest™
Phosphatase Inhibitor Cocktail (phosphataseArrest™, G-Biosciences,
USA). Cells were subsequently centrifuged for 15 min at 200 g at 4 °C
and supernatant containing the soluble proteins were stored at − 20 °C
until use. Protein lysates (20 μg) were mixed with 5X sample buffer (5%
SDS, 20% glycerol, 0.2% bromophenol-blue, 250 mM DTT, 65 mM Tris HCl)
all purchased from Sigma Aldrich (Missouri, USA), heated for 5 min at
95 °C and loaded in a 12% SDS-PAGE gel. Proteins contained in the
homogenates were separated during 30 min at 60-70 V and 1 h at a
constant voltage of 130 V. Further, 10 μl of BenchMark™ Pre-Stained
Protein Standard (Life Technologies, CA, USA) was also loaded next to
the samples. After separation proteins ttransferred onto a
Nitrocellulose Membrane (BioRad, CA, USA) during 2 h at 45 V.
Non-specific binding sites were blocked by incubating the membranes
with blocking buffer (0.05% Tween 20, 1x TBS, 5% BSA) for 1 h at room
temperature. The membrane was then incubated with the primary
antibodies: MX1 (D3W7I) Rabbit mAb #37849, IFITM1 Antibody Rabbit pAb
#13126 and the STAT1 (42H3) Rabbit mAb #9175 (all purchased from Cell
Signaling Technology, Massachusetts, USA) or rabbit polyclonal
Anti-GAPDH antibody (ab9485, Abcam, Cambridge, UK) overnight at 4 °C.
After membranes were washed, they were incubated with (1:10000) Donkey
anti-Rabbit IgG (H + L) Secondary Antibody-HRP (Thermo Fisher
Scientific, Massachusetts, USA) for 1 h at room temperature.
Chemiluminiscence detection was performed using the ECL detection kit
(Pierce™ ECL Western Blotting Substrate (Thermo Fisher Scientific,
Massachusetts, USA) in a ChemiDoc MP system (BioRad, CA, USA).
Assessment of IFNα2, IFNβ IFNγ, CXCL8 (IL8) and CXCL10 levels
Cell culture supernatants were collected after 3, 6, 12, 24 and 48 h
and assayed for chemokines CXCL10 and IL8 by means of an enzyme-linked
immunosorbent assay (ELISA) purchased from Invitrogen (CA, USA)
following manufacturer’s instructions. The assays have a detection
limit of 2 pg/ml for CXCL10 and 5 pg/mL for IL-8. Similarly, protein
concentrations of IFNα2, IFNβ and IFNγ were measured in the same
culture supernatants using the highly sensitive U-PLEX Biomarker Group
1 (hu) Assay (Meso Scale Diagnostics, Maryland, USA) following
manufacturer’s instructions. The U-PLEX assays have a detection limit
of 4.0 pg/ml, 3.1 pg/mL and 1.7 pg/mL respectively for IFNα2, IFNβ and
IFNγ.
Results
Echinaforce® treatment triggers tonic IFN regulation of innate immunity
signaling pathways
Widespread gene expression changes in monocyte THP1 cells were detected
upon 48 h 1% Echinaforce® treatment. Based on significance criteria of
FDR < 0.05 and absolute log[2] fold change > 0.4, Echinaforce® induced
modest upregulation of 205 expression probes (173 genes) while 124
probes (99 genes) were downregulated compared with the ethanol treated
solvent controls (Fig. [89]1a and Supplementary Table [90]1). In
contrast to pharmacological drugs (for example glucocorticoids (GC))
which can trigger drastic expression changes of GC-responsive genes
(typically, log[2] fold > 1), many bioactive phytochemicals rather
induce moderate transcriptional changes (typically log[2] fold > 0,4)
of multiple genes converging on the same pathway [[91]46–[92]48]. Genes
differentially expressed (DEG) by Echinaforce® treatment were enriched
for IPA canonical pathways related to innate immune responses including
interferon signaling, interferon regulatory factor (IRF) activation and
the role of pattern recognition receptors, among others (Fig. [93]1b-c
and Supplementary Table [94]2). Interestingly, most of these pathways
were predicted to be activated, as can be seen from the highly positive
activation z-scores. Interferon (IFN)α/β and IFNγ both induce
IFN-stimulated gene (ISG) expression through Janus kinase
(JAK)-dependent phosphorylation of signal transducer and activator of
transcription factors (STAT)1 and STAT2 [[95]49–[96]54]. In line with
the latter reports, we could observe transcriptional activation of
various antiviral gatekeepers and interferon inducible proteins (i.e.
MX1, IFI6/27/35/44, IFITM1/2/3, IFIT1/2/3, ISG15/20, IRF7/9), including
various STAT1 target genes (Fig. [97]1c, Supplementary Table [98]1,
[99]2). Logically, pathways related to viral infection and replication
were predicted to be inhibited (activation z-score < − 2). Also
pathways involving cellular movement, migration, recruitment and
chemotaxis were predicted to be activated (activation z-scores > 2)
(Fig. [100]1d). Aside from ISGs, transcription of various chemokines
and receptors (CXCL10, CXCL8, CCL2, CCL5, and CXCR4) were also
increased. In full accordance, recruitment and adhesion of immune
cells, infection and immune related processes were found top ranked
enriched diseases and biological functions in IPA analysis
(Supplementary Table [101]3).
Fig. 1.
[102]Fig. 1
[103]Open in a new tab
Echinaforce® induced gene expression activates innate immunity pathways
a Volcano plot showing the upregulated genes (orange color, number of
probes: 205), and downregulated genes (blue color, number of probes:
124) upon treatment of THP1 cells for 48 h with Echinaforce® tincture
(1%). b Top enriched IPA canonical pathways. Bars are colored by
activation z-score. c IPA interferon signaling pathway with
Echinaforce®-induced upregulated genes colored in red and green,
respectively. d Top enriched IPA infectious diseases and IPA immune
trafficking disease and biological function. Bar charts are colored by
activation z-score
Complementary to IPA analysis, protein-protein-interaction enrichment
analysis of DEGs by STRING [[104]55] and Metascape [[105]56] algorithms
was performed. This revealed strong enrichment of protein-protein
interactions responding to a chemical stimulus, which triggers a
defensive antiviral innate immune response involving IFN, TLR, NOD,
RIG, cytokine, chemokine and NFκB signaling pathways (Fig. [106]2,
Supplementary Table [107]4). More particularly, Metascape MCODE
analysis identified 3 interconnected subnetworks in the antiviral
cytokine response: cellular response to interferon, regulation of
leukocyte chemotaxis and (mitochondrial) metabolism (Supplementary
Table [108]4).
Fig. 2.
[109]Fig. 2
[110]Open in a new tab
Protein-protein-interaction network analysis of Echinaforce® treatment
responsive genes. STRING based protein-protein-interaction network
analysis of differentially expressed genes of THP1 cells treated for
48 h with Echinaforce® tincture (1%) shows a strong network overlap of
the cellular response to a chemical stimulus (FDR 2,91 E-18) (blue
colored dots-pies), cellular defense to virus (FDR 5,54 E-19) (red
colored dots-pies) and innate immune cytokine response (FDR 2,23 E-18)
(yellow colored dots-pies) (see also, supplementary Table [111]4)
Next, different gene members of the IFN and chemotaxis innate immune
signaling pathway, responsive to Echinaforce® treatment in THP1 cells
(Fig. [112]3a) were selected for further evaluation of time dependent
expression changes: STAT1, MX1, IFITM1, IFNα2, IFNβ, IFNγ, CXCL8 and
CXCL10 mRNA and/or protein levels were measured in THP1 monocytes after
3 to 48 h Echinaforce® treatment by means of qPCR, ELISA, multiplex MSD
U-PLEX® immunoassay and/or Western immunoblotting assays. Induction of
STAT1 and the interferon-stimulated genes MX1 and IFITM1 expression
could clearly be confirmed, with maximal mRNA transcription levels
observed after 48 h treatment (Fig. [113]3b). Corresponding changes in
STAT1 protein expression levels could also be verified by Western
analysis (Fig. [114]3c), whereas antibodies failed to detect
significant amounts of MX1 and IFITM1 protein (data not shown). Whether
MX1 and IFITM1 protein expression has high turnover rates resulting in
low protein expression levels needs further investigation [[115]57,
[116]58]. For the chemokines IL8 and CXCL10, persistent gene induction
could be observed upon Echinaforce® treatment until 48 h, with peak
transcription levels after 3 h (Fig. [117]3b). Accordingly, time
dependent accumulation of both chemokines in the cell culture
supernatants could be detected in ELISA (Fig. [118]3d). Finally, in
line with background mRNA transcription levels, multiplex immunoassay
detection of supernatant levels of IFNα2, IFNβ, IFNγ protein only
showed low expression levels, which weakly increase after 48 h
Echinaforce® treatment Fig. [119]3e). However, in contrast to high
expression levels of IFN upon acute viral infection, very weak
expression levels of IFN in absence of infection also exert profound
immunological effects, in part through “tonic” homeostatic modulation
of various signaling intermediates which regulate diverse cytokines to
train immunity [[120]59–[121]61].
Fig. 3.
[122]Fig. 3
[123]Open in a new tab
Induction of innate immune response by Echinaforce®. A) transcriptome
gene expression changes IFN, innate immunity, chemokine, cytokine genes
(logFC) B) transcription levels of MX1, IFITM1, STAT1, CXCL8(IL8) and
CXCL10 genes at different time points, the bars represent the mean
logFC values + − SD compared to the solvent control. *: P ≤ 0.05, ** P:
≤ 0.01, *** P: ≤ 0.001 and **** P: ≤ 0.0001. C) Blots showing protein
levels of STAT1 and GAPDH (as reference protein) in 20 μg protein of
cell lysates after 48 h stimulation with solvent (Ethanol) or
Echinaforce®; Bars graph represents the density of each blot band for
STAT-1 relative to the band density of GAPDH (reference protein). Band
intensities were calculated using imageJ software. Statistical
differences between solvent and Echinacea treated samples were assayed
using a paired t-test where p value < 0.05 was considered statistically
significant. (***) means p value < 0.01, (*): P ≤ 0.05, (**): P ≤ 0.01,
(***): P ≤ 0.001 and (****): P ≤ 0.0001. D) Expression levels of IL8,
CXCL10, IFNα2, IFNβ, IFNγ chemokines assayed by ELISA and MSD-U-Plex
immunoassays in supernatants collected after Echinaforce® and solvent
(Ethanol) stimulation. (***) means p value < 0.01, (*): P ≤ 0.05, (**):
P ≤ 0.01, (***): P ≤ 0.001 and (****): P ≤ 0.0001, p-values after a
paired t-test where p value < 0.05 was considered statistically
significant
Echinaforce® treatment activates IFN and antiviral innate immune response
which is suppressed in severe SARS-CoV-2 patients
Coupled to Metascape analysis [[124]36], the Coronascape database
([125]https://metascape.org/COVID) provides quick access to numerous
published COVID-19 omics data sets, and a comprehensive system level
data analysis toolkit for data mining. Remarkably, upon comparison of
our Echinaforce® responsive gene signature in THP1 monocytes with
public available datasets of gene expression profiles of SARS-CoV2
patients, we observed a very strong overlap in enriched pathways
(P-value 10^− 48–10^− 61) related to IFN, cytokine and innate immune
signaling in patients with mild to severe symptoms [[126]62–[127]69]
(Fig. [128]4a). Of special note, whereas Echinaforce® treatment was
found to promote innate immunity via multiple IFN stimulated genes
(ISG), i.e. pattern recognition receptor genes and chemokines (our
results), severe SARS-Cov2 patients typically suffer from a strongly
impaired interferon (IFN) type I response and weak innate antiviral
defense (ISGs), associated with a persistent blood viral load and an
exacerbated inflammatory response [[129]64–[130]72]. Furthermore,
Metascape TRRUST analysis [[131]37] of all statistically enriched TF
binding motifs in differentially expressed genes in severe covid
patients, which can be modulated by Echinaforce treatment identified
key roles for NFκB, STAT and IRF family transcription factors (Fig.
[132]4b). Finally, Metascape Protein-protein interaction analysis of
Echinaforce regulated protein networks identified multiple antiviral
IFN and immune signaling networks disturbed in severe SARS-CoV2
patients (Fig. [133]4c, Supplementary Table [134]4), including an EBV
specific virus infection protein network. Remarkably, EBV reactivation
and increased EBV DNA load have recently been reported in severe
SARS-CoV2 patients with impaired lymphocyte subpopulation counts
[[135]73].
Fig. 4.
[136]Fig. 4
[137]Open in a new tab
Systems level metascape analysis of transcriptome profiles of
Echinaforce treated THP1 monocytes and blood PBMC samples of SARS-CoV2
patients. a Metascape enrichment analysis of statistically enriched
ontology terms (GO/KEGG terms, canonical pathways, hall mark gene
sets). b Metascape enrichment analysis of all statistically enriched
TF-target interaction networks c GO enrichment analysis of all
protein-protein interaction networks to assign biological function to
each MCODE sub-protein-networks
Echinaforce® treatment activates JAK1, NFκB and MAPK kinases
To identify most important upstream kinase pathways responsible for
gene expression changes in THP1 monocytes following Echinaforce®
treatment, we performed a Pamchip kinome activity profiling assay
[[138]41]. This peptide array approach allows characterization of
cellular serine/threonine or tyrosine kinome activity profiles
following on chip in vitro kinase reaction of 144 conserved kinase
consensus peptide motifs in presence of THP1 monocyte lysates left
untreated or following Echinaforce® treatment [[139]74–[140]77]. Using
the upstream kinase prediction tool of the Bionavigator PamGene
software, the qualitative and quantitative changes in phosphopeptide
chip intensities upon Echinaforce® treatment were translated into a
pattern of activated or inhibited upstream kinases (Fig. [141]5a and
Supplementary Table [142]5). In agreement with the transcriptional
activation of the IFN signaling pathway described above (Fig. [143]1c),
Pamchip kinome profiling [[144]41] revealed activation of the JAK1
kinase which is important in the phosphorylation of STAT kinases and
subsequently downstream regulation of IFN-stimulated genes.
Furthermore, in line with pathway analysis of transcriptome data, we
also identified activation of the tyrosine kinase TEC (Fig. [145]5b)
(Supplementary Table [146]2, [147]3, [148]4). Surprisingly, our
analysis did not detect significant activity changes of early IFN
kinases TBK1 and IKK [[149]78].
Fig. 5.
[150]Fig. 5
[151]Open in a new tab
Activation of JAK1 and MAPK kinases by Echinaforce®. a Kinome activity
profiling on THP1 cell lysates, following 15 min treatment with
Echinaforce® tincture (1%). Showing predicted upstream kinases. Bars
are colored by specificity score with red meaning the highest score.
The direction of the bars represents the normalized kinase statistics.
A positive kinase statistic means a higher activity in Echinaforce®
treated samples. b TEC signaling pathway as predicted by IPA software
showing the up- and down-regulated genes (colored in red and green,
respectively), after Echinaforce® treatment. Numbers under genes names
represent (from up to down): the log fold changes, p-values and
adjusted p-values after a paired t-test comparing gene expression in
cells stimulated with Echinaforce® and solvent (control). c)
IPA-enriched P38 MAPK and JNK pathways upstream regulators. Genes
colored in orange are predicted to be activated, while genes colored in
blue are predicted to be inhibited. Numbers under gene names represent
(from up to down): the log fold changes, p-values and adjusted p-values
after a paired t-test comparing gene expression in cells stimulated
with Echinaforce® and solvent (control). d Effect of JAK1 inhibition on
transcript expression of interferon pathway related genes. THP1 cells
were either treated during 48 h with the JAK1 inhibitor Filgotinib
alone or in combination with Echinaforce® (n = 7). Mean expression
LogFC change relative to solvent control is represented together with
95% confidence interval. *: P ≤ 0.05, ** P: ≤ 0.01, *** P: ≤ 0.001
Besides, we also identified various Echinaforce® activated kinases
belonging to the MAPK superfamily of kinases: p38 MAPK (MAPK11, − 12,
− 13, and − 14), JNK (MAPK8, − 9 and − 10) and ERK1 (Fig. [152]5c).
This upstream regulators are also predicted by IPA to control various
canonical pathways, including pattern recognition receptors in
recognition of bacteria and viruses, activation of IRF by cytosolic
pattern recognition receptors and role of MAPK signaling in the
pathogenesis of influenza among others.
To further verify crucial involvement of JAK kinase activation in
downstream gene expression effects upon Echinaforce® treatment, we
compared THP1 gene expression changes following Echinaforce® treatment
in presence or absence of the pharmacological JAK1 inhibitor
filgotinib. We found that filgotinib significantly suppresses the
Echinaforce® responsive genes MX1 and IFITM1, whereas STAT1, CXCL10 and
IL8 gene expression were less significantly suppressed (Fig. [153]5d).
Altogether, experiments with the JAK1 inhibitor filgotinib strenghten
our transcriptome and kinome data analysis, pointing to JAK1-specific
regulation of downstream gene expression changes in response to
Echinaforce® treatment.
Echinaforce® treatment elicits epigenetic changes in innate immunity gene
pathways
Epigenetics seems to be important in training immunity [[154]60,
[155]79] during monocyte differentiation and in the immunological
memory of macrophages [[156]80, [157]81]. Today, various bioactive
phytochemicals have been identified which modulate inflammation through
epigenetic reprogramming [[158]82, [159]83]. Different phytochemicals
and nutrients are known to change DNA methylation and histone
modifications by directly influencing epigenetic enzymes or by
interfering with the availability of the substrates/cofactors of these
enzymes [[160]84–[161]86]. To assess whether the Echinaforce® induced
changes in transcriptome profiles in THP1 cells are associated with DNA
methylation changes, we measured complementary changes in DNA
methylation profiles using the Illumina EPIC methylation array.
Significant DNA methylation changes were observed following 48 h
exposure to Echinaforce® (Fig. [162]6a and Supplementary Table [163]6).
Fig. 6.
[164]Fig. 6
[165]Open in a new tab
Echinaforce® treatment leads to global hypermethylation of CpG-poor
gene bodies. a Heatmap showing the methylation values of differentially
methylated probes upon treatment of THP1 cells for 48 h with
Echinaforce® tincture (1%). Solvent (EtOH) controls are colored in blue
and Echinaforce®-treated cells in orange. b Genomic enrichment of DMPs
in different genomic regions. c CpG probes located in genes of the
interferon signaling pathway which were differentially methylated
(FDR < 0.1). * P ≤ 0.05, ** P ≤ 0.01, *** P ≤ 0.001, **** P ≤ 0.0001. d
Starburst plot showing the genes both differentially expressed and
differentially methylated. Each CpG-probe was mapped to its
corresponding gene and the -log10(FDR) from the gene expression and DNA
methylation analysis is displayed. The –log10(FDR) values of genes or
CpG-probes with a negative LogFC or delta beta was multiplied by − 1
leading to positive values when logFC or delta beta was positive and
negative values when logFC or delta beta was negative. CpG-probe – gene
pairs which were differentially expressed (FDR < 0.05) and
differentially methylated (FDR < 0.1) were colored in blue. The
CpG-probe – gene pairs of which the absolute delta beta was higher than
0.05 and the absolute logFC higher than 0.4 were colored in red. e The
IPA canonical pathways which were both significantly enriched in the
gene expression and DNA methylation analysis
A total of 1875 CpG sites was found differentially methylated
(FDR < 0.1) with a methylation difference of at least 5%. Typically,
DNA methylation changes after short (24-72 h) exposure to
phytochemicals and nutrients are much smaller than cancer associated
DNA methylation changes in oncogenes or tumor suppressor genes which
accumulate for many years in response to the microenvironment [[166]48,
[167]87, [168]88]. However, similar DMR effects sizes and cutoff (< 5%)
were found to be biologically meaningful in various disease etiologies
[[169]42, [170]89, [171]90].
From the 1875 CpG sites identified, only 40 differentially methylated
positions (DMPs) were hypomethylated whereas 1835 DMPs were
hypermethylated. DMPs were mainly enriched in gene bodies, intergenic,
and CpG-poor regions, while depleted in CpG islands, promoter, and
enhancer regions (Fig. [172]6b). Only 1259 of the 1875 CpG-probes (67%)
were located in a gene or 1500 bp upstream of a gene. Similarly, DNA
methylation variation in the immune system was predominantly found at
at CpG islands (CGI) within gene bodies, which have the properties of
cell type-restricted promoters, but infrequently at annotated gene
promoters or CGI flanking sequences (CGI “shores”) [[173]91].
Subsequent IPA pathway enrichment analysis of the genes containing DMPs
revealed inflammation or immunological diseases among others
(Supplementary Table [174]6). Of particular interest, one of the top
enriched pathways (‘Superpathway of Inositol Phosphate Compounds’)
controls various epigenetic processes related to the interferon
response [[175]92–[176]94].
Since both gene expression and kinase profiling both revealed the
involvement of interferon signaling pathways, we also checked whether
methylation of IFN pathway genes was affected by Echinaforce®
treatment. Eight probes located in BCL2, JAK1, STAT1, PIAS1 and TAP1
did show an FDR < 0.1, with small methylation differences (between 1
and 3%) (Fig. [177]6c). Whether these small methylation changes are
sufficient to “train” the innate immune gene response needs further
investigation [[178]60, [179]61, [180]79].
Since most of the DMPs were located in intergenic regions and gene
bodies, only a small subset of genes containing a DMP also resulted in
a significant change in gene expression (Fig. [181]6d). Only seven
genes were both differentially methylated and expressed, based on the
significance criteria described above: i.e. Calsyntenin 2 (CLSTN2),
Enhancer Of Zeste 2 Polycomb Repressive Complex 2 Subunit (EZH2),
Growth arrest-specific protein (GAS)-7, neuron navigator (NAV)-3,
Thioredoxin Reductase (TXNRD)-1, Tryptophanyl-tRNA synthetase (WARS)
and Zinc Finger Transcription Factor (ZNF)-644. When using less
stringent significance criteria, leaving out the effect size cutoff
(logFC), 574 CpG site – gene pairs were found to be differentially
expressed and methylated. Upon further comparing canonical pathways
which are significantly enriched for both lists of differentially
expressed genes and the list of differentially methylated genes, we
identified 10 common biological processes (Fig. [182]6e). Remarkably,
common pathways include NF-κB signaling (NF-κB activation by viruses,
NF-κB signaling), MAPK signaling (LPS-stimulated MAPK signaling,
UVA-induced MAPK signaling), and immune responses (i.e. Role of pattern
recognition receptors in recognition of bacteria and viruses, Role of
NFAT in regulation of the immune response, phagosome formation, CD40
signaling, leukocyte extravasation signaling).
Echinaforce® treatment changes DNA repeat methylation and HERV transcription
levels
DNA repeats and transposons require hypermethylation to maintain
genomic instability and prevent transposition [[183]95–[184]99].
Interestingly, differentially methylated probes (DMPs) demonstrated a
considerable enrichment in LINE, SINE and LTR transposon repeats,
flanking endogenous retroviral sequences (HERVs) (Fig. [185]7a-b). This
DMPs decreased transcription of MER4D, MER57B1, MLT1C627, MLT2B4 HERVs
after 12 and 48 h Echinaforce® treatment, whereas MLT1B and MLT1C49
HERVs were only transiently repressed at 12 h (Fig. [186]7c). However,
it remains unclear whether innate immune signaling (IFN response,
chemotaxis, and immunometabolism) is driving HERV regulation or vice
versa to mediate viral protection.
Fig. 7.
[187]Fig. 7
[188]Open in a new tab
Echinaforce® treatment leads to global hypermethylation of intergenic
repeat elements. a Genomic enrichment of DMPs in different repeat
elements. b Global DNA methylation changes in different repeat
elements. c HERV qPCR gene expression. THP1 cells were with
Echinaforce® at 12 and 48 h (n = 3). Mean LogFC change relative to
solvent control is represented together with 95% confidence interval.
*: P ≤ 0.05, ** P: ≤ 0.01, *** P: ≤ 0.001 and **** P: ≤ 0.0001
Discussion
In this study, we applied for the first time a systems biology approach
to characterize a possible mode of action of a standardized medicinal
Echinacea purpurea (L.) Moench tincture Echinaforce®, which is widely
used as a herbal remedy against respiratory tract infections.
Microarray, QPCR, Western and multiplex immunoassays demonstrate that
treatment of THP1 monocyte cells with Echinaforce® phytochemicals
elicit time dependent gene expression changes in antiviral innate
immunity signaling networks, involving tonic IFN (MX1, IFNβ, IFNγ,
IFITM1, STAT1, STAT2) chemotaxis (IL8, CXCL10) and immunometabolic
(ISG15, PKM2, SQSTM1) signaling pathways.
Most cells express a set of membrane and cytoplasmic receptors to
detect viral RNA and DNA molecules: Pattern Recognition Receptors
(PRRs). These receptors control innate immune signaling to activate the
synthesis of interferons during a viral infection. In addition to
pathogens, autophagy, metabolic and chemical stress, DNA damage,
unfolded protein response, can also regulate innate immunity through
cell-autonomous responses. Either IFN-inducible or constitutive, these
processes aim to guarantee cell homeostasis or a biodefense mechanism
against (non-self) hazardous molecules [[189]100]. Of importance, these
distinct constitutive cell-autonomous responses appear to be
interconnected and can also be modulated by microbes, viruses and PRRs
[[190]101]. Our results suggest that Echinaforce® phytochemicals train
innate immunity pathways via activation of interferon and chemokine
gene expression. As such, secondary metabolite phytochemicals involved
in plant immunity may prime evolutionary conserved innate immune
responses across species [[191]102–[192]104].
For example, Echinaforce® treatment increases expression IFI27 and
IFITM1, which both play critical roles in antiviral immunity and
disease severity in respiratory disease [[193]105–[194]107]. Along the
same line, transcriptional upregulation of the protein kinase receptor
(PKR, EIF2AK2), a cytoplasmatic pattern-recognition receptor could be
observed. PKR is known to transduce RNA helicase (MDA5) dependent virus
signals for type I IFN induction [[195]108]. Interferon regulatory
factor 7 (IRF7) is another key protein found strongly upregulated.
Transcription factors IRF7 together with IRF3 regulate expression of
early type I IFN and other proteins involved in the innate antiviral
immune response (activation of IRF by cytosolic pattern recognition
receptors) [[196]109] (Supplementary Tables [197]1, [198]2, [199]3,
[200]4). Signal transduction via PKR occurs mainly via NFκB and MAPK
pathways (Role of PKR in Interferon induction and antiviral response)
[[201]110]. Another important intracellular pattern-recognition
receptor for viral RNA which was found to be upregulated by
Echinaforce® was the RNA helicase MDA5 (IFIH1) [[202]78]. Furthermore,
upregulation of the NF-κB subunits RelB and NFKB2/p52 was observed,
which can promote downstream production of innate immunity chemokines
(NF-κB activation by viruses, NF-κB signaling) [[203]111].
In line with our results showing activation of tonic IFN regulation of
innate immunity gene responses, antiviral effects against influenza
infection and activation of IFN pathways have also been demonstrated in
vivo following Echinaforce® tincture treatment [[204]10, [205]14,
[206]20]. Along the same line, Echinaforce® treatment holds promise to
reduce disease severity symptoms in SARS-CoV2 patients by strengthening
impaired IFN specific innate immune signaling [[207]64, [208]70,
[209]71]. Our in vitro results are also in line with observations in
human studies ex vivo/in vivo showing increased immunomodulating as
well as chemotactic neutrophil effects following Echinaforce® treatment
[[210]10, [211]22, [212]23, [213]112]. For example, the antiviral
ability of CXCL10 has been attributed to its chemoattractant effects
which promote recruitment of natural killer cells [[214]113–[215]116]
and neutrophils [[216]113–[217]116]. The latter illustrates that both
neutrophils and inflammatory monocytes are intertwined in the immune
system’s anti-viral response [[218]113–[219]116]. Similar results were
previously obtained in murine dendritic cells, illustrating that
Echinaforce® stimulates cell mobility and chemotaxis and alters
expression of cell adhesion and motility genes [[220]117]. Other
studies showed that Echinaforce® may reverse the chemokine induction of
virus-infected cells [[221]11, [222]118–[223]120]. Paradoxically,
Echinaforce® may induce cytokine and chemokine expression in uninfected
cells, but suppress their expression upon virus infection or LPS
stimulation [[224]29, [225]118–[226]120]. Similarly, Echinaforce®
increased the transcription of TNFα in human monocytes, but reduced the
LPS-stimulated TNF-α protein production [[227]19]. Although studies
suggest that this stimulatory effect may be the result of
bacterial-derived LPS and lipoproteins [[228]26–[229]29], our
Echinaforce® tincture contains no polysaccharides, neither endotoxins.
Altogether, the latter suggests that its immunomodulatory effects are
due to the active compounds present in the formulation [[230]12,
[231]19]. Similar activation of IFN innate immunity and viral
protection has been observed in presence of avocado and apple extract
[[232]121, [233]122]. Interestingly, in the latter case, effects were
attributed to oligomeric proanthocyanidins and lost with their
monomeric form [[234]122].
With respect to immunometabolism, mitochondrial metabolism shows a
remarkable sensitivity to chemokine and IFN signaling [[235]123,
[236]124]. For example, ISG15 is an interferon-stimulated,
ubiquitin-like protein which regulates mitochondrial homeostasis and
targets various proteins involved in catabolic autophagy metabolism in
the mitochondria (mitophagy) during infection [[237]125, [238]126].
Moreover, mitochondrial changes in immunometabolism (glycolysis, the
tricarboxylic acid (TCA) cycle, the pentose phosphate pathway, fatty
acid oxidation, fatty acid synthesis and amino acid metabolism)
strongly contribute in (re) shaping immunity and production of
neutrophil extracellular traps (NETs) [[239]127–[240]130].
Next, phosphopeptide based kinome activity analysis revealed
Echinaforce® specific activation of innate immunity and IFN signaling
via multiple kinases, including JAK1, TEC, p38 MAPK (MAPK11, − 12,
− 13, and − 14), JNK (MAPK8, − 9 and − 10) and ERK1 kinases [[241]131,
[242]132]. JNK-STAT1 signaling induces various IFN responsive genes
[[243]133]. Moreover, JAK1 dependent regulation of downstream IFN and
chemokine related gene expression after Echinaforce® treatment, could
be reversed with the specific pharmacological JAK1 inhibitor
filgotinib. TEC activation has important roles during innate immunity,
i.e. IFN signaling via phosphorylation of JAK1 and JAK2 [[244]131,
[245]132], TLR signaling [[246]134], assembly and activation of the
caspase-8 inflammasome [[247]135], macrophage survival [[248]136], IL8
production [[249]137], phagocytosis [[250]138], and NFκB signaling
[[251]139]. p38 MAPK activation is involved in RIG-I dependent IFN
signaling [[252]140]. Various studies confirm involvement of these
kinases in Echinacea biological action [[253]19, [254]117,
[255]141–[256]144]. Alkylamides in the Echinaforce® tincture were found
to be responsible for MAPK effects upon binding to CB2 receptors
leading to increased cAMP, P38/MAPK and JNK signaling, NFκB and
ATF-2/CREB-1 activation [[257]19]. Similarly, lipophilic extracts of
Echinacea promoted murine dendritic cell maturation and mobility via
the modulation of JNK, P38 MAPK and NFκB pathways [[258]117, [259]141,
[260]142]. Another study demonstrated a JAK-STAT1 dependent antiviral
response of Tripterygium wilfordii (Thunder of God Vine) via the
quinone methide triterpene celastrol [[261]145].
Finally, genomewide epigenetic analysis of DNA methylation changes
following Echinaforce® treatment revealed almost 2000 DMP, enriched for
immune disease and immunological pathways. Although the observed
methylation changes are relatively small after 72 h treatment,
cumulative effects can contribute in building an immune memory response
by priming chromatin to mount faster and higher innate immune
transcription upon re-stimulation of immune cells [[262]103]. Besides
the regulation of gene expression, DNA methylation is also involved in
regulating alternative splicing, intron retention or promote cryptic
transcription of non-annotated TSSs (TINATs) encoding immunogenic
peptides which might prime an antiviral innate immune response
[[263]146–[264]149]. As such, it appears that Echinaforce® treatment
predominantly promotes epigenetic changes in innate immunity gene
pathways and to a less extent of adaptive immunity responsive genes.
Besides, the higher global DNA hypermethylation observed after
Echinaforce® treatment in LINE, SINE and LTR transposon repeats
flanking endogenous retroviral sequences (HERVs), may be part
evolutionary conserved (epi) genomic protective response against
retrotransposition and viral infection [[265]150, [266]151]. Similarly,
IFN was shown to promote DNA methylation silencing of repeats and
noncoding RNAs [[267]39, [268]150, [269]152, [270]153]. Specific HERVs
have been proposed to establish a protective effect against exogenous
viral infections [[271]154]. HERVs can act as IFN-inducible enhancers
and have shaped the evolution of a transcriptional network underlying
the IFN response [[272]154–[273]157]. Of particular interest, the
MER41B family of ERV sequences contains a STAT1 binding site and
regulates expression of IFN-γ–responsive genes, such as absent in
melanoma 2 (AIM2), and IFI6 [[274]158, [275]159]. CRISPR-Cas9 deletion
of a subset of these HERV elements in the human genome impaired
expression of adjacent IFN-induced genes and revealed their involvement
in the regulation of essential immune functions, including activation
of the AIM2 inflammasome. Along the same line, DNA methylation
inhibitors trigger an IFN response through viral mimicry via
transcription of dsRNAs of repetitive elements from HERVs which can
activate RIG-I and MDA5 PRRs [[276]150, [277]151]. RNA transcripts of
HERVs can be reverse transcribed to generate ssDNA or expressed to
generate proteins with viral signatures, much like the
pathogen-associated molecular patterns of exogenous viruses, which
allows them to be detected by the innate immune system [[278]160,
[279]161]. In another example, silencing of the MLT1C49 HERV decreased
expression of CXCL10 and CCL2 chemokines [[280]162]. Finally,
transcriptional changes of MLT1B and MER4D HERV transcription and
innate immune signaling have also been described upon immunometabolic
mitochondrial changes in protein kinase (PK)-M2 activity, which were
counteracted by NFκB RelB [[281]163]. From these examples, it appears
that HERV regulatory sequences now constitute a dynamic reservoir of
IFN-inducible enhancers fueling genetic innovation in mammalian immune
defenses [[282]158, [283]164, [284]165].
Previous studies showed that Echinaforce®, besides its immunomodulating
activities is also very active as a virucidal agent against viruses
with membranes, i.e. HSV-1, respiratory syncytial virus, all tested
human and avian strains of influenza A virus, as well as influenza B
virus [[285]166]. Along the same line, Echinacea polyphenol quercetin
was found to inhibit the entry of HIV-luc/SARS pseudotyped virus into
Vero E6 cells [[286]167]. Similar protective effects could recently
also be observed in a reconstituted nasal epithelium cell culture
system by exposing Echinaforce®-treated respiratory epithelium to
droplets of HCoV-229E, SARS- or MERS-CoVs, imitating a natural
infection [[287]168]. In contrast Echinaforce® was found to be less
effective against intracellular virus replication [[288]168].
Consequently, virus already present within a cell could be refractory
to the inhibitory effect of Echinaforce®, but virus particles shed into
the extracellular fluids would be vulnerable. Therefore, the antiviral
actions of the Echinaforce® may especially manifest during initial
contact with the virus, i.e. at the inception of infection, and also
during transmission of virus from infected cells.
Conclusion
In conclusion, our systems biology approach revealed that Echinaforce®
phytochemicals trigger multiple antiviral innate immunity pathways,
involving tonic IFN signaling, activation of pattern recognition
receptors, chemotaxis, immunometabolism and DNA hypermethylation of
endogenous retroviral sequences. Further studies in preclinical
respiratory infection models and double blind placebo-controlled
intervention studies are needed to proof its prophylactic efficacy
against common cold corona viruses (CoV), Severe Acute Respiratory
Syndrome (SARS)-CoV, and new occurring strains such as SARS-CoV-2, with
strongly impaired interferon (IFN) type I response and weak innate
antiviral defense.
Supplementary Information
[289]12906_2021_3310_MOESM1_ESM.xlsx^ (57.1KB, xlsx)
Additional file 1: Supplementary Table 1: Differentially expressed
probes (FDR < 0.05 and logFC > 0.4) after Echinaforce® tincture
treatment.
[290]12906_2021_3310_MOESM2_ESM.xlsx^ (26.2KB, xlsx)
Additional file 2: Supplementary Table 2: Enriched Ingenuity canonical
pathways of differentially expressed genes after Echinaforce® tincture
treatment.
[291]12906_2021_3310_MOESM3_ESM.xlsx^ (17.9KB, xlsx)
Additional file 3: Supplementary Table 3: Ingenuity pathway enrichment
analysis of diseases and biological functions of differentially
expressed genes after Echinaforce® tincture treatment.
[292]12906_2021_3310_MOESM4_ESM.xlsx^ (23.3MB, xlsx)
Additional file 4: Supplementary Table 4: STRING
protein-protein-interaction plot and Metascape
protein-protein-interaction MCODE network enrichment analysis of
differentially expressed genes after Echinaforce® tincture treatment.
[293]12906_2021_3310_MOESM5_ESM.xlsx^ (10.9KB, xlsx)
Additional file 5: Supplementary Table 5: PamGene upstream kinase
analysis.
[294]12906_2021_3310_MOESM6_ESM.xlsx^ (247.2KB, xlsx)
Additional file 6: Supplementary Table 6: Differentially methylated
positions (FDR < 0.05 and |DeltaBetas| > 0.05).
[295]12906_2021_3310_MOESM7_ESM.xlsx^ (32.9KB, xlsx)
Additional file 7: Supplementary Table 7: Enriched Ingenuity canonical
pathways of differentially methylated genes after Echinaforce® tincture
treatment.
Acknowledgements