Abstract
The global outbreak of severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2) necessitates the rapid development of new therapies
against coronavirus disease 2019 (COVID-19) infection. Here, we present
the identification of 200 approved drugs, appropriate for repurposing
against COVID-19. We constructed a SARS-CoV-2–induced protein network,
based on disease signatures defined by COVID-19 multiomics datasets,
and cross-examined these pathways against approved drugs. This analysis
identified 200 drugs predicted to target SARS-CoV-2–induced pathways,
40 of which are already in COVID-19 clinical trials, testifying to the
validity of the approach. Using artificial neural network analysis, we
classified these 200 drugs into nine distinct pathways, within two
overarching mechanisms of action (MoAs): viral replication (126) and
immune response (74). Two drugs (proguanil and sulfasalazine)
implicated in viral replication were shown to inhibit replication in
cell assays. This unbiased and validated analysis opens new avenues for
the rapid repurposing of approved drugs into clinical trials.
INTRODUCTION
To date, most small-molecule and antibody approaches for treating
severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)–related
pathology are rightly rooted in repurposing and are focused on several
key virus or host targets or on pathways as points for therapeutic
intervention and treatment. This has been underpinned by the
unprecedented pace of scientific research to uncover the molecular
bases of virus structure and the mechanisms by which it gains access to
cells before replication and release of new virus particles. The
emergence of global proteomic datasets is now propelling our
understanding of the mechanisms through which the virus interacts with
host cell proteins, determining the directly interacting proteins
(DIPs) ([54]1) and differentially expressed proteins (DEPs) ([55]2).
Such interactome outputs and related efforts in transcriptomics ([56]3)
have begun to provide detailed information on possible individual
targets and pathways against which currently available drugs can be
tested for potential coronavirus disease 2019 (COVID-19) repurposing.
Systematic analyses of these datasets will direct further research
toward likely points of successful therapeutic intervention. In this
study, we have applied the power of bespoke computational biology and
machine learning approaches to dissect these datasets and construct an
agnostic network for SARS-CoV-2–induced pathways, uncovering novel
targets and potential repurposing strategies ([57]Fig. 1A). We have
focused our study on host-directed therapy, an emerging and
complementary approach to virus-targeting drugs, that interferes with
signaling mechanisms in the host cell to effectively inhibit the
productivity of viral replication ([58]4).
Fig. 1. Construction of a SIP network.
[59]Fig. 1
[60]Open in a new tab
(A) Overview and workflow of the in silico drug repurposing pipeline.
(B) Schematic depicts our strategy of constructing a SIP hidden network
through data integration and network construction of DIPs and DEPs,
followed by identification of drugs that target key pathways in this
network. (C) The SARS-CoV-2 Orf8 subnetwork shows the extent of the
hidden layer that is revealed through the network analysis. (D)
Percentage of the shortest paths between the DIP and DEP that are via
zero to three proteins at 6 hours versus 24 hours.
RESULTS
Construction of a SARS-CoV-2–induced protein network
To determine the disease mechanisms underlying a SARS-CoV-2 infection,
we undertook a comprehensive analysis of the protein pathways
implicated in COVID-19, using computational biology workflows for data
integration and network construction. To this end, we hypothesized that
DIPs are the “cause” and DEPs are the “consequence” of SARS-CoV-2
infection. We then constructed a SARS-CoV-2–induced protein (SIP)
network in which all DIP and DEP combinations are connected to
understand how the chains of the cause and consequences are connected
([61]Fig. 1B). We identified all the possible shortest paths between
332 DIP and 64 DEP combinations in the SIP network at 6 hours and
between 332 DIP and 164 DEP combinations in the SIP network at 24 hours
using the human STRING database ([62]5). In our SIP network, there are
three layers: the DIP, the DEP, and the hidden layer between the two.
Our analysis of the DEP data identified DIPs at 6 and 24 hours after
infection; hence, we constructed the SIP network for these two time
points. There are 13,308 proteins and 344,543 interactions in the
6-hour network and 14,827 proteins and 528,969 interactions in the
24-hour network [fig. S1 shows the entire SIP network at the two time
points; [63]Fig. 1C shows a subnetwork of SARS-CoV-2 Orf8 (Open reading
frame 8) at 24 hours]. Almost 99% of the DIP-to-DEP paths in both
networks are via more than one protein ([64]Fig. 1D), and there is only
a 2% overlap between DIP and DEP. It suggests that the “hidden layer”
that we have constructed in our network is central to understanding the
pathways that connect DIPs and DEPs and allows us to discover novel
relationships by integrating the datasets.
The SIP network can be interrogated to reveal key proteins and disease
pathways
In protein-protein interaction (PPI) networks, proteins that are
central to a pathway are desirable as druggable targets because they
may have a greater impact on pathway function. To identify key proteins
and disease pathways in the SIP network, we applied multiple network
algorithms [including eigenvector centrality, degree centrality,
betweenness centrality, and random walk with restart (RWR)]. To
identify statistically significant proteins, we performed 1000
permutation tests for each network algorithm and selected proteins with
empirical P values less than 0.01. The proteins selected by each
network algorithm were merged and considered as key proteins (see
Materials and Methods). This revealed 320 proteins at 6 hours and 394
proteins at 24 hours, of which 238 (50% of 476 proteins) proteins were
in common ([65]Fig. 2A). More than half of the proteins identified as
significant at both time points were in the hidden layer: 170 (53%) and
202 (51%), respectively (fig. S2, A and B). We then asked whether these
proteins were also biologically relevant to the disease symptoms caused
by COVID-19. A disease enrichment analysis on the proteins showed that
the top 10 enriched diseases for these proteins at both time points are
diseases that are potentially relevant for COVID-19 pathogenesis,
including lung disease ([66]6, [67]7), hypertension ([68]6, [69]7), and
hyperglycemia (table S1) ([70]6). To uncover potential biological
functions of the important proteins at 6 hours, 24 hours, and both time
points, we tested for enrichment of disease ([71]8) and Gene Ontology
(GO biological process) terms to characterize the key proteins in the
SIP network. For proteins at 6 hours and proteins that are common to
both time points, the pathways were related to the immune system and
virus replication (VR) (fig. S3, A and B). In contrast, the pathways
that were relevant for the proteins at 24 hours were primarily related
to VR (fig. S3C). In this way, we established a COVID-19 SIP network
that allows investigation of disease pathways that are pertinent to
SARS-CoV-2 infection.
Fig. 2. SARS-CoV-2 viral protein subnetwork analysis shows an enrichment of
viral replication pathways.
[72]Fig. 2
[73]Open in a new tab
(A) Venn diagram of key proteins in 6- and 24-hour SIP networks. (B) A
circos plot depicting interactions between DIPs and DEPs revealed
through the SIP network at 6 hours after infection. DIPs were
subdivided into the genomic organization of SARS-CoV-2. Proteins in the
hidden layer were also subdivided into major pathways. Inner colored
circles demonstrate the subcellular localization of the proteins, and
details are shown in the dotted box. The colored lines show PPI. (C)
Twenty-four hours after infection. (D) Top 30 enriched GO terms of the
key proteins in the SIP network at 24 hours (black). The enrichment P
values of 30 terms at 6 hours are also shown as a control (gray).
SARS-CoV-2 viral protein subnetwork analysis demonstrates an enrichment of
pathways related to viral replication
SARS-CoV-2 has a large RNA viral genome (~30,000 nucleotides) with
subgenomic structures that produce 29 viral proteins (4 structural
proteins, 16 nonstructural proteins, and 9 accessory factors of the
virus genome). To understand the disease mechanism of COVID-19, we
investigated the subnetwork for each of these viral proteins and asked
which biological processes these are implicated in. We analyzed several
parameters for key proteins in each subnetwork: (i) the differences
between the 6- and 24-hour time points ([74]Fig. 2, B and C, and table
S2); (ii) the subcellular localization of the key proteins (table S3);
and (iii) the biological processes that the key proteins act in (table
S4).
First, we found a significantly increased number of interactions with
RNA metabolism at 24 hours (1504 interactions at 6 hours but 6794 at 24
hours with a P value of 2.2 × 10^−16; [75]Fig. 2D and table S2). We
observe that the viral proteins N (Nucleocapsid), Nsp 8 (Nonstructural
protein 8), and Orf8 and Orf10 of SARS-CoV-2 interact with ribosomal
proteins in the hidden layer of our SIP network, indicating that they
may have a possible influence on RNA metabolism ([76]Fig. 2, B and C,
and table S2). The N and Nsp 8 proteins are known to drive viral
replication ([77]1). Orf8 and Orf10 are the only two proteins of
SARS-CoV-2 that are distinct from other coronaviruses ([78]9). We also
observed that Orf8-interacting DIPs were enriched in the endoplasmic
reticulum (ER) ([79]Fig. 2, B and C, and table S3), which may be
significant as the ER is the intracellular niche for viral replication
and assembly ([80]10). Of the 28 proteins that SARS-CoV-2 Orf8 directly
interacts with, 13 (46.43%, P value: 3.18 × 10^−6) are localized in the
ER, compared with only 11.84% (36) of all other DIPs (304) being
localized in ER.
We then sought the most relevant biological pathways—immune system and
viral replication—that have previously been described for SARS-CoV-2
([81]11) at the highest hierarchical level in the Reactome pathway
database. The “immune system” (P value: 9.57 × 10^−18) ([82]12) was
identified for the immune response (IR). The “metabolism of RNA” (P
value: 5.37 × 10^−45) ([83]12, [84]13) and “cell cycle” (P value: 1.73
× 10^−16) ([85]14) were found for viral replication. The key proteins
belonging to these three pathways were assigned to the three subgroups
(purple, metabolism of RNA; red, cell cycle; and light blue, immune
system) under the hidden layer in [86]Fig. 2 (B and C). The key
proteins that did not belong to any of the three pathways were assigned
to “others.” There were 54 key proteins in the hidden layer that did
not have strong enrichment in the Reactome pathways (other) but that
still actively interacted with metabolism of RNA proteins at 24 hours
([87]Fig. 2C and tables S2 and S4). Further study on the other proteins
found individual links to RNA binding (ATP5A1, MRTO4, and NHP2L1),
host-virus interaction (ACE2, CXCR4, DERL1, GNB2L1, HSPD1, KDR, KRT18,
SIRT1, and TMPRSS2), histones (H2AFZ, HIST2H3PS2, and WDTC1), viral
mRNA translation (MRPS7), and ER-associated responses (ATF4, CFTR,
DERL1, and INS).
We next confirmed statistically that virus-related pathways are
enriched in the top 30 enriched GO terms (P value less than 4.64 ×
10^−17) of 976 enriched GO terms (P value less than 0.05) as well as
RNA- and ER-related processes ([88]Fig. 2D; see fig. S4, A and B, for
the top 150 terms and table S4 for all enriched GO terms). The
differences between the two time points were also confirmed. In
summary, our pattern analysis in the SARS-CoV-2 viral protein
subnetworks revealed which biological pathways change significantly
during the course of infection, with prominent increases in proteins
involved in VR by 24 hours ([89]Fig. 2D).
An in silico network proximity analysis of drug-target relationships
identifies drug candidates
Having identified key SIP proteins, we were motivated to identify
approved drugs that bound a significant number of these host proteins
and which might therefore have stronger effects in blocking
SARS-CoV-2–induced changes. We conducted an in silico network-based
proximity measure analysis ([90]15) on the key proteins of the SIP
network at 6 and 24 hours after infection. We collected 1917 approved
drugs from publicly available databases [ChEMBL ([91]16) and DrugBank
([92]17); table S5]. This virtual screening identified 200 drugs (table
S6) that are predicted to target the key proteins of the SIP network,
of which 99 (49.5%) were specific to the 6-hour time point, 14 (7%)
were specific to the 24-hour time point, and 87 (43.5%) were common to
both time points. We then checked the Anatomical Therapeutic Chemical
code (available for 180 drugs only) to determine the therapeutic areas
for which specific drugs have been developed. The top clinical areas
against which these approved drugs are used for were cancer, sex
hormone signaling, diabetes, immune system, bacterial disease, and
inflammatory/rheumatic disease (fig. S5). A total of 35% of the 200
drugs have been tested in phase 2 or 3 clinical trials for infectious
diseases, and half of these were HIV trials; furthermore, 16% of drugs
have been tested in trials for inflammatory and 10% in respiratory
disease.
Among the 200 identified drugs, 40 (20%) are now in COVID-19 clinical
trials (tables S6 and S7) ([93]18). To determine the significance of
this finding, we asked what the likelihood would be of this number of
drugs being identified as hits by chance. We found that, by comparison,
only 13% of the approved drugs (249 of 1917) were in the COVID-19
clinical trials ([94]18). A hypergeometric test for the probability of
20% of our 200 drugs being in clinical trials returned a P value of
3.59 × 10^−3, demonstrating the utility of our integrated computational
approaches for prioritizing compounds. Of the 200 drugs identified, a
further total of 30 drugs have also been reported as being potential
candidates against COVID-19 ([95]19–[96]24). Thus, network-based
proximity analysis has revealed 70 drugs in total that are either in
COVID-19 clinical trials or being considered as potential drug
candidates in preclinical studies, supporting the strength of our
approach. In this way, our analysis has identified a total of 130 drugs
that could provide novel opportunities for repurposing as COVID-19
therapeutics. The full list of 200 approved drugs along with their
detailed information is shown in table S6.
Artificial neural network analysis uncovers drug mechanisms of action
We next wanted to establish the mechanism of action (MoA) underlying
the 200 identified drugs. In particular, we wanted to cluster the
pathways and mechanisms to better evaluate their potential effect and
utility. An initial pathway enrichment test performed on the proteins
that are targeted by the 200 drugs identified a set of 148 key pathways
(see Materials and Methods). We then calculated the precision and
recall of the enrichment test to produce an F1 score that is the
measure of the enrichment accuracy (see Materials and Methods). The F1
scores were calculated per drug-pathway association; in this way, we
generated an F1 score matrix (for the 200 drugs and 148 key pathways;
table S8). To investigate the MoA (that is, the profile of pathways in
which drug targets are significantly enriched) for the 200 drugs in the
context of COVID-19, we used a self-organizing map (SOM), a type of
artificial neural network, to analyze the relationship between the 200
drugs and the 148 key pathways (termed as drug-pathway association).
First, to characterize each of the 148 key pathways, the unsupervised
training of SOM with the F1 score matrix generated 148 SOM component
plane heatmaps (fig. S6). The SOM successfully predicted highly
correlated pathways, although only the F1 scores and no prior
biological knowledge of the 148 key pathway or the 200 drugs were used
in the SOM training. Each heatmap represents the intensity patterns of
a pathway, and each hexagon in the heatmap is a unique neuron or “node”
of the SOM artificial neural network. To allow direct comparison
between heatmaps (pathways), the hexagons (neurons) have the same
position across all heatmaps. In this way, a group of pathways are
correlated if their heatmaps are visually similar. For instance, three
heatmaps at the grid positions of A7, B7, and C7 in fig. S6 are
visually similar. The three heatmaps represent pathways for “G[1]-S
transition,” “G[2]-M checkpoints,” and “G[2]-M transition”; thus, they
are biologically correlated in cell cycle. To summarize the correlation
of 148 heatmaps, the unified distance matrix (U-matrix) between the
neighbor neurons was also calculated and presented in different colored
hexagons, which illustrates the probability density distribution of
data vectors (drug-pathway association score) ([97]Fig. 3A) ([98]25).
Fig. 3. Machine learning predicts MoAs for the 200 drug repurposing
candidates.
[99]Fig. 3
[100]Open in a new tab
(A) U-matrix is shown of the trained unsupervised SOM used to analyze
the relationship between the 200 drugs and the 148 key pathways. This
contains the distance (similarity) between the neighboring SOM neurons
(pathways) and shows data density (drug-pathway association scores) in
input space. Each hexagon is colored according to distance between
corresponding data vectors of neighbor neurons, with low-distance areas
(dark purple) indicating high data density (clusters). Each smaller
hexagon on the U-matrix (A) indicates the data vector distance between
larger hexagons in the SOM cluster arrangements (B to E). Thus, a
smaller hexagon on the U-matrix corresponds to every adjacent larger
hexagon on the SOM cluster arrangements (B to E). (B) The selected
clustering arrangement was based on the U-matrix and DBI to separate
the 148 key pathways into nine clusters. The names of nine clusters are
shown in the figure. Clusters of each SOM neuron are distinguishable by
color. The size of the black hexagon in each neuron indicates distance.
Larger hexagons have a low distance to neighboring neurons, hence
forming a stronger cluster with neighbors. (C) Two MoA categories were
identified on the basis of the pathway clustering and the drug mapping.
(D) Mapping of the 200 identified drugs to each neuron (pathway) based
on matching rates and inspection of examples from each cluster. (E) SOM
component map shows mapping results of the 200 drugs into nine pathway
clusters. The names of the nine clusters are shown in the figure, and
the drugs with asterisk are already in COVID-19 clinical trials.
Next, the 148 key pathways were separated into nine clusters by a
k-means clustering algorithm with Davies-Bouldin index (DBI). The nine
clusters were “metabolism of lipids,” “metabolism of protein,” “DNA
replication,” “G[2] or M cell cycle,” “hemostasis,” “metabolic
disorder,” “Toll-like receptor (TLR) or G protein–coupled receptor
(GPCR) signaling,” “receptor tyrosine kinase (RTK) signaling,” and
“cytokine signaling” (shown in different colors in [101]Fig. 3B). To
determine the optimal number of clusters, we calculated the DBI based
on the U-matrix. The lowest DBI value occurs at nine clusters (fig.
S7); thus, we decided to separate the 148 key pathways into nine
pathway clusters. The size of the black hexagon in each colored hexagon
indicates distance to its neighbor hexagon; thus, a larger black
hexagon indicates more correlation with its neighbor hexagons.
The nine pathway clusters were then mapped into potentially important
MoA categories for SARS-CoV-2 infection by pathway analysis (table S9).
To identify these categories, we first searched the COVID-19–related
literature and determined that there are mainly “two broad categories”
of disease mechanism reported: (i) IR and (ii) viral replication
([102]11). We then mapped the nine pathway clusters based on two
factors: (i) biological supporting evidence from the literature and
(ii) computationally inferred evidence from SOM clustering
arrangements. The detailed source of the biological supporting evidence
is shown in table S9. The computationally inferred evidence was
provided by the SOM clustering arrangements between the nine pathway
clusters ([103]Fig. 3B). For instance, RTK signaling is closely
positioned by two hallmark immune system pathways (cytokine signaling
and TLR/GPCR signaling) on the SOM clustering arrangements ([104]Fig.
3B). Thus, RTK signaling was predicted to have a high probability of
having a role in the IR. The mapping revealed two MoA categories that
could explain the mechanisms of the 200 identified drugs. The two MoA
categories were VR and IR ([105]Fig. 3C). For instance, 47 pathways
among the 148 key pathways are related to metabolism of lipids that
plays a key role at various stages in viral replication, including
entry, uncoating, genome replication, assembly, and release ([106]26).
There are 18 pathways related to DNA replication, and it is known that
intermediate and late viral mRNAs concentrate in DNA replication
factories ([107]27). We also found seven cytokine signaling pathways
that regulate the IR ([108]28). The entire mapping results and
supporting evidence are provided in table S9.
Last, the SOM mapped the 200 drugs into each neuron and hence the key
pathways (the number of drugs per neuron is shown in [109]Fig. 3D, and
drug names are shown in [110]Fig. 3E). Notably, 30 of the 40 drugs that
are in COVID-19 clinical trials ([111]18) were in the VR MoA category,
while only 10 drugs were in the IR ([112]Fig. 3D). We then identified
mechanistic roles and connections for the 200 drugs and their target
proteins and mapped the drugs into nine pathway clusters ([113]Fig.
3E). A more extensive analysis of information about each drug is given
in table S6.
Proguanil and sulfasalazine reduce SARS-CoV-2 replication
We next sought to identify the precise proteins within the SIP network
that are targeted by each of the 200 drugs. We found that, of the 1573
proteins targeted by the 200 drugs, most (66%) are targeted by a single
drug (fig. S8A). However, there are 30 proteins (0.19%) that are
targeted by eight or more drugs (P value less than 0.00757; fig. S8A).
To establish whether there is a pathway relationship between these 30
proteins, we interrogated their molecular function. Figure S8B shows
that the most enriched categories of function for these proteins were
heme, microsome, oxidoreductase, and monooxygenase, all of which are
related to nicotinamide adenine dinucleotide phosphate (NADP) and
nitric oxide (NO) synthesis. As NO is important for viral synthesis
(and because NADP affects NO production), this could provide a
potential mechanism by which these drugs might alter viral infection
([114]29–[115]31). On the basis of these findings, we decided to
validate, in cellular assays, five drugs (ademetionine, alogliptin,
flucytosine, proguanil, and sulfasalazine) with good safety profiles
that are functioning within this pathway. Compounds targeting the same
pathway but with serious safety issues were not progressed for cellular
validation.
To assess whether these five drugs are able to reduce SARS-CoV-2
infection, we performed an initial screening using the monkey Vero E6
cell line, where we observed that two of the five drugs, namely
proguanil and sulfasalazine, showed significant antiviral effects
without any noticeable cellular toxicity at the indicated doses
([116]Fig. 4A and fig. S9A). We then focused on these two drugs,
expanding our validation using the human Calu-3 cell line (in addition
to Vero E6 cells). Treatment of Vero E6 and Calu-3 cells with proguanil
and sulfasalazine illustrated strong anti–SARS-CoV-2 effects
(represented by reductions of the envelope and nucleocapsid gene RNAs)
in a dose-dependent manner, mirroring the results of the initial screen
([117]Fig. 4, B to E, and fig. S9, B to E). No significant effect on
cellular viability was observed at any tested dose (fig. S9, F to H).
The effective concentration of sulfasalazine is comparable to maximal
plasma concentrations achieved routinely in patients with rheumatoid
arthritis or inflammatory bowel disease ([118]32).
Fig. 4. Proguanil and sulfasalazine reduce SARS-CoV-2 replication and
p38/MAPK signaling activity.
[119]Fig. 4
[120]Open in a new tab
(A) RT-qPCR analysis of the indicated mRNA (envelope, E-protein) from
Vero E6 cells pretreated with the indicated drugs and concentrations
for 3 hours before infection with SARS-CoV-2 for 24 hours. Student’s t
test. Means + SD of three independent replicates are shown. (B and C)
RT-qPCR analysis of indicated mRNA (envelope, E-protein) from Vero E6
cells pretreated with proguanil or sulfasalazine at indicated
concentrations for 3 hours before infection with SARS-CoV-2 for 24
hours. Student’s t test. Means + SD of three independent replicates are
shown. (D and E) RT-qPCR analysis of indicated mRNA (envelope,
E-protein) from Calu-3 cells pretreated with proguanil or sulfasalazine
at indicated concentrations for 3 hours before infection with
SARS-CoV-2 for 24 hours. Student’s t test. Means + SD of three
independent replicates are shown. (F) Western blot analysis of
phosphorylated MAPKAPK2 (Thr^334) in mock-, DMSO-, sulfasalazine-, or
proguanil-treated Vero E6 cells at indicated concentrations for 3 hours
before infection with SARS-CoV-2 for 24 hours. (G to J) RT-qPCR
analysis of the indicated mRNAs from Calu-3 cells pretreated with
proguanil or sulfasalazine at indicated concentrations for 3 hours
before infection with SARS-CoV-2 for 24 hours. Student’s t test. Means
+ SD of three independent replicates are shown.
To further demonstrate the anti–SARS-CoV-2 impact of these two drugs,
we examined the status of recently found intracellular pathways
directly associated with SARS-CoV-2 infection and cytokine production
([121]33). Treatment with either proguanil or sulfasalazine
significantly reduced the phosphorylation of MAPKAPK2 (p-MK2 and T334)
([122]Fig. 4F), an important component of the p38/mitogen-activated
protein kinase (MAPK) signaling pathway, which has been shown to be
activated via SARS-CoV-2 infection and stimulate cytokine response
([123]33). Treatment of Calu-3 and Vero E6 cell lines with proguanil
and sulfasalazine led to a significant down-regulation of the mRNA of
key cytokines ([124]Fig. 4, G to J, and fig. S10), which are dictated
by the p38/MAPK signaling pathway and shown to become elevated during
SARS-CoV-2 infection and replication (CXCL3, IFNB1, and TNF-A)
([125]33). Hence, the above results solidify the promising
anti–SARS-CoV-2 effects of the two drugs, both at the viral and the
molecular level.
DISCUSSION
Here, we have used a series of computational approaches—including
bespoke methods for data integration, network analysis, computer
simulation, and machine learning—to identify novel SARS-CoV-2–induced
pathways that could be targeted therapeutically by repurposing existing
and approved drugs ([126]Fig. 1A). Although network analysis is
increasingly being used for the analysis of genetic datasets to uncover
disease signatures ([127]34), a few key aspects of our approach were
essential in uncovering these new targets, including agnostic
construction of the SIP network and application of novel algorithms
(previously used in other industries including social media). In
addition, the use of artificial neural networks to understand
systematically the MoA for the drugs was vital to this investigation.
Our analysis identifies 200 approved drugs, along with their MoA, that
may be effective against COVID-19 (table S6). We are confident that
these drugs have a potential for repurposing for COVID-19, since 40 of
the 200 drugs have already entered clinical trials, testifying to the
discovery value of our approach. An important part of our analysis is
the use of already approved drugs. This allows for the rapid
advancement of the most promising of the 160 drugs that are not yet in
clinical trials.
We identify two drugs, sulfasalazine and proguanil, that can reduce
SARS-CoV-2 viral replication in cellular assays, raising the exciting
possibility of their potential use in prophylaxis or treatment against
COVID-19. To understand why sulfasalazine and proguanil are effective
against SARS-CoV-2 infection but others functioning in the same pathway
were not ([128]Fig. 4A), we looked more closely at the targets of each
drug. [129]Figure 5 shows that SARS-CoV-2 Orf8 binds to γ-glutamyl
hydrolase (GGH) and regulates the synthesis of NO, which is necessary
for viral synthesis. An additional auxiliary pathway, mediating the
synthesis of NADP, can also affect NO production, although indirectly.
Sulfasalazine and proguanil impinge on both of these pathways:
Sulfasalazine targets the NF-κB inhibitors NFKBIA and IKBKB as well as
CYP450 enzymes, whereas proguanil targets DHFR and CYP450 enzymes plus
interacting partners (table S6). In this way, we hypothesize that these
two drugs might more effectively target NO production and thus disrupt
viral replication. By contrast, the three drugs that were not effective
against SARS-CoV-2 infection (flucytosine, alogliptin, and
ademetionine) only affect one of the two pathways. This analysis
thereby highlights the possibility that targeting NO production through
multiple pathways may provide a potential rationale for the efficacy of
sulfasalazine and proguanil in reducing viral replication.
Fig. 5. Schematics depicting the pathways mediating NO production that are
targeted by the five tested drugs.
[130]Fig. 5
[131]Open in a new tab
The black boxes indicate key proteins in SIP network, and those
targeted by the five drugs are highlighted in red color. Sulfasalazine
and proguanil target proteins in both pathways that directly and
indirectly (via NADP production) affect NO production
([132]58–[133]61).
Safety is a particularly important consideration, since such drugs
could be prescribed to any COVID-19–positive individuals who may have a
broader range of underlying medical conditions and may not be
hospitalized at the time of taking the drug. Sulfasalazine and
proguanil have the potential to be used prophylactically or
therapeutically. Both drugs are well-established and well-tolerated
drugs ([134]35, [135]36). Sulfasalazine is already in use as an
anti-inflammatory drug against autoimmune disorders. Given that this
drug has antiviral activity ([136]Fig. 4), this raises the possibility
that sulfasalazine may act not only as an antiviral but also as an
anti-inflammatory if used against COVID-19. Proguanil is used against
malaria in combination with atovaquone. It has an excellent safety
profile and is well tolerated when used as a prophylactic and in
treatment ([137]37).
A complementary study using large-scale compound screening in cultured
cells has recently uncovered 100 molecules that have a partial effect
on viral infectivity, 21 of which show a dose-dependent reduction of
viral replication ([138]38). This list of drugs does not overlap with
ours, with only 2 of our 200 approved drugs being present in this list
(and neither sulfasalazine nor proguanil being among them). The main
reason for this apparent disparity is that only 10% of the 100
compounds tested by Riva et al. ([139]38) are approved, whereas 100% of
our 200 drugs are approved. Eight drugs in the study by Riva et al.
([140]38) that were approved by the U.S. Food and Drug Administration
(FDA) are acitretin, astemizole (now withdrawn), chloroquine,
clofazimine, ingenol mebutate, remdesivir, tazarotene, and tretinoin.
Two others are approved only in China (flumatinib mesylate) or Japan
(tamibarotene). This highlights the major difference in the two
studies: Our in silico studies identify potential antiviral drugs that
are already approved and therefore at an advanced stage of repurposing,
whereas Riva et al. ([141]38) have identified compounds validated in
African green monkey cells VeroE6, most of which are either in
preclinical or phase 1 to 3 clinical trials. Gordon et al. ([142]1)
introduced 69 drugs (29 of which are approved by the FDA, 12 of which
are in clinical trials, and 28 of which are preclinical compounds) that
bound DIP. Among the 29 approved drugs, 9 drugs (captopril,
chloroquine, daunorubicin, indomethacin, loratadine, lovastatin,
metformin, mycophenolic acid, and sirolimus) overlap with our 200
identified drugs, and 8 of these are currently in clinical trials for
SARS-CoV-2.
Computational studies aiming to identify candidate drugs for COVID-19
drug repurposing have used multistage analyses including network
proximity measure analysis that are focused on DIP specifically and its
interactomes ([143]39–[144]41). By contrast, our strategy has been to
holistically construct the entire pathway of proteins that are
significantly affected during SARS-CoV-2 infection, through uncovering
of the hidden layer between the DIP and DEP. Because the DIP and DEP
were identified from two recent papers ([145]1, [146]2) that generated
proteomic data in two different cell lines (DIP in human embryonic
kidney 293 cells and DEP in Caco-2), we also used four different
network algorithms to systematically identify the key proteins (see
Materials and Methods). Furthermore, our approach not only identified
the 200 drugs but also used neural network analysis to predict the MoA
of the drugs. This combination of unique approaches allowed us to
short-list drugs associated with VR, which were then experimentally
tested in monkey cell VeroE6 and human Calu-3 cells. However, similar
to other network analysis studies, PPI networks usually lack the
directionality that provides additional information about the types of
interaction (i.e., activation or inhibition). It will be beneficial to
analyze additional data that provide insights into this directionality
(i.e., CRISPRi datasets showing patterns of up-/down-regulation) to
overcome this limitation.
Our study has shed unanticipated new light on COVID-19 disease
mechanisms and has generated promising drug repurposing opportunities
for prophylaxis and treatment. Our data-driven unsupervised approach
and biological validation have uncovered 160 approved drugs not
currently in clinical trials, which can be investigated immediately for
repurposing, and 2 drugs that show promise as antiviral drugs. We
expect that this resource of potential drugs will facilitate and
accelerate the development of therapeutics against COVID-19.
Furthermore, our bespoke data-driven computational approach should be
useful for a rapid response to new variants of SARS-CoV-2 and other new
pathogens that could drive future pandemics and will also be applicable
to other noninfectious disease areas with high unmet medical need.
MATERIALS AND METHODS
Directly interacting proteins and differentially expressed proteins
A total of 332 high-confidence SARS-CoV-2–human interactions were
obtained from Gordon et al. ([147]1) (table S3 from
[148]https://doi.org/10.1038/s41586-020-2286-9). A total of 332
high-confidence virus-host interactions were used as DIP. Data of
proteome measurements by mass spectrometry at 6 and 24 hours after
SARS-CoV-2 infection were obtained from Bojkova et al. ([149]2) (table
S2 from [150]https://doi.org/10.1038/s41586-020-2332-7). The proteins
that were significantly up- or down-regulated (two-sided, unpaired
Student’s t test with equal variance assumed, P < 0.05, |log[2]FC| >
0.5) were selected.
SIP network construction
The SIP network was constructed of all the shortest paths between DIP
and DEP in a human PPI network from the STRING database (v11.0)
([151]5). The main purpose of constructing the SIP network in our study
was to identify COVID-19 disease–associated proteins. The STRING
database was selected as the PPI database given the previous evidence
that it contains more comprehensive information on diverse collections
of disease-associated protein sets compared with other databases
([152]42).
Only interactions with a confidence score of more than medium (0.4)
were used. The 0.4 cutoff is the default setting and the medium level
of confidence for PPI searches in the STRING database ([153]43,
[154]44). This study used network algorithms to identify key proteins
by investigating the whole network. Thus, the cutoff was used to
construct a more comprehensive network that captures any potential
interactions, and then the network analysis was conducted to
systematically identify key proteins by analyzing all these possible
interactions. The STRING database does not provide directional
information.
All of the shortest paths between all pair proteins of DIP and DEP on
the human PPI network were found using Dijkstra algorithm. For the
shortest path finding, we used the Python package NetworkX (v2.2)
([155]45). Networks were visualized using Gephi 0.9.2 (fig. S1)
([156]46).
Network analysis
Eigenvector centrality, degree centrality, betweenness centrality, and
RWR were used to identify key proteins in SIP networks. The SIP network
is represented by an adjacency matrix A, where A[ij] = 1 if there is an
edge between nodes i and j or A[ij] = 0 otherwise. The eigenvector
centrality x[i] was defined as
[MATH: λx=xA :MATH]
(1)
where x is an eigenvector of the adjacency matrix A with eigenvalue λ.
If λ is the largest eigenvalue of the adjacency matrix A, there is a
unique solution x, and all centrality values are positive ([157]47).
Degree centrality of node i was defined as
[MATH: CD(i)=∑j=1N<
/mi>Aij :MATH]
(2)
where N is the number of nodes in the SIP network. Betweenness
centrality of a node i was defined as
[MATH: CB(i)=∑s,t∈Vσ(s,t∣i)σ(s,t) :MATH]
(3)
where V is the set of nodes, σ(s, t) is the total number of shortest
paths between s and t, and σ(s, t∣i) is the number of number of the
shortest paths between s and t paths passing through node i. If s = t,
σ(s, t) = 1, and if i ∈ s, t, σ(s, t∣i) = 0.
Eigenvector centrality was used to identify the most influential
proteins in the network. If a protein is frequently interacted by other
proteins, which also have high eigenvector centrality, then the protein
will have high eigenvector centrality. Degree centrality was used to
identify the hub proteins in the network. Betweenness centrality was
used to identify the bottleneck proteins in the network. The
betweenness centrality algorithm finds the number of the shortest paths
that pass through the given protein among all protein pairs in the SIP
network. RWR was used to see which human proteins were affected the
most upon SARS-CoV-2 infection. To do this, we used 332 DIPs as the
starting points of RWR. The RWR parameters were (i) a restart
probability that is 0.15, (ii) a maximum iteration number that is 100,
and (iii) an error tolerance of 1 × 10^−6. We have assigned edge
betweenness centrality as an edge score on the SIP network. The RWR
calculated a score per protein in the SIP network that indicates how
much a given protein was influenced by SARS-CoV-2 via DIP. The
algorithms were implemented in the Python package NetworkX (v2.2)
([158]45).
Permutation tests were performed 1000 times to identify significant
proteins for each of the network centrality algorithms. In 1000
permutation tests, each test generated a random network with a
preserved degree distribution of the original network, the SIP network.
To generate a random network, we reconnected the edge in the SIP
network and swiped the node. The random network in each permutation
test therefore has at least 66% of the rewired edges. In the
permutation test, we then applied the network algorithm and obtained
the cumulative results of the network algorithm. These cumulative
results were used to calculate the empirical P value of the network
algorithm. We combined the four permutation test results to determine
the final set of key proteins that have an empirical P value of ≤0.01
in either result.
Key protein functional enrichment analysis
Key proteins of SIP network were tested for enrichment of DISEASES
([159]8) and GO (GO biological process) terms. Enrichment analyses were
performed using REST API of Enrichr
([160]https://maayanlab.cloud/Enrichr/) ([161]48).
Visualization of a key network of SIP network
Key networks were built using interactions between the key proteins of
the SIP network at 6 and 24 hours after infection. When visualizing the
key networks, subcellular localization of key proteins and enriched
pathways of hidden layer proteins was added ([162]Fig. 2, B and C).
Subcellular localization information for key proteins was found using
COMPARTMENT database ([163]49). Among the available datasets in the
COMPARTMENT database, “knowledge channel” data with a confidence score
of greater than four was used. The knowledge channel for humans is
based on the annotations of UniProtKB, manually curated data. The
confidence score of four is the highest confidence score of the
knowledge channel and is only applicable to data with experimental
results. To identify enriched functions of the hidden layer proteins,
the hidden layer proteins were tested for enrichment of Reactome
pathway terms. Most hidden layer proteins belonged to the pathways
metabolism of RNA, cell cycle, and immune system, so we subdivided the
hidden layer proteins into three subgroups for key network
visualization. The visualization was carried out using Circos
([164]50).
Drug-target interactions
Approved drugs were collected from ChEMBL ([165]16) and DrugBank
([166]17). Drug-target interaction information was collected from
DrugBank (v5.1) ([167]17), STITCH (v5.0, confidence score > 0.9)
([168]51), and Cheng et al. ([169]52).
In silico network-based proximity analysis
In silico network-based proximity analysis was conducted for key
proteins from the SIP network at 6 and 24 hours. Given K, the set of
key proteins from SIP networks, and T, the set of drug targets, the
network proximity([170]Eq. 4) of K with the target set of T of each
approved drug where d(k, t), the shortest path length between nodes k ∈
K and t ∈ T in the human PPIs ([171]52), was executed. The closest
distance measure was used to calculate the distance between a given
drug’s targets to our key proteins in the SIP network because it showed
the best performance in drug-disease pair prediction in the study of
Guney et al. ([172]15)
[MATH: dc(K,T)=1‖T‖∑t∈Tmink∈K<
/msub>d(k,t) :MATH]
(4)
To assess the significance of the distance between a key protein of SIP
network and a drug d[c](K, T), the distance was converted to z score
based on permutation tests by using
[MATH: z(K,T)=d(K,T)−μd(K,T)σd(K,T) :MATH]
(5)
The permutation tests were repeated 1000 times, each time with two
randomly selected gene sets. There are few high-degree nodes due to the
scale-free network of the human PPI network. To avoid repetitive
selection of the same high-degree nodes during random selection, we
used a binning approach with at least 100 nodes in a bin. In the
binning approach, nodes in the same bin have similar node degree to
maintain node degree distribution for random selection. When we
randomly select a set of genes, we performed a random selection among
proteins from all bins so that the minimum node degree was less than
the minimum node degree of the selected gene set and the maximum node
degree was greater than the maximum node degree of the selected gene
set. The corresponding P value was calculated on the basis of the
permutation test results. Drug–to–SARS-CoV-2 associations with a z
score of less than −2 were considered significantly proximal ([173]15).
Drug-pathway associations
To understand the MoAs for our 200 identified drugs, we conducted the
Reactome pathway enrichment analysis for the target proteins of these
drugs using R (v3.5.2) package, gprofiler2 (hypergeometric test, P
value of <0.05) ([174]53). Reactome pathway database (the version as of
15 May 2020) was used for pathway enrichment analysis because it is the
most actively updated public database of human pathways ([175]54).
Pathway enrichment analysis was first performed using only the target
proteins of each of the 200 drugs. However, 120 of 200 drugs did not
have significantly enriched pathways because these drugs had fewer than
six target proteins. To overcome this issue, “one-degree” neighbor
proteins were added for those drugs targeting fewer than six proteins.
Significantly enriched biological pathways of drug targets for each of
the 200 drugs were integrated, resulting in 148 key pathways. The
Reactome pathway has a hierarchical structure among pathways. The lower
hierarchy pathway is more specific than the higher hierarchy pathway.
The parent pathway semantically includes the children pathways. In the
process of integrating the enriched pathways per drug, we used the
lowest possible hierarchy pathways to avoid the overlapping biological
meaning among the hierarchical pathways.
On the basis of these identifications, a matrix containing F1 scores of
the 200 drugs and the 148 key pathways was generated for drug-pathway
association. The Reactome pathway enrichment analysis for the 200 drugs
using gprofiler2 provides enrichment P values and precision and recall
information that were used to produce the F1 scores. The meaning of
precision here is the proportion of drug targets that are annotated to
the pathway. The meaning of recall here is the proportion of the
pathway gene set that the drug targets recover. The pathway to which
the largest number of drug target proteins belong has the highest
precision value. The pathway with the greatest intersection of pathway
proteins and target proteins has the highest recall value. In other
words, the pathway with the highest F1 score in the drug-pathway
associations is the pathway to which the drug’s target protein belongs
the most and the pathway with the largest intersection between the
target proteins. For example, the number of target proteins for
sulfasalazine is 13. The number of “arachidonic acid metabolism”
pathway proteins is 59. The number of intersections between the target
protein of sulfasalazine and the arachidonic acid metabolism pathway
protein is 4. So, the precision is 4/13 = 0.3077, and the recall value
is 4/59 = 0.0678. Thus, the F1 score is 0.1111. The number of “fatty
acid metabolism” pathway proteins is 177, and the number of
intersections between the target protein of sulfasalazine and the fatty
acid metabolism pathway protein is 4. The precision is the same as
0.3077 for arachidonic acid metabolism, but the recall value is 4/177 =
0.0225. Thus, the F1 score is 0.0421, which is lower than the
arachidonic acid metabolism. Hence, the F1 score complements the
imbalance between the pathway protein and the target protein. This
matrix was constructed using the F1 score [F1 = 2(precision ×
recall)/(precision + recall)] from the pathway enrichment analysis
(table S8).
MoA analysis
We used SOM ([176]55) to cluster pathways based on their protein
components and F1 score profiles. SOM has a descriptive ability and
hence advantages in visual concept detection. Thus, it was useful to
directly compare the SOM component heatmaps of the 148 pathways. SOM
also has the advantage of dimensional reduction to allow a more
appropriate clustering result. SOM was used followed by k-means
clustering to calculate the low-dimensional abstractions that are then
clustered using k-means. This two-phase approach increases the
efficiency of k-means clustering with a relatively small number of
samples that is a limitation in hierarchical clustering algorithms.
Another advantage of SOM is noise reduction because SOM abstractions
are less sensitive to random variations than the input data. In
addition, SOM offers a systematic arrangement of the 200 drugs to each
neuron and hence to pathway clusters ([177]Fig. 3, D and E).
The data used in training was the F1 score matrix for drug-pathway
associations (148 pathways by 200 drugs; table S8). From the SOM
training, we generated a U-matix that represents the distance between
neighboring nodes in the map. U-matrix of the trained unsupervised SOM
contains the vector norms between the neighboring SOM nodes and shows
data density in input space. Each subunit is colored according to
distance between corresponding data vectors of neighbor units.
Low-distance areas (dark blue) have high data density (clusters)
([178]Fig. 3A). DBI ([179]56) was calculated on the basis of the
U-matrix to determine the optimal number of clusters. We used the DBI,
a metric for within-cluster distance at various SOM parameters.
Minimizing this index allowed discovery of groups of pathways with
shared MoA or protein overlaps. The lowest DBI value occurred at nine
clusters, and thus, we decided to separate the 148 key pathways into
nine pathway clusters (fig. S7). K-means algorithm was then used to
find the nine pathway clusters ([180]Fig. 3B). The SOM component maps
of 148 pathways (fig. S6) were analyzed on the basis of the clustering
result ([181]Fig. 3B) and mapped into two MoA categories based on the
biological functions ([182]Fig. 3C). The mapping result of 148 pathways
to nine clusters and two MoA groups is available in table S9. The SOM
model also labeled each neuron with the 200 drugs ([183]Fig. 3, D and
E). The detailed information of the labeled SOM neurons and the 200
drugs is available in table S6 (columns V and W). The SOM Toolbox
package ([184]57) for MATLAB was used for this analysis with default
settings and parameters.
Quantification of the most frequently targeted proteins among the 200 drugs
The frequency of drug-protein targeting was counted. Permutation tests
were then performed 100 times to identify the significance threshold
for the frequency of drug-protein targeting (fig. S8A). For each
permutation test, the 200 drugs among all the drugs that we used for
the in silico network-based proximity analysis were randomly selected.
Then, the number of drugs targeting the same protein was calculated for
all of the randomly selected 200 drugs. The proteins frequently
targeted in the SIP network (empirical P value of <0.01) were then
tested for enrichment of UniProt keywords (fig. S8B). Since UniProt
keyword contains a mixture of information from 10 different categories,
it was used for the enrichment test to detect any mechanistic
differences among the 200 drugs.
Cell culture
Chlorocebus sabaeus (green monkey) Vero E6 cells [Vero 76, clone E6,
Vero E6, American Type Culture Collection (ATCC) CRL-1586]
authenticated by ATCC and tested negative for mycoplasma contamination
before commencement were maintained in a humidified atmosphere at 37°C
with 5% CO[2], in Dulbecco’s modified Eagle’s medium (DMEM) containing
10% (v/v) fetal bovine serum (FBS; Invitrogen). Calu-3 (ATCC HTB-55)
human lung cells that tested negative for mycoplasma contamination
before commencement were maintained in a humidified atmosphere at 37°C
with 5% CO[2] in Eagle’s minimum essential medium containing 20% (v/v)
FBS. Human cell lines used were either not listed in the
cross-contaminated or misidentified cell line database curated by the
International Cell Line Authentication Committee or were previously
verified by karyotyping.
Viruses and infections
Infection experiments were performed under biosafety level 3
conditions. SARS-CoV-2 (strain München-1.2/2020/984) isolate was
propagated in Vero E6 cells in DMEM supplemented with 2% FBS. For
infection experiments in Vero E6 and Calu-3 cells, SARS-CoV-2 (strain
München-1.2/2020/984) viral supernatant was used at multiplicity of
infection (MOI) = 0.01 plaque-forming units per cell for 24 hours. All
work involving live SARS-CoV-2 was performed at the BSL-3 facility of
the Institute for Virology, University of Giessen (Germany), and was
approved according to the German Act of Genetic Engineering by the
local authority.
Cell infection and drug treatment
Vero E6 and Calu-3 cells were seeded using 8 × 10^4 cells in 24-well
plates. The following day, cells were treated for 3 hours before
infection with the indicated doses of ademetionine (30 μM;
Selleckchem), alogliptin (10 μM; Selleckchem), flucytosine (300 μM;
Selleckchem), proguanil (5 nM to 500 μM; Selleckchem), sulfasalazine (5
nM to 500 μM; Selleckchem), IFN-A (1000 U/ml), dimethyl sulfoxide
(DMSO; Sigma-Aldrich), or mock and infected with SARS-CoV-2 at an MOI
of 0.01 in serum-free DMEM at 37°C for 24 hours before RNA or protein
lysis. Infection experiments were performed under biosafety level 3
conditions.
Quantitative RT-PCR analysis
RNA was isolated using the RNeasy Mini Kit (Qiagen). SARS-CoV-2
replication (E-gene and N-gene RNA) and gene expression of the
cytokines CXCL3, IFNB1, and TNF-A were quantified by reverse
transcription quantitative polymerase chain reaction (RT-qPCR). For
complementary DNA (cDNA) synthesis, RNA was reverse-transcribed with
the SuperScript VILO cDNA Synthesis Kit (Invitrogen, 11755-050). The
levels of specific RNAs were measured using the ABI 7900 real-time PCR
machine and the PowerUp SYBR Green Master Mix (Applied Biosystems,
100029284) according to the manufacturer’s instructions. ΔCT values
were determined relative to glyceraldehyde-3-phosphate dehydrogenase
(GAPDH), and ΔΔCT values were normalized to infected DMSO-treated
samples. Error bars indicate the SD of the mean from three independent
biological replicates. All primer sequences are listed in [185]Table 1
below.
Table 1. Gene names and primer sequences used in the study.
Gene name RT-PCR
Forward primer Reverse primer
CXCL3 GCCCAAACCGAAGTCATAGC CAGTTGGTGCTCCCCTTGTT
IFNB1 GCGACACTGTTCGTGTTGTC AGCCTCCCATTCAATTGCCA
TNF-A GCTGCACTTTGGAGTGATCG TCACTCGGGGTTCGAGAAGA
GAPDH GTCTCCTCTGACTTCAACAGCG ACCACCCTGTTGCTGTAGCCAA
SARSCoV2-E ACAGGTACGTTAATAGTTAATAGCGT ATATTGCAGCAGTACGCACACA
SARSCoV2-N GACCCCAAAATCAGCGAAAT TCTGGTTACTGCCAGTTGAATCTG
[186]Open in a new tab
Cytotoxicity cell viability assays
Cytotoxicity was performed in Vero E6 and Calu-3 cells using Neutral
Red (Abcam, ab234039) and [3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl
tetrazolium bromide (MTT)] (Roche) assay, respectively, according to
the manufacturer’s instructions. Cytotoxicity was performed in Vero E6
and Calu-3 cells with the indicated compound dilutions and concurrent
with viral replication assays. All assays were performed in
biologically independent triplicates.
Western blot analysis
A total of 8 × 10^4 Vero E6 cells either mock-infected or infected and
treated with DMSO or proguanil (50 μM) or sulfasalazine (200 μM) for 24
hours were resuspended and lysed in whole-cell 1× SDS sample buffer [4×
SDS sample buffer: 143 mM tris-HCl (pH 6.8), 28.6% glycerol, 5.7% SDS,
and 4.3 mM bromophenol blue]; supplemented with 2 ml of
2-mercaptoethanol, protease inhibitors (Sigma-Aldrich), and phosphatase
inhibitors (Sigma-Aldrich); and boiled for 5 min at 95°C. A total of 10
to 20 μg of protein were separated on SDS–polyacrylamide gel
electrophoresis gels and blotted onto polyvinylidene difluoride
membranes (Millipore).
Antibodies
Western blot experiments were performed using the following antibodies:
GAPDH (Abcam, ab9484), phospho-MAPKAPK2 (Thr^334, Cell Signaling
Technology, 3007), goat anti-rabbit (Abcam, ab6721), and anti-mouse
horseradish peroxidase (Cell Signaling Technology, 7076S).
Statistical analysis
Statistical analyses performed are specified in the figure legends.
Differences were considered significant for P values of <0.05.
Acknowledgments