Abstract
Background
The coronavirus disease (COVID-19) is a pandemic disease that threatens
worldwide public health, and rheumatoid arthritis (RA) is the most
common autoimmune disease. COVID-19 and RA are each strong risk factors
for the other, but their molecular mechanisms are unclear. This study
aims to investigate the biomarkers between COVID-19 and RA from the
mechanism of pyroptosis and find effective disease-targeting drugs.
Methods
We obtained the common gene shared by COVID-19, RA ([37]GSE55235), and
pyroptosis using bioinformatics analysis and then did the principal
component analysis(PCA). The Co-genes were evaluated by Gene Ontology
(GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and ClueGO for
functional enrichment, the protein-protein interaction (PPI) network
was built by STRING, and the k-means machine learning algorithm was
employed for cluster analysis. Modular analysis utilizing Cytoscape to
identify hub genes, functional enrichment analysis with Metascape and
GeneMANIA, and NetworkAnalyst for gene-drug prediction. Network
pharmacology analysis was performed to identify target drug-related
genes intersecting with COVID-19, RA, and pyroptosis to acquire Co-hub
genes and construct transcription factor (TF)-hub genes and miRNA-hub
genes networks by NetworkAnalyst. The Co-hub genes were validated using
[38]GSE55457 and [39]GSE93272 to acquire the Key gene, and their
efficacy was assessed using receiver operating curves (ROC); SPEED2 was
then used to determine the upstream pathway. Immune cell infiltration
was analyzed using CIBERSORT and validated by the HPA database.
Molecular docking, molecular dynamics simulation, and molecular
mechanics-generalized born surface area (MM-GBSA) were used to explore
and validate drug-gene relationships through computer-aided drug
design.
Results
COVID-19, RA, and pyroptosis-related genes were enriched in pyroptosis
and pro-inflammatory pathways(the NOD-like receptor family pyrin domain
containing 3 (NLRP3) inflammasome complex, death-inducing signaling
complex, regulation of interleukin production), natural immune pathways
(Network map of SARS-CoV-2 signaling pathway, activation of NLRP3
inflammasome by SARS-CoV-2) and COVID-19-and RA-related cytokine storm
pathways (IL, nuclear factor-kappa B (NF-κB), TNF signaling pathway and
regulation of cytokine-mediated signaling). Of these, CASP1 is the most
involved pathway and is closely related to minocycline. YY1,
hsa-mir-429, and hsa-mir-34a-5p play an important role in the
expression of CASP1. Monocytes are high-caspase-1-expressing sentinel
cells. Minocycline can generate a highly stable state for biochemical
activity by docking closely with the active region of caspase-1.
Conclusions
Caspase-1 is a common biomarker for COVID-19, RA, and pyroptosis, and
it may be an important mediator of the excessive inflammatory response
induced by SARS-CoV-2 in RA patients through pyroptosis. Minocycline
may counteract cytokine storm inflammation in patients with COVID-19
combined with RA by inhibiting caspase-1 expression.
Keywords: SARS-CoV-2, COVID-19, rheumatoid arthritis, pyroptosis,
caspase-1, minocycline
Introduction
In 2019, SARS-CoV-2-caused COVID-19 was recognized as a public health
emergency of international concern (PHIEC) and subsequently identified
as a pandemic by the World Health Organization (WHO) ([40]1–[41]6).
SARS-CoV-2 is the third widespread coronavirus outbreak after SARS CoV
in 2003 ([42]7, [43]8) and MERS CoV in 2012 ([44]9, [45]10). Droplets
and aerosols mostly transmit SARS-CoV-2 at close range ([46]11–[47]13).
From the COVID-19 dashboard of the Johns Hopkins Coronavirus Resource
Center: As of 2022.8.28, more than 200 countries/regions worldwide have
recorded over 600 million confirmed cases and over 6.48 million deaths,
with a total of 12.124 billion vaccine doses administered ([48]14).
Coronaviruses (CoVs) are a group of enveloped viruses with a
single-stranded RNA genome (+ssRNA) that exhibits a high mutation rate
and variable recombination rates ([49]15–[50]17). SARS-CoV-2 is the
ninth coronavirus threatening human health ([51]18, [52]19) and has a
high degree of host genetic variation ([53]20–[54]23). SARS-CoV-2 can
encode 29 proteins ([55]24, [56]25), consisting of 16 non-structural
proteins (NSP) ([57]26), 4 structural proteins (spike [S], envelope
[E], membrane [M], and nucleocapsid [N]) ([58]27), and 9 auxiliary
proteins ([59]28). COVID-19 is not just a respiratory disease but also
a systemic disease that affects many of the body’s systems and organs
([60]29, [61]30). SARS-CoV-2 infection frequently disrupts the immune
system ([62]31), resulting in increased expression of autoantigens
during infection and the development of autoantibodies due to the
organism’s potential antigenic cross-reactivity ([63]32–[64]34).
SARS-CoV-2 is not only predisposed to the onset and progression of
autoimmune diseases ([65]35–[66]37), but even SARS-CoV-2 vaccination
can trigger autoimmune phenomena ([67]38, [68]39). Consequently,
patients with autoimmune illnesses have a higher risk of contracting
COVID-19 ([69]40, [70]41).
The COVID-19 Global Rheumatology Alliance Global Registry records: As
of 2022.08.31, the most common autoimmune/rheumatic disease among
COVID-19 patients is RA (40.92%) ([71]42). RA is one of the most
prevalent autoimmune diseases, with a prevalence of up to 1 percent
([72]43–[73]46), and its expanding population coverage has posed a
significant threat to global public health ([74]47). The three primary
causes of RA development are genetic, environmental, and immunological
factors ([75]48, [76]49), with viruses, as part of the environmental
factors, playing a significant role in the development of RA ([77]50,
[78]51). Correspondingly, the immunological dysregulation in RA
patients favors the invasion of SARS-CoV-2 ([79]52, [80]53).
Additionally, the traditional use of DMARDs and glucocorticoids in RA
enhances viral replication via immunosuppression, and the use of
biological agents (e.g., TNF-α-inhibitors) also raises the likelihood
of viral infection in RA ([81]54–[82]57). Therefore, there may be a
potential mutual pathogenic factor between COVID-19 and RA that
contributes to disease progression, and we need to find appropriate
therapeutic agents to combat it.
Pyroptosis is an emerging form of regulated cell death (RCD) and an
active area of research ([83]58). It is caused by innate immune
dysregulation and disruption of organism/cellular homeostasis due to
pathogen invasion ([84]59), as shown by increased plasma membrane
permeability, cell swelling, and rupture ([85]60, [86]61). caspase-1 is
one of the first pro-pyroptosis inflammatory cystathiases identified
([87]62–[88]65), creating NLRP3 inflammasome by binding to NLRP3,
apoptosis-associated speck-like Protein (ASC), which establishes the
canonical route of pyroptosis leading to cell lysis and the release of
IL-1β and IL-18 ([89]66–[90]70). Firstly, active NLRP3 inflammasome and
caspase-1 are detected in the peripheral blood and tissues of COVID-19
patients and are positively correlated with severity markers for
COVID-19 (e.g., IL-6) ([91]71). In SARS-CoV-2 infected cells, NLRP3
inflammasome and caspase-1 activity increase and promote pyroptosis and
cytokine storm ([92]72–[93]74). Secondly, the overactivation of NLRP3
inflammasome and caspase-1 in individuals with RA’s serum, synovium,
and synovial fluid induces pyroptosis and inflammatory responses and is
positively linked with disease activity ([94]75–[95]79). Thus, the
caspase-1-mediated classical pyroptosis pathway may be an important
cause of the vicious cycle of cytokine storm caused by the interaction
between COVID-19 and RA disease. This study investigates the
pathogenesis and disease targets of COVID-19 associated with RA through
bioinformatics and network pharmacology analysis as well as
computer-aided drug design methods and explores the drug and
pharmacology of this target.
Methods
Data collection and processing
Three RA datasets ([96]GSE55235, [97]GSE55457, [98]GSE93272) were
screened using the National Center for Biotechnology Information (NCBI)
Gene Expression Omnibus (GEO) ([99]https://www.ncbi.nlm.nih.gov/geo/)
([100] Table 1 ). [101]GSE55235 contains synovial tissue samples from
10 RA cases and 10 healthy people. [102]GSE55457 contains synovial
tissue samples from 13 RA cases and 10 healthy people, and
[103]GSE93272 contains 232 whole blood samples from RA patients and 43
healthy people. The GeneCards database
([104]https://www.genecards.org/) ([105]80) platform searched for the
keywords “SARS-CoV-2” and “COVID-19” and found 4055 and 4778 related
genes. Xiong et al., 2020 ([106]81), Ziegler et al., 2020 ([107]82),
and Jain et al., 2020 ([108]83), respectively, contributed an
additional 25, 17, and 28 COVID-19-related genes ([109] Supplementary
Table 1 ). A total of 5103 COVID-19-related genes were obtained by
pooling and de-duplicating these genes. Similarly, a search of the
GeneCards database using the keyword “pyroptosis” yielded 254 related
genes.
Table 1.
Basic information of selected datasets.
Dataset ID Platform Tissue(Homo sapiens) Experimental group Normal
control Experiment type
[110]GSE55235 [111]GPL96 Synovium 10 10 Array
[112]GSE55457 [113]GPL96 Synovium 13 10 Array
[114]GSE93272 [115]GPL570 Whole blood 232 43 Array
[116]Open in a new tab
Identification of co-genes
The empirical Bayesian method in the limma package
([117]http://www.bioconductor.org/packages/release/bioc/html/limma.html
) ([118]84) was used to analyze the RA and healthy controls (HC) groups
of the [119]GSE55235 dataset in different gene expression analyses.
|log2 FC| >0.5 and P< 0.05 as the cutoff. Further mapping of volcanoes
using the ggplot2 package to reflect RA-differentially expressed genes
(DEGs). Co-genes were obtained from the intersection of COVID-19,
RA-DEGs ([120]GSE55235), and pyroptosis-related genes using the
Venn-diagram package in R software and subjected to PCA.
GO, KEGG, and ClueGO enrichment analyses of co-genes
For the investigation of the pathway and function of the Co-genes, the
R package “clusterProfiler” ([121]85) was used to conduct GO and KEGG
enrichment analyses. Co-genes are visualized through ClueGO (a plug-in
for Cytoscape, using kappa’s statistical analysis method) to
differentiate between up-and down-regulated genes to construct
interactive gene network maps and analyze the function of target gene
sets.
PPI network analysis and machine learning for the identification of hub genes
The STRING database ([122]https://string-db.org/) ([123]86) was
utilized to analyze the Co-genes and build a PPI network with a
confidence score > 0.40 as the threshold. The k-means algorithm is an
effective unsupervised machine learning technique ([124]87). It enables
the prediction of protein-protein interactions without explicit data
labeling. We used the k-means algorithm (the network was clustered to a
specified number of clusters, the number clusters: 3) Clustering
analysis of Co-genes. The Cytoscape platform ([125]88) is utilized to
visualize PPI network data, while the MCODE (a Cytoscape plug-in) is
utilized for modular analysis of PPI networks. The cytoHubba uses the
Degree algorithm to identify Hub genes from Co-genes.
Metascape, geneMANIA and network analyst analyses of hub genes
Metascape ([126]https://metascape.org/gp/index.html#/main/step1)
([127]89) is a gene function analysis website that aggregates over 40
databases and groups genes into clusters based on Terms with a P< 0.01,
a minimum count of 3, and an enrichment factor >1.5 to group genes into
clusters and find pathways for the enrichment of Hub genes and
associated functional annotations. Cytoscape connected terms with
similarity > 0.30 to further build a network graphic to capture the
linkages between gene clusters. GeneMANIA
([128]http://www.genemania.org) ([129]90) is a website that integrates
different databases and technologies, including Gene Expression Omnibus
(GEO) and the Biological General Repository for Interaction Datasets
(BioGRID), for predicting the functions of Hub genes and identifying
gene priority and interconnections. NetworkAnalyst
([130]https://www.networkanalyst.ca/) ([131]91) is a website for visual
analysis of gene expression profiling and meta-analysis. The hub genes
were analyzed for associations with potentially relevant medications
(DrugBank Version 5.0) by the site’s Protein-drug interactions function
(minimum network).
Screening for minocycline-related target genes and co-hub genes
CASP1, CASP3, and ILB in the hub genes were closely related to
minocycline from NetworkAnalyst analysis. Therefore, minocycline was
hypothesized to be an effective drug against this mechanism, and
relevant validation was carried out. We used SwissTargetPrediction
([132]http://www.swisstargetprediction.ch/) ([133]92), CTD
([134]http://ctdbase.org/) ([135]93), Drugbank
([136]https://go.drugbank.com/drugs/DB01017) ([137]94) and STITCH
([138]http://stitch.embl.de/cgi/input.pl) which are four databases to
search for potentially related genes of minocycline. The STITCH
database unifies drug-gene connections between more than 68,000
distinct compounds and 1.5 million genes; we utilize STITCH to
visualize minocycline and target genes. COVID-19, RA-DEGs
([139]GSE55235), pyroptosis-related genes, and minocycline-related
target genes were intersected to determine the set of Co-targets.
Subsequently, the Hub genes were intersected with the Co-targets to
obtain Co-hub genes.
Establishment of the TF-hub genes and miRNA-hub genes network
Co-hub genes were submitted to the NetworkAnalyst platform, TFs were
obtained from the ENCODE database, and miRNAs were obtained from
miRTarBase and TarBase. Visualization of TF-hub genes and miRNA-hub
genes network using Cytoscape.
Validation of co-hub genes and identification of key gene
To increase the reliability of the results as well as
comprehensiveness, we included [140]GSE55457 and [141]GSE93272 as
validation sets in this study. The intersection of the co-hub genes,
RA-DEGs ([142]GSE55457) and RA-DEGs ([143]GSE93272), was identified as
a key gene. Boxplot analyzed the expression of the key gene, and ROC
([144]95) was used to determine the sensitivity and specificity of the
key gene. The area under the curve (AUC) > 0.8 is considered to have a
significant diagnostic value.
Upstream pathway activity
SPEED2 ([145]https://speed2.sys-bio.net/) ([146]96) is an upstream
signaling pathway enrichment analysis platform that evaluates the
significance of 16 classical signaling pathways based on P-values using
gene set data from human cell biology research. We used the bates test
in SPEED2 to predict the upstream signaling pathways of the co-hub
genes and the Key gene.
Analysis of immune cell infiltration
The CIBERSORT algorithm ([147]http://CIBERSORT.stanford.edu/) is a
linear support vector regression-based methodology ([148]97) applied to
assess the makeup and number of immune cells in RA and HC. The
relationship between the expression of the key gene and the abundance
of immune cells in RA was revealed using person correlation coefficient
analysis to find the immune cells closely related to it. The Human
Protein Atlas ([149]https://www.proteinatlas.org/) contains data on the
tissue and cellular distribution and expression abundance of nearly all
human proteins. The HPA database was utilized to validate the key
gene-immune cell associations to guarantee the accuracy of the results.
Molecular docking
Molecular docking techniques were used to verify the affinity of
minocycline to the crystal structure of the protein expressed by the
Key gene. First, a two-dimensional (2D) structure of minocycline was
obtained in sdf format from the Drugbank database or the PubChem
database ([150]https://pubchem.ncbi.nih.gov/) ([151]98) for use as a
ligand. Entry for Key gene obtained from Uniport database
([152]https://www.uniprot.org/) ([153]99) (CASP1: [154]P29466). Enter
the entry into the RCSB PDB database ([155]https://www.rcsb.org/)
([156]100) and download the protein structure in pdb format to use as a
receptor. Second, using ChemBio 3D Ultra 12.0 software, the 2D
structure of the ligand (minocycline) was transformed to a 3D
structure, optimized, and saved in mol2 format. The receptor
(caspase-1) was processed using PyMOL 2.4.0 software to remove solvent
molecules and ligands and then saved in pdb format. Third, After
processing the ligands and receptors in Autodock 1.5.6 software and
saving the results in pdbqt format, molecular docking was used to
identify the activity pockets of candidate loci and export the results
in gpf format. Finally, the AutoDock Vina software was used to carry
out the molecular docking commands, and PyMOL 2.4.0 was used to
visualize and analyze the results.
Molecular dynamics simulation and molecular mechanics-generalized born
solvent accessibility
Further investigation of the dynamic properties, stability, and
structural flexibility of protein-drug complexes can be done by
molecular dynamics simulations. It permits the examination of the
interaction between the drug and the amino acid residues of the target
protein and acts as an in-depth validation of molecular docking. MD to
MDS and MM-GBSA calculations are a series of workflows for
computer-aided drug design to study the properties of ligand-receptor
interactions.
AMBER 18 was used to examine the stability of the complexes by
simulating the molecular docking of ligands and receptors using
all-atom MDS of ligands and receptors. Before the simulation, the
charge of the minocycline was determined using the HF-SCF (6-31G**)
computation with the antechamber module and gauss 09 software. The
GAFF2 small molecule force field and the ff14SB protein force field
were utilized to describe, respectively, the ligand (minocycline) and
the receptor (caspase-1) ([157]101, [158]102). The LEaP module was
utilized to introduce hydrogen atoms, and a TIP3P solvent cartridge was
added at 10Å. The system’s charge is then balanced by adding Na^+/Cl^-,
and the topology and parameter files required for the molecular
simulation are then output. Optimization of system energy via a
2500-step steepest descent method and a 2500-step conjugate gradient
method. The system was warmed up at 200 ps and stabilized from 0 K to
298.15 K, followed by a 500 ps NVT ensemble simulation and a 500 ps
equilibrium simulation. The system was warmed up at 200 ps, from 0 K to
298.15 K, followed by an NVT system simulation (isothermal isomer) at
500 ps, followed by an equilibrium simulation (isothermal isobaric) at
500 ps. The final NVT system simulation (isothermal isobaric) was
carried out for 100 ns. Other parameters: truncation distance set to 10
Å, collision frequency γ set to 2 ps^-1, system pressure 1 atm,
integration step 2 fs, trajectory saved at 10 ps intervals.
The free energy of binding between receptor and ligand is calculated by
the MM/GBSA method ([159]103, [160]104). The specific formula is as
follows:
[MATH:
ΔGbind=ΔGcomplex –<
mtext> (ΔGreceptor+ ΔGligand) :MATH]
[MATH:
=ΔEintern
al+ΔE<
mrow>VDW+ΔEelec+ΔGGB+ΔGSA :MATH]
ΔE[internal] : Internal energy, ΔE[VDW] : Van der Waals interactions,
ΔE[elec] : Electrostatic interactions, ΔG[GB] and ΔG[SA] :
solvation-free energy.
The flowchart shows all of our study’s key and important procedures
([161] Figure 1 ). The GitHub page for this study is
HTTPS([162]https://github.com/zheng5862/COVID-19-RA.git).
Figure 1.
[163]Figure 1
[164]Open in a new tab
The schematic block diagram of the entire workflow of this study. ❶
Bioinformatics analysis. ❷ Network pharmacology. ❸ Computer-aided drug
design.
Results
Identification of co-genes
2230 RA-DEGs were obtained from the [165]GSE55235 dataset and
visualized using volcano maps and clustered heat maps ([166] Figures 2
, [167]3 ). Co-genes are intersecting genes for COVID-19, RA-DEGs
([168]GSE55235), and pyroptosis and include 35 genes, of which 23 are
upregulated and 12 are down-regulated ([169] Figure 4A ). PCA analysis
of the Co-genes in the [170]GSE55235 dataset revealed that PC1 (54.84%)
and PC2 (7.91%) confirmed the Co-genes’ significant reliability and
between-group variability ([171] Figure 4B ).
Figure 2.
[172]Figure 2
[173]Open in a new tab
RA-DEGs identification. In the [174]GSE55235 dataset, red triangles
represent upregulated genes (P < 0.05), green triangles represent
downregulated genes (P < 0.05), and gray dots represent genes not
significantly differentially expressed across the RA and HC groups (P >
0.05).
Figure 3.
[175]Figure 3
[176]Open in a new tab
RA-DEGs distribution. The Clustering heat map displays the top one
hundred DEGs from the [177]GSE55235 dataset. The samples from the RA
group were colored red, while those from the HC group were colored
blue. Yellow rectangles represent highly expressed genes (P < 0.05),
while blue rectangles represent lowly expressed genes (P < 0.05).
Figure 4.
[178]Figure 4
[179]Open in a new tab
Screening Co-genes. (A) Venn-diagram on COVID-19, RA-DEGs
([180]GSE55235), pyroptosis-related genes. Co-genes include 35 genes.
(B) PCA analysis of Co-genes in the [181]GSE55235 dataset: PC1 (54.84%)
and PC2 (7.91%).
Functional enrichment analyses of co-genes
GO analysis showed that the biological process (BP) was mainly enriched
in the immune system process ([182] Figure 5A ). Cellular component
(CC) was mainly enriched in the cytoplasm, inflammasome complex,
death-inducing signaling complex, NLRP3, and NLRP1 inflammasome complex
([183] Figure 5B ). Molecular function (MF) was mainly enriched in
signaling receptor binding, protein domain-specific binding, cytokine
receptor binding, tumor necrosis factor receptor superfamily binding,
and death receptor binding ([184] Figure 5C ). The ClueGO analysis
showed visually that the upregulated genes of Co-genes were mainly
enriched in NLRP3 inflammasome complex, positive response to cytokine
stimulus, cytokine production involved in immune response, and
regulation of interleukin (IL-1β, IL-6, IL-8, IL-17) production ([185]
Figure 5D ). KEGG analysis was mainly enriched in the NOD-like receptor
(NLR) signaling pathway, the IL-17 signaling pathway, and the Toll-like
receptor (TLR) signaling pathway ([186] Figure 5E ).
Figure 5.
[187]Figure 5
[188]Open in a new tab
Co-genes functional enrichment analysis using GO, ClueGO, and KEGG. (A)
Enrichment of Co-genes in BP. (B) Enrichment of Co-genes in CC. (C)
Enrichment of Co-genes in MF. (D) Co-genes Analysis Using ClueGO.
Red-denoted pathways for upregulated genes, while blue-denoted pathways
for downregulated genes. (E) Co-genes Analysis Using KEGG.
PPI network analysis and machine learning for hub genes
This PPI network has 35 nodes, 202 edges, an average node degree of
11.5, and an average local clustering coefficient of 0.632 ([189]
Figure 6A ). Using a machine learning algorithm, the k-means clustering
analysis of the PPI data predicted the four genes in the lower right
corner of the amplified content to be CASP1, NLRP3, IL1B, and IL18
([190] Figure 6B ). These are the genes for the four most important
proteins in the caspase-1-driven classical pyroptosis pathway. By using
the degree algorithm of the CytoHubba program to the PPI data, the
distribution of genes becomes specific and hierarchical, and it can be
seen that the top 11 hub genes in the center of the ring were: IL1B,
CASP1, CASP3, JUN, MYD88, CASP8, NLRP3, HSP90AA1, CXCL8, IL18, EGFR
(where the Degree algorithm values for IL18 and EGFR were equal) ([191]
Figure 6C ).
Figure 6.
[192]Figure 6
[193]Open in a new tab
Screening Hub genes. (A) PPI network diagram obtained after applying
the k-means algorithm based on machine learning to the Co-genes. The
four genes in the lower right-hand corner of the enlarged diagram are
CASP1, NLRP3, IL1B, and IL18. (B) PPI network diagram after processing
with Cytoscape software. (C) The Top 11 hub genes are filtered using
the Degree algorithm under the CytoHubba package condition.
Functional network analysis of the top 11 hub genes
The results of the Metascape analysis were as follows. In pathway and
process enrichment analysis, the main enrichments were in the network
map of the SARS-CoV-2 signaling pathway; Nucleotide-binding
oligomerization domain (NOD) pathway; and Signaling by Interleukins
([194] Table 2 ) ([195] Figure 7A ). Network diagrams will allow
visualization of the associations between the pathways ([196] Figure 7B
). In the PPI enrichment analysis, the main enrichments were in the NOD
pathway, the activation of the NLRP3 inflammasome by SARS-CoV-2 ([197]
Figure 7C ), and the NLR signaling pathway ([198] Figure 7D ).
Inflammasome complex, positive regulation of cysteine-type
endopeptidase activity, production of IL(LI-1β, IL-6), NF-κB signaling,
TNF-mediated signaling pathway, and regulation of cytokine-mediated
signaling pathway were all enriched in GeneMANIA analysis of the top 11
hub genes ([199] Figure 8A ). Of these, CASP1 is the most involved in
the pathway. The protein-drug interactions function on NetworkAnalyst
(DrugBank database 5.0) found minocycline to be closely related to
CASP1, CASP3, and IL1B ([200] Figure 8B ).
Table 2.
Pathway and Process Enrichment Analysis in metascape.
GO Category Description Count % Log10(P) Log10(q)
hsa05417 KEGG Pathway Lipid and atherosclerosis 10 90.91 -20.53 -16.18
hsa05133 KEGG Pathway Pertussis 7 63.64 -15.8 -12.3
WP5115 WikiPathways Network map of SARS-CoV-2 signaling pathway 8 72.73
-14.93 -11.49
WP1433 WikiPathways Nucleotide-binding oligomerization domain (NOD)
pathway 6 54.55 -14.71 -11.31
hsa04657 KEGG Pathway IL-17 signaling pathway 6 54.55 -12.45 -9.28
R-HSA-449147 Reactome Gene Sets Signaling by Interleukins 8 72.73
-12.35 -9.22
WP2324 WikiPathways AGE/RAGE pathway 5 45.45 -10.71 -7.68
hsa04625 KEGG Pathway C-type lectin receptor signaling pathway 5 45.45
-9.7 -6.91
M110 Canonical Pathways PID IL1 PATHWAY 4 36.36 -9.36 -6.59
WP2873 WikiPathways Aryl hydrocarbon receptor pathway 4 36.36 -8.74
-6.09
GO:0062197 GO Biological Processes cellular response to chemical stress
5 45.45 -7.64 -5.13
GO:0000165 GO Biological Processes MAPK cascade 4 36.36 -6.41 -4.08
GO:0046677 GO Biological Processes response to antibiotic 3 27.27 -6.27
-3.96
GO:1902107 GO Biological Processes positive regulation of leukocyte
differentiation 3 27.27 -4.5 -2.49
[201]Open in a new tab
Figure 7.
[202]Figure 7
[203]Open in a new tab
Metascape analysis of Hub genes. (A) Pathway and process richness
analysis. (B) The network is shown using Cytoscape^5, with nodes with
the same cluster ID typically located close to one another. (C, D)
Protein-protein Interaction Enrichment Analysis.
Figure 8.
[204]Figure 8
[205]Open in a new tab
GeneMANIA and NetworkAnalyst analysis of Hub genes. (A) The GeneMANIA
database examined the gene-gene interaction network of the top 11 hub
genes and the 20 most nearby genes. Each node represents a gene. The
color of the node links shows the relationship between the relevant
genes. (B) Results for the top 11 Hub genes by NetworkAnalyst’s
Protein-Drug Interaction Function (DrugBank database 5.0). Drugs were
in red and target genes were in green.
Identification of minocycline-related target genes and co-hub genes
Top 100, 92, 12, and 10 minocycline-related target genes from
SwissTargetPrediction, CTD, Drugbank, and STITCH databases,
respectively ([206] Supplementary Table 2 ). We can visualize the
connection between minocycline, each target gene, and gene to gene in
the STITCH interaction network diagram ([207] Figure 9A ). A total of
194 minocycline-related Targets were obtained by pooling the total
genes and removing duplicates. Co-targets were 194 genes intersecting
with COVID-19, RA-DEGs ([208]GSE55235), and pyroptosis-related genes,
including 7 genes: CASP1, CASP8, IL1B, CASP3, JUN, EGFR, CXCL8 ([209]
Figure 9B ). Co-targets were intersected with the top 11 hub genes to
obtain the Co-hub genes ([210] Figure 9C ). All 7 genes in the Co-
Targets were contained in the top 11 hub genes, suggesting that the
targets of minocycline action may be proteins of core genes involved in
the pyroptosis mechanism of COVID-19 and RA.
Figure 9.
[211]Figure 9
[212]Open in a new tab
(A) Network diagram of minocycline and related target genes on the
STITCH platform, with minocycline in capsules and related target genes
in circles. (B) Venn-diagram of Co-genes versus minocycline-targets.
(C) Venn diagram of the top 11 hub genes versus Co-targets, with
Co-targets all contained in the top 11 hub genes.
TF-hub genes and miRNA-hub genes network for co-hub genes
The TF-hub genes network consists of 7 seeds, 51 edges, and 40 nodes
([213] Figure 10A ), and the simplified minimum network consists of 7
seeds, 19 edges, and 14 nodes ([214] Figure 10B ). YY1 has the
potential to regulate CASP1, CASP8, and CXCL8. The miRNA-hub genes
analyzed using the TarBase package consisted of 7 seeds, 407 edges, and
267 nodes ([215] Figure 10C ), and the simplified minimum network
consisted of 7 seeds, 40 edges, and 17 nodes ([216] Figure 10D ).
CASP1, CASP3, IL1B, CXCL8, and JUN were all closely related to
hsa-mir-429. The miRNA-hub genes analyzed using the miRTarBase package
consisted of 7 seeds, 210 edges, and 189 nodes ([217] Figure 10E ), and
the simplified minimum network consisted of 7 seeds, 19 edges, and 14
nodes ([218] Figure 10F ). CASP1, CASP3, and CASP8 were all closely
related to hsa-mir-34a-5p. In conclusion, YY1, hsa-mir-429, and
hsa-mir-34a-5p may play an important role in the expression of CASP1.
Figure 10.
[219]Figure 10
[220]Open in a new tab
TF-hub genes and miRNA-hub genes network construction using
NetworkAnalyst. (A, B) TF-hub genes network and simplified diagram.
Circles were genes, while squares were TFs. (C, D) miRNA-hub genes
network and simplified diagram (TarBase version 8.0). Circles represent
genes, while squares are miRNAs. (E, F) miRNA-hub genes network and
simplified diagram (miRTarBase v8.0). Circles represent genes, while
squares are miRNAs.
Validation of co-hub genes and identification of key gene
900 DEGs were obtained from the [221]GSE55457 validation set, of which
470 were upregulated genes and 430 were down-regulated genes ([222]
Figure 11A ). 338 DEGs were obtained from the [223]GSE93272 validation
set, 322 upregulated genes, and 16 down-regulated genes ([224]
Figure 11B ). The distribution of these two RA-DEGs was visualized
separately using volcano plots. The only Key gene in the Venn-diagram
intersection of the Co-hub genes with these two RA-DEGs is CASP1 ([225]
Figure 11C ). CASP1 was highly expressed in the RA group in all three
datasets (P<0.01) ([226] Figures 11D–F ). The AUC values of CASP1 in
the [227]GSE55235, [228]GSE55457, and [229]GSE93272 datasets were 0.97
(0.91-1.00), 0.88 (0.72-1.00), and 0.85 (0.79-0.90), respectively, all
of which were greater than 0.8, using ROC curves to verify the
diagnostic validity of CASP1 as a biomarker with good specificity and
sensitivity ([230] Figures 11G–I ).
Figure 11.
[231]Figure 11
[232]Open in a new tab
Screening and validation of key gene. (A) Volcano map of the
[233]GSE55457 dataset. (B) Volcano map of the [234]GSE93272 dataset.
Red triangles represent upregulated genes (P < 0.05), green triangles
represent downregulated genes (P < 0.05), and gray dots represent genes
not significantly differentially expressed across the RA and HC groups
(P > 0.05). (C) Venn-diagram of RA-DEGs of [235]GSE55457 and
[236]GSE93272 with Co-hub genes. (D–F) Expression of CASP1 in the
[237]GSE55235, [238]GSE5457, and [239]GSE93272 datasets, Red for the RA
group and cyan for the HC group (**P < 0.01 and ****P < 0.0001). (G–I)
The AUC of the ROC curve verifies the diagnostic validity of CASP1 in
[240]GSE55235, [241]GSE55457和[242]GSE93272 (P < 0.05).
Upstream pathway activity
SPEED2 analysis in the context of all human gene sets showed that
Co-Hub Genes were associated with the IL-1 signaling pathway ([243]
Figure 12A ), and the Key gene (CASP1) was associated with the Janus
kinase/signal transducer and activator of transcription (JAK-STAT)
signaling pathway ([244] Figure 12B ).
Figure 12.
[245]Figure 12
[246]Open in a new tab
Upstream Pathway Activity. (A, B) SPEED2 platform analysis for Co-Hub
Genes and key gene.
Immune infiltration analysis
In this study, LM22 immune cell infiltration data in RA ([247]GSE93272)
was obtained by the CIBERSORT algorithm. CASP1 was positively
correlated with monocytes, dendritic cells activated, and neutrophils
by Pearson correlation coefficient analysis ([248] Figure 13A–C ). Both
the HPA and Monaco datasets in the HPA platform showed that the top
three immune cells with high CASP1 expression were monocytes, dendritic
cells (DCs), and neutrophils ([249] Figures 13D, E ), thus validating
our results for immune infiltration analysis.
Figure 13.
[250]Figure 13
[251]Open in a new tab
Analysis of immune cell infiltration. (A–C) Immune infiltrating cells
positively associated with high CASP1 expression in LM22: Monocytes,
Dendritic cells activated, and Neutrophils. (D, E) Distribution of
CASP1 expression in immune cells from HPA datasets and Monaco datasets.
Molecular docking
A drug’s conformation within a protein target binding site can be
predicted by molecular docking, which can also predict the binding
affinity. We obtained the 2D and 3D structures of minocycline ([252]
Figures 14A, B ) and showed by MD analysis that minocycline forms four
hydrogen bonds with the four amino acid residues ASP-157, LYS-158,
SER-159, and HIS-404 of caspase-1, allowing minocycline to bind tightly
to the active pocket of caspase-1 to form a stable complex ([253]
Figure 14C ).
Figure 14.
[254]Figure 14
[255]Open in a new tab
Structure of minocycline and molecular docking. (A, B) 2D and 3D
structures of minocycline. (C) Results of molecular docking of
minocycline with caspase-1 protein.
Molecular dynamics simulation and MM-GBSA
The MDS’s root-mean-square deviation (RMSD) depicts the movement of
caspase-1 and minocycline; a greater value and amplitude of the RMSD
suggests an intense movement and vice versa for a smooth movement. In
[256]Figure 15A , caspase-1 (red line) swings widely in the early
portion of the simulation, begins to converge at 40 ns and plateaus
later in the simulation, and caspase-1 fluctuates within 5Å overall,
indicating that there has been no major disintegration. Minocycline’s
(black line) value and amplitude were minor, fluctuating steadily
around 1 Å and not reaching 1.5 Å. Typically, the RMSD of small
molecules does not exceed 2 Å, indicating a weak conformational change.
In conclusion, caspase-1 binds stably to the minocycline, almost
tightly bound to the active site docked with caspase-1. The
root-mean-square fluctuation (RMSF) indicates the flexibility of
caspase-1 during the MDS process. When the drug attaches to the
protein’s active site, its flexibility diminishes, stabilizing the
protein and allowing the drug to have its biochemically active action.
In [257]Figure 15B , caspase-1 is composed of Chain A and Chain B.
Overall, Chain A has a lower RMSF than Chain B, indicating that Chain A
is less flexible. Minocycline interacts with Chain The start sequence
of caspase-1 (the yellow background highlights the binding site) and
the fact that the RMSF value for this region is less than 2 Å,
indicating low protein flexibility, indicates that the binding of
minocycline to caspase-1 is in a highly stable state.
Figure 15.
[258]Figure 15
[259]Open in a new tab
Molecular Dynamics Simulation and MM-GBSA. (A) Variation of the root
means square deviation (RMSD) difference with time for small molecule
compounds (black line) and proteins (red line) during molecular
dynamics simulations. (B) Root mean square fluctuations (RMSF) are
calculated based on molecular dynamics simulation trajectories. (C) The
top 10 amino acids that contribute to small molecule and protein
binding. (D) Changes in the number of hydrogen bonds between small
molecules and proteins result from molecular dynamics simulations.
Based on MDS, the binding energy of minocycline to caspase-1 was
determined using MM-GBSA. It can reflect the binding pattern of the
medication to the protein more precisely. A negative binding energy
value (ΔG [bind] ) implies that the medication binds to the protein
with affinity, whereas a smaller value indicates a greater binding
capability. The binding energy of minocycline/caspase-1 was -21.43 ±
3.89 kcal/mol, showing that minocycline has a strong binding affinity
for caspase-1. The energy decomposition reveals that van der Waals and
electrostatic forces are the primary contributors to their binding
([260] Table 3 ). The amino acid residue decomposition results of
MM-GBSA can be more accurate than the active amino acid residues
obtained by molecular docking. In [261]Figure 15C , The top 10 amino
acids that play a key role in minocycline/caspase-1 were: ILE-155,
TRP-145, ASP-157, LEU-154, ALA-141, MET-156, GLN-142, SER-159, ARG-161.
The ILE-155 ΔG[bind] is -2.625 kcal/mol, TRP-145 is -1.513 kcal/mol,
and ASP-157 is -0.967 kcal/mol ([262] Table 4 ). Thus ILE-155, TRP-145,
and ASP-157 are the major and maintained by hydrogen bonding
minocycline/caspase-1 tightly bound amino acids. Hydrogen bonding is
one of the greatest forces for the non-covalent binding of medicines
and proteins, and an investigation of the number of hydrogen bonds is
required to comprehend the relationship between minocycline and
caspase-1. Based on MDS trajectory monitoring, we acquired the
coordinates of the number of hydrogen bond formations between
minocycline and caspase-1 over time. In [263]Figure 15D , In the early
part of the simulation (0-20 ns), the number of hydrogen bonds
fluctuated in the range of 1-5, and in the middle and late part of the
simulation (20-100 ns), the number of hydrogen bonds was mainly
concentrated in 1-2. Thus, minocycline interaction with caspase-1
relies heavily on 1-2 hydrogen bonding forces.
Table 3.
The prediction of binding free energies and energy components by
MM/GBSA.
System name Minocycline/caspase-1(kcal/mol)
ΔE [vdw] -31.73±1.15
ΔE [elec] -33.22 ±9.62
ΔG[GB] 47.07±5.66
ΔG[surf] -3.55 ±0.11
ΔG[bind] -21.43 ±3.89
[264]Open in a new tab
ΔE[vdW]: van der Waals energy.
ΔE[elec]: electrostatic energy.
ΔG[GB]: electrostatic contribution to solvation.
ΔG[SA]: non-polar contribution to solvation.
ΔG[bind]: binding free energy.
Table 4.
The binding energy of top10 amino acids contributes to
minocycline/caspase-1 binding.
Residue ΔG[bind](kcal/mol) STD
ILE-155 -2.6540984 0.571406151
TRP-145 -1.512980667 0.413772826
ASP-157 -0.966774667 1.55565844
LEU-138 -0.828761067 0.244439787
PRO-154 -0.727401867 0.173517763
ALA-141 -0.677654667 0.207741157
MET-156 -0.544312933 0.242333531
GLN-142 -0.412951867 0.235780676
SER-159 -0.292502533 0.244958839
ARG-161 -0.282 0.068203128
[265]Open in a new tab
Discussion
35 Co-genes were obtained by the intersection of COVID-19, RA
([266]GSE55235), and pyroptosis-related genes enriched in NLR/TLR
signaling pathway, NLRP3 inflammasome complex, death-inducing signaling
complex, regulation of interleukin production and cytokine production
involved in immune responses. The top 11 hub genes in Metascape were
enriched in the network map of the SARS-CoV-2 signaling pathway,
activation of the NLRP3 inflammasome by SARS-CoV-2, NLR signaling
pathway, and interleukins signaling pathway. While they were enriched
in GeneMANIA in inflammasome complex, IL production pathway, NF-κB
signaling, TNF signaling, and regulation of cytokine-mediated signaling
pathway. CASP1 was most involved in these enrichment pathways.
Minocycline was found to be closely associated with CASP1 by
NetworkAnalyst analysis. Therefore, based on bioinformatics analysis
and further network pharmacology analysis, it was surprising to find
that the 7 Co-hub genes obtained from the intersection of minocycline
with COVID-19, RA ([267]GSE55235), and pyroptosis were all contained in
the top 11 hub genes of COVID-19, RA ([268]GSE55235), and pyroptosis.
One important TF (YY1) and two important miRNAs (hsa-mir-429 and
hsa-mir-34a-5p) associated with CASP1 were obtained by TF-hub genes and
miRNA-hub genes network. The key gene was validated by the
[269]GSE55457 and [270]GSE93272 validation sets and obtained as CASP1,
which was highly expressed in the RA group in all three datasets and
validated with ROC for significantly good test performance. This gene
coincided with the results of previous pathway analysis. SPEED2
analysis indicates that CASP1 is associated with the JAK-STAT signaling
pathway. Immune cell infiltration analysis revealed that monocytes,
dendritic cells activated, and neutrophils were able to express CASP1
at high levels, and the reliability of the results was verified by
using the HPA dataset and Monaco dataset databases. Finally, the
relationship between minocycline and caspase-1 was investigated and
verified by MD, MDS, and MM-GBSA: minocycline can dock close to the
active site of caspase-1 to form a highly stable state and exert the
biochemical activity of the drug.
Caspase-1 induces the classical pathway of pyroptosis
In this study, COVID-19, the crossover genes between RA and pyroptosis
were enriched in the NLR/TLR signaling pathway, NLRP3 inflammasome
complex, death-inducing signaling complex, regulation of interleukin
production, NF-κB signaling, and TNF signaling. These pathways are all
closely related to the caspase-1-induced pyroptosis pathway.
It is known that the innate immune system can recognize the viral
pathogen-associated molecular pattern (PAMP) and host cell-derived
damage-associated molecular pattern (DAMP) using the pathogen
recognition receptor (PRR) ([271]105–[272]107). PRRs are divided into 2
main categories of 4 sensors: transmembrane proteins (TLRs, C-type
lectin-receptors (CLRs)) and cytoplasmic proteins (RIG-I-like receptors
(RLRs), NLRs) ([273]108–[274]110). NLRs, also known as versatile
cytosolic sentinels ([275]111, [276]112), play a significant role in
the molecular processes (antigen presentation, inflammatory response,
and cell death) linked to viral infectious diseases and autoimmune
diseases ([277]111, [278]113, [279]114). Five isoforms of NLRs, NLRA,
NLRB, NLRC, NLRP, and NLRX1, activate two downstream signaling
pathways: NOD1/NOD2 signaling and inflammasome signaling pathways
([280]115), which recruit immune cells to produce pro-inflammatory
cytokines ([281]116). Caspases are a class of conserved cysteinyl
proteases that activate themselves and other caspases by
aspartate-specific cleavage ([282]117) and can also cleave vast
quantities of cellular substrates to drive cell death (e.g., apoptosis,
pyroptosis) and inflammation ([283]118). Caspases are classified as
either apoptotic or inflammatory ([284]119), with caspase-1 being the
first member of the protease family of cysteases to be found ([285]120)
and the apical caspase of the inflammasome ([286]121). caspase-1, one
of the most typical inflammatory caspases, plays a crucial function in
the regulation of pyroptosis and pro-inflammatory activities ([287]122,
[288]123). Since inflammatory caspases are inactive zymogens, they must
be activated by the inflammasome to become proteolytically active
([289]124). Inflammasomes are multiprotein complexes activated in
response to endogenous and microbiological stimuli ([290]125). The
NLRP3 inflammasome is one of the most thoroughly researched and
best-characterized inflammasomes in recent years ([291]126), and it is
the canonical activation platform for caspase-1 ([292]127). The NLRP3
inflammasome is made up of a sensor (NLRP3), an adaptor (ASC), and an
effector (caspase-1) ([293]128). NLRP3 has a C-terminal Leucine rich
repeat (LRR), a central nucleotide-binding and oligomerization domain
(NACHT), and an N-terminal pyrin domain (PYD) ([294]129, [295]130),
whereas ASC has an N-terminal PYD and a C-terminal caspase recruitment
domain (CARD) ([296]131). full-length caspase-1 is composed of an
N-terminal CARD, a main big catalytic domain (p20), and a C-terminal
small catalytic subunit domain (p10) ([297]132). PYD and CARD
structural domains belong to the death domain (DD) fold superfamily
([298]133).
NLRP3 inflammasome requires an initiation and activation pathway. The
beginning step is the NF-κB-NLRP3 axis, in which the detection of
PAMP/DAMP by a particular PRR (e.g., TLR) activates the NF-κB pathway,
increasing NLRP3 expression ([299]134, [300]135). During the initiation
phase, phosphorylation and ubiquitination are further
post-translational modifications of NLRP3 ([301]136). The activation
phase is the NLRP3/ASC/pro-caspase-1/caspase-1 axis, with NLRP3
recruiting the adaptor ASC through PYD-PYD interactions ([302]137,
[303]138), then ASC recruiting pro-Caspase-1 through CARD-CARD
interactions ([304]139, [305]140). Since autocatalytic activity permits
autoconversion into p33 (both CARD and p20) and p10, removing CARD from
the inflammasome after secondary autoconversion of caspase-1 p33/p10
releases an enzymatically active caspase-1 tetramer comprising p20/p10
subunits ([306]141–[307]143). There are two primary caspase-1 effector
routes. One is the cleavage of pro-IL-1β and pro-IL-18 by the p20/p10
subunit of active caspase-1, which results in the release of IL-1β and
IL-18 and the initiation of an inflammatory response
([308]144–[309]147). The second is for active caspase-1 to cleave and
activate the executioner gasdermin D (GSDMD), cleave and remove its
inhibitory GSDMD-C domain, and release the GSDMD-N domain (GSDMD-NT),
allowing it to generate pores in the cell membrane and initiate
pyroptosis ([310]148–[311]150).
Therefore, pyroptosis is a classical cytolytic type of PCD induced by
caspase-1 ([312]151). The pyroptosis pathway can be activated by
various viral infections ([313]64, [314]152–[315]154) and can also be
induced by autoantibodies to autoimmune diseases (AID) ([316]155,
[317]156). COVID-19 and RA share a tight relationship with the
pyroptosis mechanism, which may be one of the pathogenic mechanisms by
which COVID-19 interacts with RA to induce deterioration.
Caspase-1 in COVID-19
In this study, the top 11 hub genes pathways of COVID-19, RA, and
pyroptosis were enriched in the Network map of the SARS-CoV-2 signaling
pathway, Activation of the NLRP3 inflammasome by SARS-CoV-2, IL, NF-κB,
TNF signaling pathway and regulation of cytokine-mediated signaling
pathway. Caspase-1 activation is not only a critical effector molecule
in the development of acute respiratory distress syndrome (ARDS)
([318]157, [319]158), but it is also a major contributor to the
development of ALI ([320]159, [321]160). In peripheral blood immune
cells and tissues of COVID-19 patients, activated NLRP3 inflammasome,
caspase-1, and high levels of GSDMD-NT were found, as well as elevated
expression of IL-1β and IL-18 in serum ([322]161–[323]166). In animal
investigations, high caspase-1 expression was also detected in the
peripheral immune cells of SARS-CoV-2-infected rhesus monkeys
([324]167). With the in-depth study of the mechanism of pyroptosis
triggered by SARS-CoV-2, it was found that NSP6 in non-structural
proteins ([325]74, [326]168), N-protein ([327]169), and S-protein
([328]170) in structural proteins, and ORF3a protein ([329]171) in
auxiliary proteins all lead to overexpression and activation of NLRP3
inflammasome and caspase-1 and are positively correlated with the
severity of COVID-19 ([330]164). SARS-CoV-2 ultimately leads to an
excessive inflammatory response in the form of a “cytokine storm”
([331]172–[332]174) and severe host cell pyroptosis ([333]175).
Cytokine storm is an uncontrolled, lethal immune disease characterized
by the excessive release of pro-inflammatory cytokines and chemical
mediators from immune cells ([334]176, [335]177), capable of causing
damage to multiple organs, including the respiratory system ([336]165,
[337]178), and it is believed to be a major cause of deterioration and
death in COVID-19 patients ([338]179).
In this study, immune cell infiltration analysis of COVID-19, RA, and
the key gene for pyroptosis (CASP1) was found to be positively
correlated with Monocytes, and the reliability of the results was
verified by the HPA dataset and Monaco dataset databases. Among the
numerous immune cells, monocytes play a vital part in the cytokine
storm of COVID-19 patients ([339]180). It was demonstrated that
monocytes in COVID-19 patients are the outposts of SARS-CoV-2 invasion
via TLR sensing and can release inflammatory cytokines by assembling
NLRP3, activating caspase-1 to generate a “cytokine storm,” and
synthesizing GSDMD-NT to induce cellular pyroptosis ([340]72,
[341]168). Monocytes from COVID-19 patients not only overexpress IL-1β
and IL-18 but also show pyroptosis morphology, suggesting that
pyroptosis is a possible key mechanism for cytokine storm in COVID-19
([342]123, [343]166).
Caspase-1 in RA
The peripheral blood and synovial tissue of RA patients have been
reported to contain a high level of expression and activation of the
NLRP3 inflammasome and caspase-1, as well as a high level of expression
of IL-1β and IL-18 ([344]181–[345]183). In animal investigations,
inhibition of NLRP3 and caspase-1 was also found to be useful in
alleviating the symptoms of arthritis in RA (CIA mouse model)
([346]79). A cytokine network in the form of a cytokine storm, similar
to that in COVID-19, is also present in RA and is a major factor in the
disease’s onset, persistence, and progression ([347]184, [348]185). The
most important pro-inflammatory cytokines in RA are IL-1β and IL-18,
and the expression of these cytokines is positively correlated with
active disease status ([349]186–[350]188). In recent years the
mechanism of pyroptosis has been shown to play a key role in the
development of autoimmune diseases. In the course of the
pro-inflammatory process, activation of the pyroptosis pathway causes
host cells to release large amounts of pro-inflammatory cytokines and
directs innate immune cells to the site of injury ([351]119), which
ultimately results in an overreactive immune response akin to a
“cytokine storm” that sustains an ongoing autoimmune disease ([352]189,
[353]190).
In this study, immune cell infiltration analysis of the CASP1 in the RA
dataset revealed that its expression was positively correlated with
monocytes, dendritic cells activated, and neutrophils. It was found
that high expression of NLRP3 and activated caspase-1 was detected in
monocytes, dendritic cells, and neutrophils in the peripheral blood of
RA patients, most notably in monocytes ([354]181, [355]191, [356]192).
Blood that circulates in the periphery Monocytes from RA patients can
cleave GSDMD via the TLR4-NLRP3-caspase-1 pathway, resulting in
pyroptosis and the production of a significant variety of cytokines,
including IL-1β and IL-18, and are positively linked with disease
activity ([357]75, [358]193).
In conclusion, COVID-19 and RA are both capable of high expression of
activated caspase-1 in peripheral blood and tissues. The invasion of
SARS-CoV-2 in RA patients may enhance the caspase-1-induced pyroptosis
mechanism, creating a vicious cycle of common outbreaks of “cytokine
storm” and cell death, leading to increased hospitalization, morbidity,
and mortality ([359]194–[360]197).
The JAK-STAT pathway upstream of caspase-1
In this study, the functional enrichment of the collection of Co-genes
and the Top 11 Hub Genes included the regulation of IL-6 production,
and the Upstream Pathway of the key gene (CASP1) was closely related to
the JAK-STAT signaling pathway. The JAK/STAT pathway, also called the
IL-6 signaling pathway, can be activated by IL-6 ([361]198, [362]199),
which is also a significant indication of COVID-19 severity ([363]1,
[364]200). Activation of the JAK/STAT pathway, which produces
pro-inflammatory cytokines, also a significant role in the development
of rheumatoid arthritis (RA) ([365]201). Thus the JAK/STAT pathway is
also one of the crosstalk pathways of COVID-19 and RA ([366]202,
[367]203). JAK inhibitors, represented by Tofacitinib, have been
approved by the FDA to treat moderately and severely active RA
([368]204, [369]205). However, it increases the risk of viral infection
([370]206, [371]207). Since IFN can trigger the JAK/STAT pathway to
launch a cascade response against viral infection ([372]208), JAK
inhibitors would interfere with the natural IFN/ISG antiviral immune
system in the context of SARS-CoV-2 infection. Currently, the WHO only
advises baricitinib for the treatment of severe COVID-19 ([373]209),
and the evidence for the use of JAK inhibitors in the treatment of
COVID-19 is weak and requires additional investigation
([374]210–[375]212). Since the JAK/STAT pathway can promote caspase-1
expression and activation via cytokines (e.g., GM-CSF) and interferons
(e.g., IFN-γ) ([376]213–[377]216), this study, in conjunction with
other evidence, suggests that the NLRP3/caspase-1 pathway is a key
mechanism by which COVID-19 and RA disease exacerbate each other.
Therefore, we can look for drug targets downstream of the JAK/STAT
pathway to avoid interfering with the IFN/ISG system by inhibiting the
JAK/STAT pathway, but also to effectively inhibit the pyroptosis link,
interrupting the “cytokine storm” that erupts from each other and thus
interrupting the vicious cycle. Interestingly, caspase-1 is one of the
common crosstalk targets between JAK/STAT and pyroptosis pathways.
Minocycline and caspase-1
In the present COVID-19 pandemic, the discovery of new medications is
challenging, time-consuming, risky, and less successful, and drug
repurposing is a good option ([378]217, [379]218). Minocycline is a
second-generation semi-synthetic tetracycline derivative with a good
safety profile ([380]219). In addition to being a broad-spectrum
antibiotic ([381]220), it is also a broad-spectrum antiviral agent
(e.g., HIV, WNV, DENV) ([382]221–[383]223) and possesses
anti-inflammatory, antioxidant, anti-cell death (e.g., pyroptosis),
immunomodulatory effects in terms of non-anti-microbial action
([384]224–[385]226). Fundamental investigations have demonstrated that
minocycline inhibits caspase-1 activity in mice suffering from
traumatic brain injury (TBI) ([386]227); reduces the expression of
caspase-1 to alleviate stress-induced depression in mice ([387]228);
acts as a caspase-1 inhibitor to delay the death of mice with
Huntington’s disease ([388]229); reduces caspase-1 activity in the
retina of diabetic mice ([389]230) and suppresses caspase-1 activation
in mice with acute lung injury to reduce inflammation ([390]231).
Retrospective multicentre cohort studies have shown that minocycline
inhibits caspase-1 to reduce the incidence of acute renal failure
([391]232). In conclusion, minocycline can reduce IL-1β and IL-18
levels by selectively inhibiting caspase-1 expression and activation,
and it can have anti-inflammatory and anti-pyroptosis effects in the
lung and throughout the body. Minocycline could play an important
potential role in treating patients with COVID-19 through these
properties ([392]233) and exert a powerful antimicrobial effect against
co-infections/secondary bacterial infections in patients with COVID-19
([393]234, [394]235). A current clinical study indicates that the
combination of minocycline and favipiravir has significant efficacy and
safety in treating COVID-19 inpatients ([395]236). Minocycline has also
demonstrated efficacy in treating COVID-19 individuals who are secluded
at home ([396]237). In addition, minocycline has been known to be
clearly and effectively used in treating RA for many years
([397]238–[398]240).
Thus, minocycline can counteract the “cytokine storm” inflammatory
response and resist pyroptosis in patients with COVID-19 combined with
RA by inhibiting the expression and activation of caspase-1. This
process also indirectly demonstrates a potential caspase-1-directed
pyroptosis and a shared pro-inflammatory mechanism between COVID-19 and
RA, which requires further basic and clinical research.
Conclusions
Bioinformatic analysis revealed that COVID-19, RA, and
pyroptosis-related genes were enriched in pyroptosis and
pro-inflammatory pathways (NLR/TLR signaling pathway, NLRP3
inflammasome complex, death-inducing signaling complex, regulation of
interleukin production), natural immune pathways (activation of the
NLRP3 inflammasome by SARS-CoV-2) and COVID-19-and RA-related cytokine
storm pathways (IL, NF-κB, TNF signaling pathway and regulation of
cytokine-mediated signaling). Of these, CASP1 is involved in most
pathways. The genes related to minocycline were then obtained by
network pharmacology analysis and intersected with COVID-19, RA, and
pyroptosis to obtain the common hub gene, and then the key gene was
verified as CASP1 by two validation sets. Caspase-1 may be an important
mediator of the excessive inflammatory response induced by SARS-CoV-2
in RA patients through pyroptosis. Finally, minocycline was analyzed by
computer-aided drug design as an effective drug against the mechanism
of caspase-1-induced pyroptosis. Our study provides insight into the
causes of the high hospitalization and mortality rates of COVID-19
combined with RA from a new perspective of pyroptosis and offers
potentially effective drugs that could provide new directions for
further analysis of its pathogenesis and the development of targeted
clinical treatments.
Data availability statement
The datasets presented in this study can be found in online
repositories. The names of the repository/repositories and accession
number(s) can be found in the article/[399] Supplementary Material .
Author contributions
QZ, RL and YC: Consulted the literature and prepared materials. QZ, RL,
YC, QL, JZ and JBZ: Experimented and analyzed the data. QZ, RL and YC:
Drawn up the manuscript. WW and WX devised the concept and supervised
the study. All authors contributed to the article and approved the
submitted version.
Acknowledgments