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. 2020 Sep 9;5(1):14. doi: 10.1186/s41231-020-00066-x

C-C chemokine receptor type 5 links COVID-19, rheumatoid arthritis, and Hydroxychloroquine: in silico analysis

Mahmood Y Hachim 1, Ibrahim Y Hachim 2, Kashif Bin Naeem 3, Haifa Hannawi 3, Issa Al Salmi 4, Suad Hannawi 3,✉
PMCID: PMC7479747  PMID: 32923679

Abstract

Patients with rheumatoid arthritis (RA) represent one of the fragile patient groups that might be susceptible to the critical form of the coronavirus disease − 19 (COVID-19). On the other side, RA patients have been found not to have an increased risk of COVID-19 infection. Moreover, some of the Disease-Modifying Anti-Rheumatic Drugs (DMARDS) commonly used to treat rheumatic diseases like Hydroxychloroquine (HCQ) were proposed as a potential therapy for COVID-19 with a lack of full understanding of their molecular mechanisms. This highlights the need for the discovery of common pathways that may link both diseases at the molecular side. In this research, we used the in silico approach to investigate the transcriptomic profile of RA synovium to identify shared molecular pathways with that of severe acute respiratory syndrome-corona virus-2 (SARS-COV-2) infected lung tissue. Our results showed upregulation of chemotactic factors, including CCL4, CCL8, and CCL11, that all shared CCR5 as their receptor, as a common derangement observed in both diseases; RA and COVID-19. Moreover, our results also highlighted a possible mechanism through which HCQ, which can be used as a monotherapy in mild RA or as one of the triple-DMARDs therapy (tDMARDs; methotrexate, sulphasalazine, and HCQ), might interfere with the COVID-19 infection. This might be achieved through the ability of HCQ to upregulate specific immune cell populations like activated natural killer (NK) cells, which were found to be significantly reduced in COVID-19 infection. In addition to its ability to block CCR5 rich immune cell recruitment that also was upregulated in the SARS-COV-2 infected lungs. This might explain some of the reports that showed beneficial effects.

Introduction

Since the outbreak of Coronavirus disease-19 (COVID-19) disease, the clinical features of this disease showed significant variability between different subpopulations. Severe acute respiratory syndrome coronavirus 2, shortened to SARS-CoV-2, is the virus that causes COVID-19 disease [1]. Initially, patients with chronic conditions, as well as immunodeficiencies, were considered as high-risk groups patients for the development of the more severe form of the COVID-19 [2, 3]. Patients with rheumatoid arthritis (RA), a prevalent immune-mediated disease, are at higher risk of bacterial and viral infections due to its pathogenesis and the use of immunosuppressive agents as an RA treatment. As a result, RA patients represent one of those fragile patients groups that might be susceptible to the critical form of the COVID-19 disease [4–6].

Unexpectedly, recent reports showed that patients with RA have no increased risk of COVID-19 infection. Moreover, some of the Disease-Modifying Anti-Rheumatic (DMARDs) that commonly used to treat rheumatic diseases like Hydroxychloroquine (HCQ) were proposed as potential therapies for COVID-19 [7–10]. HCQ is used as monotherapy in mild RA cases, or it can be used as a combined treatment, particularly with methotrexate and sulphasalazine as Triple Disease Anti-Rheumatic Drugs (tDMARDs) regimen [11]. Several mechanisms were proposed for HCQ to produce its action, and this includes the anti-inflammatory effect through lysosomal acidification interference and phospholipase A2 inhibition [12, 13]. Also, HCQ was proposed to modulate the inflammatory response through its inhibition of the toll-like receptors signal as well as the T and B cell receptors leading to inhibition of their cytokine production, including the interleukin (IL)-1 and IL-6 [13, 14]. This cytokine inhibition was proposed as an essential mechanism that might explain the role of HCQ in reducing the cytokine storm critical in COVID-19 pathogenesis [15]. HCQ was also reported to inhibit viral replication [16].

The controversial results that recently linked to the efficacy of HCQ in COVID-19, in addition to the lack of full understanding of its molecular mechanisms, highlight the need for the discovery of common pathways that may link both diseases; COVID-19 and RA at the molecular side. This step is essential for the identification of possible targets that can block pathogenesis of RA and prevent severe forms of COVID-19. Also, it might help in identifying the predictive biomarkers that can help in more efficient patient stratification to predict COVID-19 patient’s responses to HCQ.

In this study, we used in silico approach to investigate the transcriptomic profile of RA synovium to identify shared molecular pathways with that of SARS-COV-2 infected lung tissue.

Materials and methods

RA synovium specific DEG

The Gene Expression Omnibus (GEO) public repository was used to retrieve the gene expression profile of synovial tissue from 33 RA, 26 osteoarthritis (OA) patients, and 20 healthy controls from three datasets (GSE55235, GSE55457, GSE55584) as previously reported [17]. Raw cell files were reanalyzed using AltAnalyze tool (20) and in house pipeline for normalization and filtration as previously described [18] to identify novel synovium related biomarkers.

tDMARDs response in RA synovium

We used the publicly available synovial tissue transcriptomic data to compare the infiltration of the immune cells at baseline and after six months of tDMARDs to identify subgroups that might not respond well to tDMARDs. RNAseq dataset (GSE97165) of synovial biopsies taken from 19 early RA (defined as within 12 months of the onset of symptoms) patients at baseline and after six months of tDMARDs treatment were retrieved and reanalyzed.

SARS-COV-2 and RA

RNAseq dataset (GSE147507) were retrieved using the GEO and used to identify Differentially Expressed Genes (DEGs) between infected and uninfected lung samples using BioJupies tools [19].

Pathways and gene set enrichment

Differentially expressed genes between the subgroups were defined, and gene set enrichment analysis was performed to identify the underlying pathways in each group using BioJupies tools. The DEGs were explored for common pathways using Metascape online tool (http://metascape.org) [10].

Estimating immune and stromal cells in the synovium

In order to achieve this goal, we used a recently available tool called ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) to estimate the difference in the infiltration of immune cells in healthy, OA and RA synovium. ESTIMATE R package was used to estimate the difference in immune cells’ infiltration between the three groups using their transcriptomic profile.

Estimating infiltrating immune cells and their activation status in the synovium

The raw RNAseq data were used for in silico prediction of the immune cells’ infiltration of the synovial tissue using CIBERSORT analytical tool to evaluate the pre versus post tDMARDs changes in the immune population and/or activation status. Then, patients were divided according to the level of alteration in immune cells percentage after the treatment. The immune cells that express a higher level of the identified receptor were explored using the Database of Immune Cell Expression (DICE) project tool (https://dice-database.org/). The expression of the chemokine receptor was searched in a microarray dataset (GSE77298) of synovial biopsies of RA and healthy controls.

Results

RA synovium express genes related to immune cells activation, migration, signaling, and response to viruses

For a better understanding of the RA disease pathogenesis, we reanalyze the gene expression profile of synovial tissue from 33 RA and compare to samples from 26 OA and 20 healthy controls. Our results showed that RA synovium expresses a specific signature that can differentiate it clearly from OA as well as healthy controls. This includes cytokine-mediated signaling pathway, positive regulation of cytokine production, Interleukin-2 family signaling, T cell receptor signaling pathway, leukocyte migration, negative regulation of chemotaxis, cellular response to interleukin-1, T cell activation, and regulation of morphogenesis of an epithelium. Moreover, pathways related to defense response to other organisms, antigen processing and presentation of peptide antigen via major histocompatibility complex (MHC) class I and response to the virus were also enriched specifically in RA synovium. (Fig. 1, Tables 1 and 2).

Fig. 1.

Fig. 1

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Comparison between the synovium transcriptomics profile of rheumatoid arthritis (RA) patients versus healthy controls (N) and osteoarthritis (OA). a principle component analysis (PCA) showing that the top selected DEGs can cluster the groups precisely b Heatmap of the top markers that can differentiate the three groups and c shows top pathways enriched in RA specific markers identified

Table 1.

Top Genes that are specific to healthy, OA, and RA synovium

ID Markers Specific to Healthy Markers Specific to OA Markers Specific to RA
Probeset_id Gene Name Probeset_id Gene Name Probeset_id Gene Name
1 204180_s_at ZBTB43 204284_at PPP1R3C 210538_s_at BIRC3
2 204131_s_at FOXO3 217963_s_at NGFRAP1 217933_s_at LAP3
3 213649_at SFRS7 219197_s_at SCUBE2 204279_at PSMB9
4 222303_at 212256_at GALNT10 216920_s_at TARP
5 204243_at RLF 203478_at NDUFC1 211798_x_at IGLJ3
6 222164_at FGFR1 205330_at MN1 217281_x_at IGHV3–7
7 206359_at SOCS3 218126_at FAM82A2 209924_at CCL18
8 201160_s_at CSDA 210534_s_at B9D1 217179_x_at
9 209682_at CBLB 202016_at MEST 214973_x_at IGHD
10 215330_at 204776_at THBS4 209267_s_at SLC39A8
11 219228_at ZNF331 204797_s_at EML1 218223_s_at PLEKHO1
12 204748_at PTGS2 201842_s_at EFEMP1 211644_x_at IGKV3–20
13 210764_s_at CYR61 205364_at ACOX2 205159_at CSF2RB
14 218859_s_at ESF1 214620_x_at PAM 212956_at TBC1D9
15 201465_s_at JUN 210997_at HGF 206247_at MICB
16 220046_s_at CCNL1 219953_s_at C11orf17 217378_x_at LOC100130100
17 200921_s_at BTG1 208792_s_at CLU 205488_at GZMA
18 202768_at FOSB 210302_s_at MAB21L2 211643_x_at
19 209184_s_at IRS2 219182_at FLJ22167 205569_at LAMP3
20 213462_at NPAS2 215913_s_at GULP1 211637_x_at IGHV4–4
21 200702_s_at DDX24 37408_at MRC2 205831_at CD2
22 218880_at FOSL2 213167_s_at SLC5A3 213716_s_at SECTM1
23 210094_s_at PARD3 207326_at BTC 209670_at TRAC
24 207316_at HAS1 207447_s_at MGAT4C 206991_s_at CCR5
25 210180_s_at SFRS10 222125_s_at P4HTM 214916_x_at
26 208707_at EIF5 206439_at EPYC 216401_x_at LOC652493
27 220266_s_at KLF4 205127_at PTGS1 214768_x_at FAM20B
28 212501_at CEBPB 218837_s_at UBE2D4 217480_x_at LOC339562
29 202340_x_at NR4A1 209466_x_at PTN 204891_s_at LCK
30 211458_s_at GABARAPL1 205150_s_at KIAA0644 211645_x_at
31 201473_at JUNB 205898_at CX3CR1 212314_at KIAA0746
32 212384_at BAT1 212713_at MFAP4 205267_at POU2AF1
33 200800_s_at HSPA1A 205817_at SIX1 219648_at MREG
34 202014_at PPP1R15A 201279_s_at DAB2 210915_x_at TRBC1
35 204622_x_at NR4A2 206070_s_at EPHA3 216576_x_at IGKC
36 210852_s_at AASS 205857_at SLC18A2 217258_x_at IGL@
37 202861_at PER1 205638_at BAI3 213915_at NKG7
38 222162_s_at ADAMTS1 206373_at ZIC1 204613_at PLCG2
39 215248_at GRB10 220595_at PDZRN4 221658_s_at IL21R
40 214805_at EIF4A1 218675_at SLC22A17 202307_s_at TAP1
41 201810_s_at SH3BP5 217511_at KAZALD1 203528_at SEMA4D
42 202948_at IL1R1 206726_at PGDS 203828_s_at IL32
43 212732_at MEG3 204933_s_at TNFRSF11B 201690_s_at TPD52
44 217911_s_at BAG3 211958_at IGFBP5 214777_at IGKV4–1
45 200768_s_at MAT2A 221447_s_at GLT8D2 216207_x_at IGKV1D-13
46 221031_s_at APOLD1 205833_s_at PART1 206082_at HCP5
47 202672_s_at ATF3 203440_at CDH2 208885_at LCP1
48 212227_x_at EIF1 204749_at NAP1L3 1405_i_at CCL5
49 203752_s_at JUND 221029_s_at WNT5B M97935_3_at STAT1
50 202431_s_at MYC 207497_s_at MS4A2 204116_at IL2RG
51 213006_at CEBPD 210372_s_at TPD52L1 209374_s_at IGHM
52 201531_at ZFP36 210006_at ABHD14A 209606_at CYTIP
53 203140_at BCL6 220076_at ANKH 204533_at CXCL10
54 36711_at MAFF 213195_at LOC201229 202270_at GBP1
55 208869_s_at GABARAPL1 204773_at IL11RA 219386_s_at SLAMF8
56 209681_at SLC19A2 219416_at SCARA3 205890_s_at GABBR1
57 212665_at TIPARP 206089_at NELL1 205242_at CXCL13
58 202284_s_at CDKN1A 219561_at COPZ2 206134_at ADAMDEC1
59 209305_s_at GADD45B 206480_at LTC4S 203915_at CXCL9
60 203574_at NFIL3 205475_at SCRG1 206513_at AIM2
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Table 2.

Top Pathways enriched in the DEGs specific to RA compared to healthy and OA

Category Term Description LogP Log(q-value) InTerm_InList Symbols
GO Biological Processes GO:0098542 defense response to other organism −13.0634 −8.744 16/596 BIRC3, GBP1, IGHD, IGHM, IGKC, CXCL10, MICB, CXCL9, STAT1, AIM2, CXCL13, IGHV3–7, TRBC1, IGKV3–20, SLAMF8, IGLL5, CCR5, CSF2RB, GZMA, PLCG2, CCL5, POU2AF1, IGKV4–1, LCK, PSMB9, CD2, LCP1, TPD52, IL21R, TAP1, LAMP3, IGKV1D-13, IL2RG
GO Biological Processes GO:0050900 leukocyte migration −11.5986 −7.757 14/504 CD2, CCR5, IGHM, IGKC, CXCL10, LCK, CXCL9, CCL5, CCL18, CXCL13, IGHV3–7, IGKV4–1, IGKV3–20, SLAMF8, CSF2RB, IL2RG, IL21R, STAT1, PLCG2, SLC39A8, SEMA4D, PLEKHO1, FAM20B, ADAMDEC1, GABBR1, AIM2, BIRC3, NCOR2, GBP1
GO Biological Processes GO:0019221 cytokine-mediated signaling pathway −11.1576 −7.441 16/796 BIRC3, CCR5, CSF2RB, GBP1, IL2RG, CXCL10, LCP1, CXCL9, PSMB9, CCL5, CCL18, STAT1, IL32, AIM2, CXCL13, IL21R, LCK
Reactome Gene Sets R-HSA-451927 Interleukin-2 family signaling −7.38881 −4.517 5/44 CSF2RB, IL2RG, LCK, STAT1, IL21R, CCR5, GZMA, TAP1
GO Biological Processes GO:0009615 response to virus −6.22631 −3.606 8/334 BIRC3, GBP1, CXCL10, MICB, CXCL9, CCL5, STAT1, AIM2, PSMB9, CCL18, PLCG2, SLAMF8, LCK, LAMP3, CCR5, TAP1
GO Biological Processes GO:0050852 T cell receptor signaling pathway −4.11131 − 1.834 5/202 GBP1, LCK, PLCG2, PSMB9, TRBC1, CD2, TPD52, SLAMF8, BIRC3, STAT1, MICB
GO Biological Processes GO:0050922 negative regulation of chemotaxis −3.43346 −1.270 3/64 SEMA4D, CXCL13, SLAMF8, GBP1, LCK, CCL5, CYTIP, ADAMDEC1, CCL18, AIM2, TBC1D9, SLC39A8, CXCL10, PLCG2, MICB
GO Biological Processes GO:0001819 positive regulation of cytokine production −3.28901 −1.157 6/467 BIRC3, CD2, IGHD, PLCG2, STAT1, AIM2, GBP1, CCL5, LCP1
GO Biological Processes GO:0071347 cellular response to interleukin-1 −3.19668 −1.073 4/180 GBP1, PSMB9, CCL5, CCL18, STAT1
GO Biological Processes GO:0002474 antigen processing and presentation of peptide antigen via MHC class I −2.85603 −0.782 3/101 MICB, PSMB9, TAP1
GO Biological Processes GO:0042110 T cell activation −2.4476 −0.425 5/472 CD2, LCK, LCP1, MICB, CCL5
GO Biological Processes GO:1905330 regulation of morphogenesis of an epithelium −2.14316 −0.163 3/181 CXCL10, PSMB9, STAT1, LCK, NCOR2
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RA synovium express higher CCL5 and its receptor CCR5

Next and in order to investigate the role of the main cytokines that control the immune response including cell number, activation, maturation, differentiation, and migration, we filtered the top DEGs between the three groups (healthy, OA, and RA) to look for chemokines and interleukins only. Interestingly, RA synovium showed significantly higher expression of important chemokines ligands (CCL18, CXCL9, CXCL10, CXCL13 CCL5, and its receptor CCR5). Moreover, RA synovium expresses higher interleukins related genes (IL21R, IL32, IL2RG) (Table 3).

Table 3.

Top chemokine and interleukins related genes in the DEGs specific to RA compared to healthy and OA

Groups Symbol log_fold-OA_vs_N adjp-OA_vs_N log_fold-RA_vs_N adjp-RA_vs_N log_fold-RA_vs_OA adjp-RA_vs_OA ANOVA-rawp ANOVA-adjp largest fold
Healthy IL1R1 −1.04318 3.14E-06 −1.08606 4.87E-08 −0.04288 0.835425 1.84E-12 2.15E-10 1.086057
OA CX3CR1 2.285902 9.5E-11 1.231936 2.49E-05 −1.05397 1.18E-05 3.35E-15 1.12E-12 2.285902
OA IL11RA 0.938146 1.54E-05 −0.08216 0.711224 −1.02031 4.32E-10 1.52E-11 1.25E-09 1.020306
RA CCL5 0.164174 0.521514 1.630079 3.64E-09 1.465904 1.42E-09 1.17E-15 4.92E-13 1.630079
RA CCR5 0.34399 0.043302 1.251077 1.01E-10 0.907087 9.01E-08 2.75E-15 9.57E-13 1.251077
RA CCL18 0.355375 0.432754 2.165572 3.85E-09 1.810197 6.47E-09 1.86E-13 3.26E-11 2.165572
RA CXCL9 0.177374 0.57414 2.881994 7.72E-11 2.704619 7.48E-13 8.75E-21 6.58E-17 2.881994
RA CXCL10 0.462931 0.022287 2.326323 1.16E-10 1.863391 5.94E-09 1.66E-17 1.91E-14 2.326323
RA CXCL13 0.46977 0.094643 3.919964 4.83E-12 3.450194 5.31E-11 8.86E-21 6.58E-17 3.919964
RA IL2RG 0.212245 0.311266 1.6011 7.46E-10 1.388855 8.68E-10 1.23E-16 8.85E-14 1.6011
RA IL32 0.40024 0.056249 1.659554 3.32E-10 1.259314 4.85E-09 4.25E-16 2.37E-13 1.659554
RA IL21R 0.08408 0.495735 1.135477 1.03E-07 1.051397 2.02E-08 9.69E-15 2.58E-12 1.135477
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RA synovium showed a higher infiltration of plasma cells, CD4 memory T cells, and gamma delta T cells but less dendritic and activated NK cells

In order to decipher the effect of infiltrating immune cells to the synovium and their status of activation, which might mask the local gene expression and can explain the dynamics of immune cells in disease pathophysiology, we explored the immune infiltration using in silico tools. RA synovium showed a significantly higher level of infiltrating immune cells compared to OA and healthy controls confirming the DEGs and pathways enrichment results. Specifically, RA synovium showed higher infiltration of plasma cells, CD4 memory T cells, and gamma delta T cells but less dendritic and activated NK cells (Fig. 2).

Fig. 2.

Fig. 2

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Estimating immune cells infiltration in the synovium using transcriptomics profile of rheumatoid arthritis (RA) patients versus healthy controls (N) and osteoarthritis (OA). We used the ESTIMATE tool to estimate the difference in the infiltration of immune cells in healthy, OA, and RA synovium using their transcriptomic profile. The raw RNAseq data were used for in silico prediction of the immune cells’ infiltration of the synovial tissue using CIBERSORT analytical tool to evaluate changes in the immune population and/or activation status between the groups

SARS-COV-2 infected lungs express more CCL4, CCL8, and CCL11 that share CCR5 as a common receptor

Next, we tried to understand some of the molecular mechanisms involved in SARS-COV-2 pathogenesis with potential interaction with the mechanisms and pathways involved in RA. Eighty-four DEGs were identified between uninfected and COVID-19 infected lung samples. These DEGs were enriched in pathways specific to (response to the virus, response to interferon, leukocyte activation, and chemotaxis) Interestingly, SARS-COV-2 infected lungs express more CCL4, CCL8, and CCL11; the three ligands shared the same receptor, which is CCR5 (Fig. 3). Top immune cells that express CCR5 were CD4 T memory T reg cells, Th17, Th1, and monocytes.

Fig. 3.

Fig. 3

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Flowchart for identification of DEGs between SARS-CoV-2 infected and uninfected lung samples using RNAseq dataset (GSE147507) retrieved from GEO using BioJupies tools. The flow of transcriptomics reanalysis, identification of chemokines, their common receptors, and immune cells with high receptor are summarized

tDMARDs treatment in early RA increase synovial activated natural killers and resting mast cells but decrease plasma cells and M1 macrophages

Next, we tried to investigate the effect of tDMARDs on immune modulation, which might improve our understanding of its role in the treatment of RA as well as other diseases like COVID-19 infection. To achieve this, we investigated the effect of the treatment of tDMARDs on different immune cell populations of the synovium. Our results showed that four immune cell populations were significantly changed after six months of tDMARDs. This includes the resting mast cells and activated NK cells that were shown to be increased by 84 and 74% of patients, respectively. On the other hand, M1 macrophages and plasma cells were decreased after treatment in 68 and 58% of patients, respectively (Fig. 4).

Fig. 4.

Fig. 4

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Effect of tDMARDs Treatment In Early RA synovial immune cells profile. We used the publicly available synovial tissue transcriptomic data to compare the infiltration of the immune cells at baseline and after six months of tDMARDs to identify subgroups that might not respond well to tDMARDs. RNAseq dataset (GSE97165) of synovial biopsies taken from 19 early RA (defined as within 12 months of the onset of symptoms) patients at baseline and after six months of tDMARDs treatment were retrieved and reanalyzed. ANOVA test was used

DMARDs can block RA pathogenic CCR5 rich immune cell recruitment

Further analysis confirmed our previous finding that CCR5 was significantly upregulated in RA compared to healthy controls synovium (p = 0.04), Fig (5a). Moreover, our results also showed that this receptor was dramatically downregulated after six months of tDMARDs treatment (p = 0.004), as shown in Fig. (5b). Those results highlighted a possible beneficiary effect of DMARDs in patients with COVID-19, through its ability to block CCR5 rich immune cell recruitment that we already found to be upregulated in the SARS-COV-2 infected lungs.

Fig. 5.

Fig. 5

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CCR5 expression in synovial biopsies of RA and control and CCR5 expression at baseline and after six months of tDMARDs treatment. The expression of the chemokine receptor was searched in a microarray dataset (GSE77298) of synovial biopsies of RA and healthy controls. A paired T-test was used for comparison

Discussion

Since the outbreak of COVID-19 infection, it was evident that this disease had a variable clinical impact on different subpopulations [2, 3]. Due to the immune dysregulation as well as the use of immune-modulating treatments, patients with rheumatic diseases were considered among the fragile subpopulations that might suffer from the more aggressive form of COVID-19 [4–6]. Interestingly, a group of disease-modifying anti-rheumatic drugs (DMARDS), including HCQ and IL6 inhibitors such as tocilizumab, was also proposed as a possible therapeutic option to treat COVID-19 patients [20]. However, the mechanisms through which those agents produce their effect is not fully understood.

Chloroquine and hydroxychloroquine showed antiviral characteristics in vitro, and some reports showed their efficacy in the treatment of COVID-19 [8]. It is suggested that these drugs interfere with lysosomal activity, membrane stability, signaling pathways, and immune-related transcriptional activity [21].

Therefore, a better understanding of the relationship between RA and its associated therapies and COVID-19 disease might help to improve the response to COVID-19 pandemic. Our results here highlight a possible link between RA and COVID-19, which might explain the molecular basis of the benefits of some of the DMARDS used for treating COVID-19 infection.

Indeed, SARS-COV-2 infected lungs showed upregulation of chemotactic factors, including CCL4, CCL8, and CCL11, that all shared CCR5 as their receptor. This receptor is mainly expressed in the CD4 T memory, T reg cells, Th17, Th1, and monocytes.

Recent reports showed the importance of this receptor in the pathogenesis of RA. Indeed, CCR5 were found to be highly expressed in RA synovium, in addition to massive infiltration of the synovium with T helper cell type 1 inflammatory cell [22].

Our results showed that lungs infected with SARS-CoV-2 express higher levels of CCL4, CCL8, and CCL11. CCL4 exhibit chemoattractive ability towards different cell types, including immune cells, and coronary endothelial cells [23]. CCL4 and its receptor CCR5 were reported to be significantly induced in the infarct myocardium, vulnerable atherosclerosis plaques, advanced atherosclerotic lesions, and to be associated with a higher risk of stroke and cardiovascular events [23]. The other chemokines ligand CCL8 is known to recruits further neutrophils to the infarct to release MMPs and soluble IL-6 [24]. CCL11 bind CCR3 to stimulates the migration of immune cells like neutrophils [25] and was shown to recruit such cells to the heart and contribute to myocardial fibrosis [26].

The pathogenesis of RA is suggested to involve Th1-type T cells that preferentially express CCR5 where its chemokines ligands (macrophage inflammatory protein (Mip)-1α, CCL3; and Mip-1β, CCL4) participate in selective recruitment of CCR5 + CXCR3+ T cells to the inflamed synovium [27]. The infiltration of such IFN-γ secreting CCR5 + CD4+ T cells into the RA joint cavity is regulated by the synovial microenvironment [28]. On the other hand, CCR5 silencing suppresses inflammatory response in RA by inhibiting synovial cell viability but promoting apoptosis [29]. Another source of CCR5 in RA are Vδ2 T cells which infiltrated into the synovium under the influence of high levels of TNF-α [30]. Moreover, an in vivo model using a non-functional form of the CCR5 receptor (CCR5-Δ32) was shown to protect against RA [31, 32]. Carriers of the CCR5-Δ32 allele were at a significantly higher frequency in non-severe compared to severe patients making it a genetic marker related to the severity of RA [33].

In COVID-19 patients, disruption of the CCL5-CCR5 axis through CCR5 blocking antibody leronlimab was shown to reduce plasma IL-6, and SARS-CoV-2 plasma viremia [34]. For that reason, leronlimab is currently under investigation in a Phase2b/3 for severely ill COVID-19 patients [35]. Interestingly, the CCR5 Δ32 allele was found to be an important genetic marker of SARS-CoV-2 related death [36].

The similarity that we observe here in the pathogenesis of both diseases might provide evidence about the molecular pathways through which many of the commonly used drugs for RA treatment are proposed to have benefits in COVID-19 management [4].

Another observation we notice here is the finding that the tDMARDs used for RA treatment was able to significantly upregulate some immune cell populations, including resting mast cells and activated NK cells. The recent observation that during the COVID-19 infection, the main lymphocyte populations, including NK cells, were remarkably decreased, and this decrease was more prominent in the severe cases of COVID-19 infection compared to mild cases as well as healthy controls [37, 38]. Moreover, another report also revealed that NK cells, in addition to the CD8+, were found to be important in modulating the anti-COVID-19 response [39].

This might explain the recent findings that patients with chronic arthritis treated with different forms of DMARD showed no evidence of increased risk of life-threatening or respiratory complications following the COVID-19 infection compared to the general population [4].

On the other hand, our reanalysis showed that tDMARDs significantly decrease the M1 macrophages and plasma cells, as shown in Fig. 4. It is known that the number and the level of activation of inflamed synovial macrophages correlate significantly with the severity of RA [40]. In RA, synovium can forms a niche for potentially autoreactive—B cells and plasma cells that play a central role in RA pathogensis [41]. The ability of tDMARDs to block these cells can explain its anti-RA effects.

Lung macrophages in severe COVID-19 infection orchestrate local inflammation by recruiting inflammatory monocytic cells and neutrophils, whereas, in moderate COVID-19 infection, macrophages produce more T cell-attracting chemokines [42]. SARS-CoV-2 infection of alveolar macrophage can drive the “cytokine storm” that further damages multiple organs other than the lung, as in the case of heart and kidney [43].

During SARS-CoV-2 infections, immune cell subsets change, and among the B cells, the plasma cells increased remarkably, whereas the naïve B cells decreased [44]. Interestingly, one of the characteristics of the formation of SARS-CoV-2 anti-virus antibodies in a trial to limit viral replication is that these protective antibodies will cause friendly damage by the binding of the virus-Ab complex to FcR on monocytes/macrophages induces pro-inflammatory responses that end up with the accumulation of pro-inflammatory M1 macrophages in the lungs escalating lung injury [45].

The ability of tDMARDs to significantly decrease the M1 macrophages and plasma cells can suggest that such drugs can be beneficial only in those who develop severe to moderate disease and have secondary antiviral antibodies, and this can explain why not all patients receiving such therapy are benefited from them.

In contrast, our results demonstrate a possible mechanism through which HCQ as a member of DMARDs might help in the management of COVID-19 infection, Fig (6). The possible role SARS-COV-2 infected lungs chemokines in recruiting CCR5 rich immune cells. Epithelial cells secrete three chemokines that recruit immune cells that stimulate Th17 and Th1 profile to kill the virus but recruit inflammatory to the area. Infected epithelium can stimulate plasma cells to secrete antiviral Ab that stimulates local macrophages to have an inflammatory M1 profile. tDMARDs can be helpful in the COVID-19 scenario by blocking CCR5 expression on immune cells plus inhibiting plasma and M1 macrophages while enhancing NK cells to kill the virus.

Fig. 6.

Fig. 6

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A working hypothesis for tDMARDs and COVID-19 interactions. The possible role of (1) SARS-COV-2 infected lungs (2) chemokines in recruiting (3) CCR5 rich immune cells. Epithelial cells secrete three chemokines that recruit immune cells that stimulate Th17 and Th1 profile to kill the virus but recruit inflammatory to the area. Infected epithelium can stimulate (4) plasma cells to secrete antiviral Ab that can (5) stimulate local macrophages to have an inflammatory M1 profile. tDMARDs can be helpful in the COVID-19 scenario by blocking CCR5 expression on immune cells plus inhibiting plasma amd M1 macrophages while enhancing NK cells to kill the virus

Some issues to be considered carefully based on our results is that tDMARDs effect on CCR5 can inhibit Regulatory T (Treg) recruitment, which is required to inhibit the immune response and were reported to be reduced in severe COVID-19 patients [46]. Such an effect of HCQ might hamper innate and adaptive antiviral immune responses leading to growing uncertainty about these agents for the treatment of COVID-19 [47].

Conclusion

In summary, our results highlight common pathways that are involved in the pathogenesis of RA as well as COVID-19. Those pathways might represent ideal targets for the discovery of more efficient and targeted therapeutic options to treat RA and COVID-19. Besides, it might help to improve our understanding of the mechanisms through which some of the medications are already used to treat COVID-19 infection, including the HCQ.

Acknowledgments

We would like to thank all our patients for their patience. Also, we highly appreciate our colleagues and staff for their tremendous hard work during this crisis. In addition, we extend our gratitude to all staff at the Research Ethics Committee and the Information Technology Department for their help.

Abbreviations

C-C

chemokine

COVID-19

coronavirus disease − 19

RA

rheumatoid arthritis

SARS-COV-2

severe acute respiratory syndrome-corona virus-2

tDMARDs

triple-DMARDs therapy; methotrexate, sulphasalazine, and HCQ

NK

natural killer cells

DMARDs

Disease-Modifying Anti-Rheumatic Drugs

HCQ

Hydroxychloroquine

IL

interleukin

GEO

Gene Expression Omnibus

OA

osteoarthritis

DEGs

Differentially Expressed Genes

ESTIMATE

Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data

DICE

Database of Immune Cell Expression

MHC

major histocompatibility complex

Authors’ contributions

All authors have contributed equally. The authors read and approved the final manuscript.

Funding

No funding.

Availability of data and materials

Data generated in the study are included in the tables.

Ethics approval and consent to participate

The study was approved by the Scientific Research Committee MOHAP/DXB-REC/MMM/NO.44/2020 and certify that the study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments ethical standards.

Consent for publication

All authors have agreed to the publication and to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Competing interests

All authors declare no conflict of interest related to the current manuscript.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Data Availability Statement

Data generated in the study are included in the tables.

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