Abstract

   Synovial fibroblasts have emerged as critical underlying factors to
   perpetuate chronic joint inflammation in Rheumatoid Arthritis. Like any
   other cell, synovial fibroblasts are covered with a complex layer of
   glycans that can change in response to extracellular signals, such as
   inflammation. We have previously shown that inflammatory synovial
   fibroblasts show decreased levels of sialic acid, but our understanding
   of sialic acid-dependent pathophysiological pathways in these stromal
   cells is still very limited. In this report, we used in vivo and in
   vitro studies with exogenous sialidases and RNA sequencing to
   investigate the responses of murine synovial fibroblasts upon
   desialylation. Our results show that hyposialylated fibroblasts present
   a dysregulated migratory ability and an activated phenotype
   characterized by the expression of inflammatory mediators, such as
   cytokines and chemokines, and anti-viral related mechanisms. Removal of
   surface sialic acid also affected the expression of sialyltransferases,
   revealing the existence of a positive feedback to sustain reduced
   sialylation. Moreover, we demonstrate that synovial fibroblasts subsets
   have distinct sialyltransferase expression profiles, both in healthy
   and arthritic mice. These findings underline the ability of sialic acid
   to modulate homeostatic and inflammatory responses in non-immune
   synovial fibroblasts, suggesting that sialylation plays a key role in
   perpetuating local inflammation in the arthritic joint.

   Keywords: synovial fibroblast (FLS), sialic acid, glycoimmunology,
   rheumatoid arthritis, inflammation, migration, cytokines

Introduction

   Rheumatoid Arthritis (RA) is an autoimmune joint condition that causes
   pain, swelling and stiffness in the joints, the result of an ongoing
   chronic inflammatory process. Inflammation is however an essential
   defensive instrument of the human body, starting protective responses
   and subsequent healing processes to restore tissue homeostasis.
   Dysregulation of these mechanisms lead to a persistent inflammatory
   phenotype characteristic of chronic RA, whose primary target is the
   joint synovium, the soft tissue that lines the inner surfaces of
   diarthrodial joints. In health, this membrane nourishes the cartilage
   and bone tissue. Nonetheless, the synovial membrane becomes noticeably
   thicker in RA promoting immune cell infiltration and cartilage and bone
   damage ([31]1). Genetic, epigenetic, and environmental factors can
   contribute to disease initiation ([32]2) but specific mechanisms are
   still unclear. Equally importantly, we still do not fully understand
   why inflammation persists in RA, although recent research in the field
   of stromal immunology has shown that non-immune cells such as Synovial
   Fibroblasts (SFs) play a critical role in perpetuation of RA. SFs are a
   basic part of the synovium and they become activated during
   inflammatory arthritis, secreting cytokines, such as IL-6, GM-CSF and
   chemokines like Cxcl10, Ccl2, IL-8 that attract macrophages,
   neutrophils and lymphocytes ([33]3–[34]6). SFs also support ectopic
   tertiary lymphoid structures to continue aberrant immune responses in
   the joint ([35]7). These recruited immune cells increase the local
   concentration of TNFα, IL-1β or IL-17, that continue to promote SF
   activation ([36]8–[37]10) generating pathogenic self-perpetuating
   inflammatory loops. Furthermore, SFs become hyperproliferative, migrate
   to bone and cartilage inducing tissue damage, secrete matrix-degrading
   enzymes and RANKL and promote local angiogenesis upon VEGF secretion
   ([38]11–[39]13). Interestingly, recent findings based on single cell
   RNA sequencing have demonstrated the existence of distinct SFs subsets
   with specific anatomical location within the synovium and
   non-overlapping effector functions ([40]14, [41]15). For example,
   FAPα+CD90+ SFs found in the sublining synovium lead the immune effector
   function, whereas lining FAPα+CD90− SFs drive cartilage destruction
   ([42]16). Nevertheless, there is still a lack of clinical targets to
   intervene SFs in the clinic.

   Loss of inhibitory or regulatory signalling is a critical mechanism to
   trigger autoimmunity and chronic inflammation. At the heart of many of
   these signals is the cell glycome, which comprises the entire pool of
   glycans found at the cell-cell interface. The outermost monosaccharide
   decorating glycans in humans is usually a molecule belonging to the
   Sialic Acid (SA) family. Given its location, negative charge and
   hydrophilicity, SA modulates a wide variety of pathological processes.
   An increasing body of evidence supports the hypothesis that SA acts an
   immune check-point ([43]17), as high SA levels can deliver
   anti-inflammatory or tolerogenic signals, whereas low concentration of
   SA is linked to inflammation. Corroborating this theory, multiple
   cancer cells overexpress α2,3, α2,6, and α2,8 linked SA to evade immune
   responses, inhibiting NK, T and B cells via SA-Siglec signalling
   ([44]18). On the other hand, deficiency of CD22 (Siglec-2) and Siglec G
   leads to hyperactivated B cells and autoimmunity ([45]19, [46]20),
   including exacerbation of experimental arthritis and lupus ([47]21).
   Besides, activation of TLR-NFκB-mediated responses in immune cells
   appears to be associated with a reduction of SA on the cell surface
   upon sialidase activity ([48]22–[49]25). This agrees with our previous
   results, where we have shown that TNFα-mediated down-regulation of α2-6
   sialylation is a hallmark of activated SFs in experimental arthritis
   ([50]26). In this report, we first describe the sialylation pathways
   associated to distinct SFs subsets in healthy and arthritic mice.
   Second, we investigate the responses of murine SFs upon in vitro cell
   surface desialylation, hypothesising that enzymatic removal of sialic
   acid in healthy SFs cell surface would trigger intracellular signalling
   to initiate inflammatory responses. Our data indicate that desialylated
   SFs rapidly adopt a phenotype reminiscent of the SFs found in the
   arthritic joint, characterised by enhanced cell migration, activation
   of NFkB-mediated pathways and antiviral responses. Interestingly,
   removal of SA also modulates mRNA expression of the sialyltransferases
   ST6Gal1 and ST3Gal3, suggesting that environmental or temporal changes
   affecting SA content may be perpetuated in time to consolidate local
   inflammation.

Methods

Ex Vivo Culture of SFs

   Isolation and ex vivo expansion of murine SFs was done as previously
   described ([51]27). Briefly, paws were harvested from mice, skin and
   soft tissue were removed, synovial tissue was dissected and digested
   with type II collagenase (1 mg/ml; Sigma #C6885) for 80 minutes at
   37°C. Samples were vortexed vigorously to release cells and
   centrifuged. For ex vivo expansion, cells were resuspended in DMEM
   (#21969-035) supplemented with 10% fetal calf serum (FCS; #10270106),
   1% penicillin and streptavidin (#15140122), 1% L-glutamine (#25030-024)
   and 1% NEAA (#11140-035, all from Invitrogen, UK) and cultured in 5%
   CO2 at 37°C for 24 hours, when culture medium was replaced. Media was
   changed twice a week and the cells were passaged at 90% confluence
   using trypsin-EDTA (Invitrogen, #25300-054). Prior to experimental
   setup, expression of Podoplanin (PDPN, Biolegend, #156204) and CD11b
   (Invitrogen, #11-0112-85) was assessed by flow cytometry. Myeloid
   CD11b+ cells were labelled with biotinylated anti-CD11b antibody
   (Biolegend, #101204) and subsequently depleted using Streptavidin
   magnetic MicroBeads (MACS Miltenyi Biotec, #130-090-485). For in vitro
   cytokine stimulation, recombinant IL-1β was used at the indicated
   concentrations for 6 hours in cDMEM.

Collagen-Induced Arthritis (CIA) Mouse Model

   8-10 weeks male DBA/1 mice were purchased from Envigo (UK) and
   maintained in the Biological Services Unit of University of Glasgow in
   according to the Ethics Review Boards (AWERB) of University of Glasgow
   and the Home Office UK licences PIL IF5AC4409 and PPL P8C60C865. Mice
   received 100 μg of chicken type II collagen (MD Bioproducts
   #804002-Sol) emulsified with an equal amount of complete Freund’s
   adjuvant (CFA, MD Bioproducts #501009) on day 0 via intradermal
   injection above the tail base. On day 21, mice were injected
   intraperitoneally with 100 μg collagen in PBS. Mice were monitored
   every two days for body weight, paw thickness and clinical scores.
   Clinical scores were assigned according to clinical signs, using a
   scale from 0 to 4 for each paw. An overall score exceeding 10 or weight
   loss exceeding 20%, paw thickness exceeding 4.5 mm or more than three
   inflamed paws was considered as an experimental endpoint and the mouse
   was immediately euthanized.

FACS Sorting of SFs Subsets

   Cells from mouse synovium were obtained as described above for cell
   culture, with the addition of DNase I (1 mg/ml; Sigma #DN25) during
   collagenase digestion. Cells were then resuspended in red cell lysis
   buffer for 3 min at room temperature, and red cell lysis was stopped by
   adding 20 ml of cold PBS. Cells were then centrifuged and stained with
   flexible viability dye eFluor 780 (Invitrogen #65-0865-14) at 1 μg/ml
   in PBS for 20 min on ice to discriminate live and dead cells. FC
   receptor was blocked using CD16/CD32 specific antibody (Invitrogen,
   #14-0161-85) for 20 min on ice. Cells were then incubated with primary
   antibodies or isotype controls at 1 μg/ml in FACs buffer (PBS 1%FBS 2
   mM EDTA) for 20 min at 4°C. Antibodies used were: anti-CD31PE
   (Invitrogen, #12-0311-81), anti-CD45-PE (Biolegend, #103106),
   anti-CD90-FITC (Biolegend, #105316), anti-PDPN-A647 (Biolegend,
   #156204), anti-rat IgG2b-PE (BD bioscience #25393), anti-rat IgG2a-PE
   (Biolgend, # 400508), anti-rat IgG2b-FITC (Biolegend #400634) and
   anti-rat IgG2a-APC (Biolegend, #400512). Cell sorting was performed
   using FACS Aria III or FACS Aria IIu (all from BD), data were analyzed
   with FlowJo software 10.7.1.

Desialylation of Synovial Fibroblasts In Vitro

   To hydrolyse sialic acid, SFs were cultured until reaching 90%
   confluence. Cells were washed three times with cold PBS and incubated
   for 1 hour with 100 mU Neuraminidase from Clostridium perfringens (CP,
   Roche, #11585886001) diluted in sialidase buffer (PBS: RPMI 1640 = 1:1,
   pH=6.8) or with sialidase buffer only (negative control, NT). Cells
   were washed with cold PBS prior to assessing the removal of sialic acid
   by lectin staining, or washed and cultured with cDMEM 10% FCS for
   further RNA extraction. RNA was isolated as explained below, and used
   for RNA-Seq or RT-PCR experiments.

RNA Isolation and RT-qPCR

   RNA from SFs was isolated using either RNeasy Micro Kit (Qiagen #74004)
   or EZ-10 RNA Mini-Preps Kit (Bio Basic #BS88136) according to the
   manufacturer’s instructions. Reverse transcription was performed using
   High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific
   #4368814) and RT-qPCR was performed using TaqMan™ Gene Expression Assay
   (Applied Biosystem). The expression of actin mRNA was used as an
   endogenous control to normalise samples. Taqman predesigned primers
   (Applied Biosystem) were: Actb/Mm02619580_g1; IL-6/Mm00446190_m1;
   CCL2/Mm00441242_m1; MMP3/Mm00440295_m1; MMP13/Mm00439491_m1;
   TNFRSF11b/Mm00435454_m1; TNFSF11/Mm0041906_m1; St6gal1/Mm00486119_m1;
   St6galnac5/Mm00488855_m1, St3gal1/Mm00501493_m1; St3gal2/Mm00486123_m1;
   St3gal3/Mm00493353_m1; St3gal4/Mm00501503_m1, St3gal6/Mm00450674_m1;
   Myd88/Mm00440338_m1 and NFKBIB/Mm01179097_m1.

Cell Migration Assay

   SFs (10^4) were seeded in u-dishes (ibidi, #80466) coated with
   fibronectin (R&D systems #1030-FN) until reaching 90% confluence.
   U-dishes have a plastic insert that leaves a cell-free gap when
   removed. Cells were allowed to grow in the gaps after insert removal,
   and cell-free areas were measured after gap was created (T0) and 24
   hours later (T24). Following monitoring of cell cultures, 24 hours was
   selected as our experimental time point because it allowed sufficient
   cell migration to observe biological differences without completely
   covering the cell-free region. Cell migration was quantified by
   measuring the difference in the width of cell-free region between T0
   and T24, calculated with ImageJ software.

Flow Cytometry

   For proliferation studies, cells were labelled with 10 μM proliferation
   dye eFlour 670 (eBiosciences, #65-0840-90) for 10 min on ice. Labelling
   was stopped by adding 4-5 volumes of complete culture medium for 5 min
   on ice. Cells were then subjected to flow cytometry analysis (day0) or
   maintained in culture for additional 5 days prior to analysis by Flow
   Cytometry. For lectin staining, Peanut Agglutinin (PNA, #B-1075),
   Sambucus Nigra Lectin (SNA, #B-1305), Aleuria Aurantia Lectin (AAL,
   #B-1395) and Maackia Amurensis Lectin II (MAA, # B-1265-1), all from
   vector laboratories, were used. Cells were blocked in carbon-free
   blocking buffer (vector laboratories, #SP-5040) for 20 min on ice, and
   then incubated with biotinylated lectins diluted in PBS containing 5%
   carbon-free blocking buffer. Lectins were then detected with
   FITC-conjugated streptavidin (Biolegend, #405201), Alexa Flour
   647-conjugated streptavidin (Biolegend, #2068269) or PE-conjugated
   Streptavidin (Biolegend, #410504) in PBS for 20 min at 4°C. To
   differentiate between live and dead cells, all samples were stained
   with DAPI (Sigma, #32670, dilution 1:1000) prior to data acquisition.
   Data were acquired using an LSR II flow cytometer (BD) and analysed
   using FlowJo version 10.8.0.

RNA-Sequencing (RNA-Seq) and Data Analysis

   Total RNA from cultured SFs was isolated, RNA integrity check was
   performed using the Agilent 2100 Bioanalyzer System and RNA integrity
   number (RIN) value was > 9 for all samples. Library preparation was
   done using RNA poly A selection at Glasgow Polyomics (Glasgow, UK). Low
   sequencing reads were removed using Trimmomatic ([52]28) before mapped
   to mouse reference genome (GRCM38) using Hisat2 version 2.1.0.
   Featurecounts version 1.4.6 was used to quantify reads counts. Mouse
   ENSEMBL gene ID to gene symbol conversion was performed in BioTools
   ([53]https://www.biotools.fr). Differentially expressed (DE) genes were
   identified using DESeq2, and Principal component analysis (PCA) were
   performed using R Bioconductor project DEbrowser ([54]29). Genes
   passing a threshold of Padj<0.01 and |log2Foldchange| > 1 were
   considered as differentially expressed. Gene Ontology (GO) Biological
   Process enrichment and KEGG pathway enrichment were conducted with
   Metascape ([55]30) and PathfindR ([56]31).

MTS Assay Protocol

   The MTS assay kit (Abcam, # ab197010) was used to measure the cellular
   metabolic activity of SFs according to manufacturer’s instructions.
   Briefly, cells were grown in 96-well plates (10,000 cells/well), medium
   was removed and 100 μL of cDMEM and 10 μL of MTS solution were added
   into each well. Plates were incubated for 4 hours at 37°C when
   absorbance was read at optical density of 590 nm. The same amount of
   cDMEM and MTS solution without cells was used as an internal control
   for no metabolomic changes and background absorbance.

Statistical Analysis

   Data are presented as the mean ± standard error (SEM). Statistical
   analysis was performed using Prism 8 software (GraphPad). One-way
   analysis of variance (ANOVA) was used to test significant differences
   among multi-groups, and student t-test was used between two groups
   studies. P values <0.05 were considered significant.

Results

SFs Subsets Show Distinctive Regulation of Sialyltransferases in Arthritic
Mice

   We have recently shown that inflammatory SFs isolated from mice
   undergoing experimental Collagen-Induced Arthritis (CIA) show reduced
   expression of the glycosyltransferase ST6Gal1 and associated α2-6
   sialylation ([57]26) but we did not have data about the relative
   expression of other sialyltransferases in SFs subsets. Therefore, to
   continue investigating SFs sialylation profile in joint disease, we
   isolated SFs from healthy and arthritic CIA mice. SFs were identified
   by flow cytometry as podoplanin+ and CD45-CD31- (to exclude immune and
   endothelial cells), and expression of CD90 was used to sort lining
   (CD90-) and sublining (CD90+) SFs ([58]Figure 1A), subsets that have
   shown distinct anatomical locations and pathophysiological roles
   ([59]14). As expected, we recovered a higher number of cells from the
   arthritic joints ([60]Figure 1B) and the relative proportion of CD90-
   versus CD90+ SFs was altered ([61]Figure 1C). RNA was extracted from
   naïve and CIA sorted CD90- and CD90+ SFs, and expression of IL-6, Ccl2
   and MMP3 was evaluated by RT-PCR ([62]Figure 1D). CIA SFs showed
   elevated expression of these inflammatory markers, corroborating their
   activated/inflammatory status compared to healthy SFs. In line with
   previous reports, SFs subsets showed a differential expression pattern
   for inflammatory cytokines IL-6 and Ccl2, with a more noticeable
   distinction under non-inflammatory conditions. Next, we quantified the
   mRNA expression of ST6Gal1, ST3Gal1, ST3Gal2, ST3Gal3, ST3Gal4, ST3Gal6
   and ST6GalNAc5, sialyltransferases expressed in murine SFs involved in
   glycoprotein sialylation ([63]Figure 1E). SFs subsets showed a
   different sialyltransferase expression profile, probably reflecting
   their different biological role and anatomical location. In healthy
   synovium, CD90+ SFs presented higher expression of ST6Gal1, ST3Gal1 and
   ST3Gal2 compared to CD90- cells. However, in inflammatory CIA
   conditions, CD90+ cells down-regulated expression of ST6Gal1 (which
   adds α2-6-linked sialic acids to glycoproteins), whereas enzymes
   involved in α2-3 sialylation remained unaltered or even up-regulated in
   the case of ST3Gal4. These results corroborate our previous findings
   ([64]26) and also provide further support to the hypothesis that
   reduced α2-6 sialylation is an inflammatory checkpoint in CD90+ SFs.

Figure 1.

   [65]Figure 1
   [66]Open in a new tab

   CD90+ SFs show a distinct expression of sialyltransferases in
   experimental arthritis. (A) SFs were isolated from joints of naïve and
   mice undergoing Collagen-Induced Arthritis. Cells were sorted by FACs
   gating on low viability dye, CD31-, CD45- and PDPN+ cells. Subsets of
   SFs were discriminated by expression of CD90. Sorting strategy is shown
   in the dot plots. (B) Total number of isolated cells from paws from
   naïve (n = 7) and CIA (n = 10) animals. (C) Relative proportions of SFs
   subset (lining: CD90- and sublining: CD90+) in total SFs of naïve (n =
   11) and arthritic mice (n = 12) evaluated by flow cytometry. (D)
   Relative expression of IL-6, CCL2 and MMP3 mRNA in SFs subsets were
   quantified by RT-qPCR (n ≥ 6). (E) Relative expression of α2,3- and
   α2,6-sialyltransferases (St6gal1, St6GalNac5, St3gal1, St3gal2,
   St3gal3, St3gal4 and St3gal6) were quantified in SFs subsets by RT-PCR
   (n ≥ 6). Data are represented as mean ± SEM; each dot represents SFs
   from one individual mouse. **p < 0.01 by Mann-Whitney test in (B, C),
   *p < 0.05, **p < 0.01, ***p < 0.001 by Kruskal-Wallis test in (D, E).

Enzymatic Removal of Sialic Acid Enhances SF Migration

   Next, we sought to examine the pathophysiological function(s) of SA in
   SFs. To this end, cells were expanded ex vivo from healthy murine
   synovium, as cultured SFs maintain most of their epigenetic and
   phenotypic signatures for several passages ([67]32, [68]33).
   Furthermore, cultured SFs express high and homogeneous levels of CD90
   ([69]9). Such phenotype is reminiscent of sublining SFs, subset that
   shows down-regulated ST6Gal1 expression during joint inflammation
   ([70]Figure 1E). Thus, expanded SFs provided a suitable tool for in
   vitro experiments in this context. To mimic the desialylation observed
   in vivo during disease, we treated SFs with Clostridium perfringens
   sialidase. Sialidase treatment reduced levels of both α2-6- and
   α2-3-linked SA as evidenced by the reduced binding of Sambucus nigra
   agglutinin (SNA) and Mackia amurensis agglutinin (MAA) ([71]Figure 2A).
   Cells showed 49.4% ( ± 0.029) SNA binding and 25.3% ( ± 0.038) MAA
   binding after desialylation. The sialidase specificity was further
   confirmed by an increased Peanut agglutinin (PNA) binding, since the
   presence of SA inhibits its glycan recognition, and unaffected binding
   of Aleuria aurantia agglutinin (AAL), a fucose specific lectin
   ([72]Figure 2A). SA has been linked to cell migration, with reports
   showing that both α2-6 and α2-3-linked sialic acid can promote cell
   migration in various cell types and cancer ([73]34–[74]37). Because SFs
   adopt a migratory phenotype during RA, we hypothesized that SFs with
   reduced levels of SA would have an increased migration capacity,
   similar to the activated cells in RA. To test this, SFs were
   desialylated with C. perfringens sialidase and cell migration was
   evaluated using wound healing assays on fibronectin coated wells.
   Results confirmed the proposed hypothesis, since cell migration was
   significantly increased in desialylated SFs ([75]Figure 2B). By
   contrast, neither cell proliferation ([76]Figure 2C), nor cellular
   metabolomic rate ([77]Figure 2D) were affected.

Figure 2.

   [78]Figure 2
   [79]Open in a new tab

   In vitro desialylation enhances SFs motility. SFs isolated from naïve
   mice were cultured and expanded ex vivo. Cells were cultured to reach
   confluency and then treated with 0.1U/ml of C. perfringens sialidase
   (CP) for 1 hour to remove sialic acid from cell surface. (A) Control
   and sialidase-treated cells were stained with biotinylated lectins
   (SNA, MAA, PNA and AAL), followed by incubation with Alexa-647
   streptavidin. Cells were then examined by flow cytometry to quantify
   lectin binding, as shown in histograms. Images were acquired in an
   EVOS™ FL Auto microscope, showing SNA and PNA-stained cells. Error
   bars: 200 μm. (B) SFs were seeded in migration chambers and grown until
   monolayer confluence to conduct migration assays. Images show one
   representative experiment, including control and sialidase-treated
   naïve SFs when insert was removed (time 0, T0) and 24 hours after
   desialylation (T24). Superimposed black lines define the cell-free
   area, scale bar: 200 μm. Column graph show the mean migration distance
   of 5 independent experiments, showing non-treated (NT) and
   sialidase-treated (CP) cells. Error bars represent SEM, each dot
   represents one independent experiment (n = 5), *p < 0.05 by
   Mann-Whitney test. (C) SFs were labelled with proliferation dye eFluor
   670 and analysed directly by flow cytometry (Day0) or treated with
   sialidase and maintained in culture for 5 days (Day5). Histogram shows
   one representative experiment. (D) Naïve SFs were seeded in 96-well
   plates, treated with sialidase and maintained in culture condition to
   assess cell viability and metabolomic activity using a colorimetric MTS
   assay. Each dot represents one independent experiment (n = 3) analysed
   in technical triplicate, error bars represent SEM, ns, non-significant,
   by Mann-Whitney test.

SA Removal Induces Rapid Pro-Inflammatory Transcriptomic Changes in Healthy
SFs

   We had previously observed that SFs up-regulated IL-6 and Ccl2 mRNA
   following loss of SA ([80]26), but the functional consequences of SA
   down-regulation in SFs-mediated immunity were effectively unknown.
   Therefore, to identify SA-associated pathways and hence further
   understand the role of sialylation in SFs, non-treated control cells
   and cells desialylated with C. perfringens sialidase were subjected to
   RNA-Seq analysis. Principal Component Analysis identified that the two
   groups displayed distinct transcriptome profiles ([81]Figure 3A). Thus,
   we searched for significant differential gene expression [DE fold
   change > 2, adjp <0.01] ([82]Figure 3B) to identify distinct
   transcriptomic signatures associated with hyposialylated conditions.
   This DE gene list ([83]Supplementary Table 1) was investigated for
   pathway enrichment using KEGG database ([84]Figure 3C). Remarkably, SA
   removal induced a clear activated phenotype in SFs, including enriched
   pathways for Rheumatoid Arthritis, cytokine signaling (TNFα, IL-17,
   chemokines) and NFkB and TLR signalling ([85]Figure 3C). Next, we used
   the MCODE algorithm via the bioinformatics tool Metascape ([86]30) to
   find functional gene nodes among the differentially expressed genes in
   desialylated SFs, identifying 10 nodes able to hold significant
   interconnected protein interactions ([87]Figure 4). All nodes could be
   grouped under the broad immunity label and were connected to a greater
   or lesser extent. Node 1 covered the larger number of genes, mostly
   related to the CXCL chemokine signalling and inflammatory mediators,
   indicating that SFs adopted a distinctive inflammatory stage upon
   desialylation. In line with this, nodes 2, 3 and 4 comprised genes
   involved in cytokine-cytokine receptor signalling, NOD-like receptor
   pathways and NFkB activation. Interestingly, pathways in nodes 1 to 4
   resembled a typical SF response during RA ([88]Figure 4A), even when
   cells in this experiment had not been stimulated with any inflammatory
   factor. In addition, another 5 functional nodes were defined [nodes
   5-9], that were directly connected to pathways involved in responses to
   viruses or associated processes, such as interferon signalling,
   phagosome formation or MHC-I complexes ([89]Figure 4B).

Figure 3.

   [90]Figure 3
   [91]Open in a new tab

   Sialidase-treated SFs show an inflammatory transcriptomic profile. SFs
   from naive mice were cultured and expanded ex vivo, RNA was isolated
   from non-treated control (NT) and C. perfringens sialidase treated (CP)
   SFs, then subjected to bulk RNA-Seq (75bp paired-end, 30M reads, n =
   3). (A) Principal component analysis (PCA). (B) All detected genes are
   plotted as a volcano plot. Genes that passed a threshold of padj < 0.01
   and |foldChange| > 2 are considered differentially expressed genes
   (DEGs), comparing sialidase-treated and non-treated SFs. Colour code,
   red: upregulated, blue: downregulated, in CP treatment. (C) Upregulated
   genes in sialidase treatment identified in (B) were used to perform
   KEGG pathway enrichment analysis. KEGG pathways are plotted in bubble
   chart with fold enrichment on x-axis and -log10 p-value on the coloured
   scale. The size of bubble is proportional to the number of DEGs in the
   given pathway.

Figure 4.

   [92]Figure 4
   [93]Open in a new tab

   Protein-protein interaction networks of upregulated DEGs in C.
   perfringens sialidase treated SFs. Upregulated DEGs identified in
   [94]Figure 3 were used to perform protein-protein interaction
   enrichment in Metascape ([95]https://metascape.org/ ). Genes are
   represented by coloured circles, size is directly proportional to the
   number of genes in each node. DEGs genes clustered a total of 10
   independent nodes based on known protein-protein interactions.
   Functional pathways significantly represented in each node are shown.
   Nodes are classified into two main categories based on the functional
   roles: ‘rheumatoid arthritis’ (A) and ‘response to virus’ (B).

   Thus, our RNA-Seq data described in detail how elimination of sialic
   acid on SF surface acts as a molecular signal to activate an
   inflammatory and anti-viral programme. Intriguingly, such
   sialidase-induced inflammation showed common characteristics with the
   known inflammatory phenotype of activated SFs during chronic RA, albeit
   some other pathways were unrelated and resembled immune responses to
   viral infections. To corroborate the inflammatory capacity of
   desialylated SFs, we selected a representative set of genes associated
   with inflammatory joint disease whose expression was significantly
   enhanced in the RNA-Seq dataset to be evaluated in independent
   experiments by RT-PCR. Specifically, we selected 8 up-regulated genes
   representative of SF-mediated inflammation, including cytokines (IL-6,
   Ccl2), matrix metalloproteinases (MMP3, MMP9, MMP13) and NFkB
   signalling pathways (MyD88, NFκBIB). In addition, we evaluated TNFSF11
   (TNFSF11, TNF Superfamily Member 11 or Receptor activator of nuclear
   factor kappa-B ligand, RANKL) and TNFRSF11B (TNF Receptor Superfamily
   Member 11b, or Osteoprotegerin, OPG) expression, because disturbed
   RANKL/OPG ratio promotes osteoclastogenesis and bone damage in RA
   ([96]38). Corroborating RNA-Seq data ([97]Figure 5A), IL-6, Ccl2, MyD88
   and all MMPs were up-regulated in desialylated SFs ([98]Figures 5B-D).
   Likewise, RANKL was up-regulated and OPG was down-regulated
   ([99]Figure 5E), matching sequencing results and suggesting an
   increased osteoclastogenic potential in desialylated SFs.

Figure 5.

   [100]Figure 5
   [101]Open in a new tab

   Sialidase-treated synovial fibroblasts show an enhanced inflammatory
   response. (A) mRNA expression as detected by RNA-Seq from [102]Figure 3
   for MMP3, MMP9, MMP13, IL-6, CCL2, Myd88, Nfκbib, Tnfsf11 and
   Tnfrsf11b. (B–E) RNA was isolated from control (NT) and C. perfringens
   sialidase treated (CP) naïve SFs. Relative expression of the genes
   shown in (A) was assessed by RT-qPCR using the ΔΔCt method and actin as
   housekeeping gene. (B) IL-6 and CCL2, (C) MMP3, MMP9 and MMP13, (D)
   Myd88 and Nfκbib, and (E) Tnfsf11 and Tnfrsf11b. For (B–E), each dot
   represents one independent experiment, error bars represent SEM (n =
   5), *p < 0.05, **p < 0.01 evaluated by Mann-Whitney test. NS,
   non-significant.

   Overall, our results show that hyposialylated SFs show an inflammatory
   phenotype reminiscent of arthritic cells, with an increased migratory
   ability and expression on pro-inflammatory cytokines and chemokines
   known to play a key role in RA. In fact, when RNA-Seq was used to
   compare SFs transcriptomic changes upon desialylation with those
   induced by IL-1β, a well-known inflammatory mediator in RA, we observed
   20.7% of overlapping up-regulated genes between both experimental
   conditions ([103]Figure 6). Among these genes, we found inflammatory
   cytokines and chemokines, like IL-6, Csf3, CXCL and Ccl members and
   MMPs ([104]Supplementary Table 2), which showed significantly enriched
   pathways for cytokine-receptor signalling, NFκB signalling, and other
   immune pathways ([105]Figure 6), further highlighting the pro-arthritic
   status of hyposialylated SFs.

Figure 6.

   [106]Figure 6
   [107]Open in a new tab

   Comparison of transcriptomic activation of desialylated and
   IL-1β-stimulated SFs. Bulk RNA-Seq (75bp paired-end, 30M reads, n = 3)
   was performed on i) naïve and IL-1β-stimulated, and ii) non-treated and
   C. perfringens-treated naïve SFs, to identify DEGs (Padj < 0.01 and
   |foldchange| > 2) in both experimental conditions. (A) Venn diagram
   shows the number of overlapping and unique DEGs identified in the IL-1β
   vs naïve and CP vs NT comparisons. (B) Overlapping genes identified in
   (A) were used to perform pathway enrichment using the bioinformatics
   tool Metascape. (C) Unique genes in IL-1β vs naïve and (D) unique genes
   in CP vs NT identified in (A) were used to perform protein-protein
   interaction enrichment in Metascape.

Enzymatic Removal of Surface SA Regulates Sialyltransferase Expression

   Although the inflammatory signature of hyposialylated SFs shared a
   significant number of pathways with classical IL-1β stimulation, it
   also contained a distinctive set of DE 213 genes [fold change > 2, adjp
   <0.01] only observed in response to sialidase treatment ([108]Figure 6
   and [109]Supplementary Table 2). Among these, we still found a clear
   immune signature, including inflammatory cytokines (IL-15, IL-16), CXC
   chemokines and TNFα NF-κB signalling pathways. Cellular activation was
   evidenced by a large presence of P-loop NTPase fold-containing
   proteins, like Guanylate-binding proteins (Gbp), involved in the
   hydrolysis of phosphate bond of nucleoside triphosphates like ATP or
   GTP. This is mechanistically related to oxidative killing,
   phagolysosomes function and anti-viral responses, some of the other
   pathways activated only by desialylation. Interestingly, we also
   observed in our RNA-Seq datasets that the glycosyltranferase genes
   ST6Gal1, ST3Gal3, Gcnt1, B3Galt1 and Galnt18 were significantly
   regulated in response to surface desialylation, but not in response to
   IL-1β ([110]Figure 7A and [111]Supplementary Table 2). This suggests
   that exogenous factors inducing a loss of SA might provide a positive
   feedback to modulate endogenous expression of sialosides, which could
   represent a link between extracellular factors that modify SA content
   and consolidation of inflammatory response, perhaps leading to chronic
   disease. Hence, to evaluate whether SA removal also modulates
   SF-sialylation pathways, we analysed expression of the two
   sialyltransferases, St6Gal1 and ST3Gal3, by RT-PCR after C. perfringens
   sialidase treatment. Corroborating the RNA-Seq data, SF desialylation
   significantly down-regulated St6Gal1 mRNA expression, whilst it
   up-regulated ST3Gal3 mRNA expression ([112]Figure 7B). Moreover, flow
   cytometry lectin binding experiments showed that CP-treated SFs had a
   decreased capacity to rebuild surface SA expression upon sialidase
   treatment, as evidenced by increased PNA-binding and decreased
   SNA-binding ([113]Figure 7C). This could indicate that desialylation
   implements a molecular mechanism to maintain low levels of α2-6
   sialylation even if the cells are no longer exposed to sialidase
   hydrolytic action.

Figure 7.

   [114]Figure 7
   [115]Open in a new tab

   Enzymatic removal of sialic acid modulates intracellular
   sialyltransferases expression. (A) mRNA expression in SFs as detected
   by RNA-Seq from [116]Figure 6 for ST6Gal1, ST3Gal3, Gcnt1, B3Galt1 and
   Galnt18, including gene expression following C. perfringens sialidase
   treatment or IL-1β stimulation. Table shows adjp value and fold change
   of the glycosyltransferase genes, significantly regulated (adjp < 0.01,
   |log2Foldchange| >1) in C. perfringens sialidase treated SFs but not in
   in IL-1β-stimulated SFs. (B) RNA was extracted from control and C.
   perfringens sialidase treated naïve SFs. Relative expression of ST3gal3
   and ST6gal1 was evaluated by RT-qPCR. Each dot represents one
   independent experiment, error bars represent SEM (n = 5), *p < 0.05,
   **p < 0.01 by Mann-Whitney test. (C) The presence of sialic acid on
   control and C. perfringens sialidase treated naive SFs was examined by
   flow cytometry for the binding of SNA, MAA, PNA and AAL. Experiments
   were performed after treatment (T0) and 24-hour incubation after
   treatment (T24).

Discussion

   In this study, we report that synovial fibroblasts undertake a highly
   inflammatory phenotype when SA is removed from the cell surface. This
   supports our in vivo results, showing that inflammatory SFs show lower
   sialylation than healthy cells, mainly α2-6 linked SA. Notably,
   sialylation has been involved in cellular processes that are critical
   for SFs-mediated pathophysiology in RA, like cell migration and immune
   regulation. SA also determines metastatic potential and migration in
   cancer as well as tumour aggressiveness and invasiveness ([117]39,
   [118]40). Specifically, elevated α2-3 sialylation has been associated
   with enhanced migration in breast cancer, melanoma and pancreatic
   adenocarcinoma ([119]41–[120]43). Likewise, pathogenic cell migration
   has also been reported for α2-6 linked SA. Pally et al. described that
   distinct sialylation levels correlate with migratory phenotypes of
   epithelial cancer cells in three-dimensional cultures ([121]44) and
   endometriotic cells show enhanced migration after α2-6 desialylation
   ([122]45). Similar mechanisms could therefore happen in the arthritic
   joint to promote SFs migration and invasion of cartilage tissue.
   However, local factors such as the composition of the extracellular
   matrix in health and disease will influence cell migration. Thus,
   comparisons with other cell types or environments should be taken with
   caution and tailored studies are needed to fully understand how SA
   might affect SF migration in the context of arthritis.

   Our data also revealed a strong link between low sialic acid content
   and initiation of SFs inflammatory and immune responses, suggesting
   that sialic acid acts as a molecular switch to control tissue
   homeostasis and inflammation in the joint synovial space. Our in vivo
   data show that a decreased α2-6/α2-3 SA ratio is responsible of SFs
   activation, something that C. perfringens sialidase recapitulates in
   vitro, proving that hyposialylated SFs become highly inflammatory. In
   this regard, the pathophysiology of SA in cancer represents the
   opposite scenario to autoimmune RA, as high SA content provides an
   advantageous scenario to cancer cells, particularly from the
   perspective of moderating immune responses. Tumour cells evade immune
   responses by adopting a hypersialylated phenotype to exploit SA-Siglec
   immunosuppressive signalling ([123]46–[124]49), whereas highly
   sialylated SFs may provide the immunosuppressive environment required
   in joint physiology. These overall results offer strong support to
   consider SA as an immunoregulatory switch, whose opposite actions in
   cancer and autoimmunity may represent two sides of the same coin.
   Considering this, SA is a potential target in cancer immunotherapy,
   since eliminating SA restraint in the tumour microenvironment could
   release anti-tumour immunity. Early studies dating back a few decades
   showed that treatment of a leukaemia cell lines with neuraminidases
   increased their immunogenicity ([125]50, [126]51). A more specific
   approach is the recent development of engineered antibody–sialidases
   conjugates to target Siglec-dependent binding of NK cells, which makes
   tumour cells more susceptible to antibody-dependent cell-mediated
   cytotoxicity ([127]52). However, our results in SFs could perhaps
   suggest that biologics with sialidase activity could also have the
   potential of starting off-target inflammation and autoimmunity,
   although additional work in animal models or clinical studies are
   required to challenge this hypothesis. Nevertheless, further steps have
   already been taken to optimise the selectivity of antibody-sialidases
   conjugates, by assessing several recombinant sialidases ([128]53), but
   consideration of sialidase-dependent immune effects in long-term
   therapeutic regimes may be carefully considered for the development of
   safer and better sialidase-conjugated biologics.

   Our study provides a causal link between presence of SA and initiation
   of immune responses in stromal SFs, but it still presents important
   limitations that should be addressed in follow-up studies. We used a
   recombinant sialidase from C. perfringens but details about the
   specific changes in sialylated glycoconjugates are still unknown.
   Specific SA linkages, membrane distribution, and SA acetylation might
   be an important aspect of SA-dependent communication and signalling. In
   fact, studies using CRISPR Cas9 showed that SA acetylation affects
   Siglec-mediated functions ([129]54). Besides, the predominant sialic
   acids on murine cells are N-acetylneuraminic acid (Neu5Ac) and
   N-glycolylneuraminic acid (Neu5Gc), the latter not synthesised in
   humans because of the loss of the CMAH gene. This evolutionary event
   caused a rapid adaptation of the Siglec family to the new human glycome
   dominated by Neu5Ac. Therefore, translation of findings from murine
   models to human biology may be challenging, especially given that
   several pathotypes have been described in human RA ([130]55). It is
   still very unclear how inflammatory mediators modulate the content of
   SA in human RA to initiate or perpetuate inflammation, and further
   studies, considering disease heterogeneity, should be conducted. To
   facilitate these translational findings, it is required to delineate
   the molecular mechanisms underlying SA-dependent SF activation. Loss of
   SA would imply a lack of regulatory Siglec signalling, but it may also
   uncover underlying galactose residues allowing galectin-3-binding and
   subsequent cell activation, as galectin-3 is a highly inflammatory
   mediator. In fact, galectin-3 induces a higher expression of
   pro-inflammatory IL-6, GM-CSF, MMP3 and even TNFα in SFs than in skin
   fibroblasts ([131]3). A reduction of such inflammatory mediators in the
   synovium would also reduce local inflammation and cell recruitment,
   further reducing local TNFα and maintaining high levels of sialylation,
   since inflammatory TNFα down-regulates ST6Gal1 expression and α2-6
   sialylation ([132]26). Therefore, the inflammatory axis
   TNFα-hyposialylation-galectin-3 could have a stronger impact in the
   synovium compared with other tissues, such as the skin, perhaps
   explaining the tissue tropism observed in RA inflammation.
   Nevertheless, the potential effect of hyposialylated fibroblasts must
   also be investigated in other tissue more prone to suffer chronic
   inflammation, like the lung or gut. Interestingly, low sialylation has
   been linked to other pathways and cell types during RA, for example,
   activated chondrocytes show reduced levels of α2-3 SA and
   hyposialylation is also observed in Rheumatoid arthritis
   (RA)-associated IgG antibodies ([133]56, [134]57).

   Finally, we believe that understanding the factor(s) responsible of SF
   loss of SA in vivo is of high relevance to understand chronic RA. Such
   factors could have heterogeneous origins including i) cytokine
   signalling, like the TNFα-mediated downregulation of ST6Gal1 as we have
   shown before, ii) endogenous sialidases, secreted by SFs or other
   immune cells in arthritic joint, iii) infections, for example viral
   sialidases and iv) environmental factors, like diet and environment.
   For example, cigarette smoke reduces ST6Gal1 and α2-6 sialylation in
   bronchial epithelial cells leading to IL-6 production ([135]58),
   similarly to the effects that we have observed in SFs upon
   desialylation. Similarly, sialidases are secreted by several viruses
   and other pathogens to modulate SA-dependent actions ([136]59,
   [137]60), perhaps indicating that infections can remodel the local
   glycome to trigger, or favour, the establishment of future inflammatory
   RA. The fact that desialylated SFs activate anti-viral responses would
   provide support to such viral infection-chronic inflammation link.
   Importantly, our results show that removal of surface SA reconfigures
   expression of some sialyltransferases, suggesting that initial SA loss
   can lead to chronic inflammatory feedbacks, contributing to
   perpetuation of disease in RA regardless of the initiating factors.

Data Availability Statement

   The data presented in this study are deposited in NCBI's Gene
   Expression Omnibus and are accessible through GEO Series accession
   numbers [138]GSE192488 and [139]GSE196898.

Ethics Statement

   The animal study was reviewed and approved by Ethics Review Board of
   the University of Glasgow.

Author Contributions

   MP conceived and performed experiments, oversaw the project,
   interpreted the results, and wrote the manuscript with feedback from
   all authors. YW performed experiments and contributed to design of the
   experiments, data analysis and manuscript writing. PP, CC, and AK
   performed experiments. All authors contributed to the article and
   approved the submitted version.

Funding

   The work was funded by a Career Development award to MP from Versus
   Arthritis (21221), a China Scholarship Council (CSC) PhD scholarship
   awarded to YW and a YLSY Turkish Ministry of National Education Study
   Abroad Programme awarded to CC.

Conflict of Interest

   The authors declare that the research was conducted in the absence of
   any commercial or financial relationships that could be construed as a
   potential conflict of interest.

Publisher’s Note

   All claims expressed in this article are solely those of the authors
   and do not necessarily represent those of their affiliated
   organizations, or those of the publisher, the editors and the
   reviewers. Any product that may be evaluated in this article, or claim
   that may be made by its manufacturer, is not guaranteed or endorsed by
   the publisher.

Supplementary Material

   The Supplementary Material for this article can be found online at:
   [140]https://www.frontiersin.org/articles/10.3389/fimmu.2022.847581/ful
   l#supplementary-material
   [141]Click here for additional data file.^ (19.9KB, xlsx)
   [142]Click here for additional data file.^ (18.8KB, xlsx)

References