Regulatory T Cell Activation Triggers Specific Changes in Glycosylation

bioRxiv preprint doi: https://doi.org/10.1101/2020.07.29.226399; this version posted July 30, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

2 Regulatory T cell activation triggers specific changes in 3 glycosylation associated with Siglec-1-dependent inflammatory 4 responses

6 Gang Wu, Gavuthami Murugesan, Manjula Nagala, Alex McCraw, Stuart M. Haslam1, Anne Dell1, 7 Paul R. Crocker

8 9 Division of Cell Signalling and Immunology, School of Life Sciences, University of Dundee, Dundee, 10 DD1 5EH, Scotland, United Kingdom 11 1 Department of Life Sciences, Imperial College London, London, United Kingdom

12 13

14 Abstract

15 Siglec-1 is a macrophage lectin-like receptor that mediates sialic acid-dependent cellular interactions.

16 It was shown previously to promote inflammation in autoimmune disease through suppressing the

17 expansion of regulatory T cells (Tregs). We have investigated the molecular basis for Siglec-1 binding

18 to these cells using in vitro-induced Tregs. Siglec-1 binding was strongly upregulated on activated cells,

19 but lost under resting conditions. Glycosylation changes that affect Siglec-1 binding were studied by

20 comparing activated and resting Tregs using RNA-Seq, glycomics, proteomics and binding of selected

21 antibodies and lectins. A proximity labelling and proteomics strategy identified 49 glycoproteins

22 expressed by activated Tregs that may function as Siglec-1 counter-receptors. These represent ~5%

23 of the total membrane protein pool and were mainly related to T cell activation and proliferation. We

24 demonstrate that several of these counter-receptors are upregulated following activation of Tregs and

25 provide initial evidence that their altered glycosylation may also be important for Siglec-1 binding.

26 Introduction

27 All mammalian cells are coated with a dense layer of glycans termed the glycocalyx (Tarbell & Cancel,

28 2016). Despite the diverse structures and inherent complexity of these glycans, they are frequently

29 capped with sialic acid moieties. Sialic acids can mediate a wide variety of functions (Varki, Schnaar,

30 & Schauer, 2017), but an important feature is that they serve as ligands for a family of endogenous

31 sialic acid binding lectins of the Ig superfamily known as Siglecs (Crocker, Paulson, & Varki, 2007). The

32 interaction between Siglecs and their ligands can regulate the functional activities of most cells of the

33 immune system (Crocker et al., 2007; Macauley, Crocker, & Paulson, 2014). Siglec-1 (also known as

34 sialoadhesin, Sn or CD169) is a macrophage-restricted Siglec that is well conserved across mammals

35 (Klaas & Crocker, 2012). Under normal physiological conditions, it is highly expressed on macrophage

36 subsets in secondary lymphoid tissues and its expression on other macrophages can be induced at

37 sites of inflammation (Klaas & Crocker, 2012; Schadee-Eestermans et al., 2000). Siglec-1 appears to

38 have evolved primarily as a cellular interaction molecule. It has an unusually large number of 17 Ig

39 domains that are thought to project the sialic acid binding site away from the plasma membrane to

40 promote interactions with sialic acid ligands presented on other cells (Klaas & Crocker, 2012). This is

41 in striking contrast to other Siglecs which have between 2 and 7 Ig domains and are typically masked

42 at the cell surface by interactions with sialic acids in cis (Crocker et al., 2007).

43 In addition to being displayed on cells of the host, the sialic acids recognised by Siglec-1 can be present

44 on certain pathogens such as sialylated bacteria, protozoa and enveloped viruses and their recognition

45 can lead to increased pathogen uptake by macrophages and enhanced host susceptibility (reviewed in

46 (Macauley et al., 2014)). However, the predominant biological functions of Siglec-1 involve interactions

47 with sialic acids of the host. For example, Siglec-1 can mediate sialic acid-dependent crosstalk between

48 macrophages and various immune cells including neutrophils (Crocker, Freeman, Gordon, & Kelm,

49 1995), dendritic cells (van Dinther et al., 2018), innate-like lymphocytes (Zhang et al., 2016) and

50 regulatory T cells (C. Wu et al., 2009).

51 An important biological function of Siglec-1, discovered in studies of Siglec-1-deficient mice, is its role

52 in promoting inflammatory responses during various autoimmune diseases of the nervous system (Groh

53 et al., 2016; Ip, Kroner, Crocker, Nave, & Martini, 2007; Jiang et al., 2006; Kobsar et al., 2006; C. Wu

54 et al., 2009). Mechanistically, this is likely to be due to Siglec-1-dependent suppression of Treg

55 expansion. This was initially implied in studies of experimental autoimmune uveitis, which showed that

56 Siglec-1 was a prominent marker on inflammatory macrophages at the peak phase of tissue damage

57 (Jiang et al., 2006). Siglec-1-deficient mice exhibited reduced disease severity and decreased

58 proliferation and IFN-γ secretion by effector T cells. Direct evidence for an important cross-talk between

59 Siglec-1 and Tregs was seen in a study of experimental autoimmune encephalomyelitis (EAE), which

60 is a mouse model of multiple sclerosis (C. Wu et al., 2009). The EAE model revealed that Siglec-1-

61 expressing macrophages are closely associated with activated CD4+Foxp3+ Tregs at sites of

62 inflammation within the central nervous system. Siglec-1-deficient mice had increased numbers of

63 Tregs and reduced levels of Th17 cells producing inflammatory cytokines, leading to attenuated disease

64 severity (C. Wu et al., 2009). The Tregs isolated from diseased mice showed strong sialic acid-

65 dependent binding to Siglec-1 and co-culture with macrophages suppressed their expansion in a

66 Siglec-1-dependent manner. Similar results have been observed in a mouse model of neuronal ceroid

67 lipofuscinoses, which showed that Siglec-1 negatively controls CD8+CD122+ regulatory T cells, and

68 promotes neuroinflammation-related disease progression (Groh et al., 2016).

69 A major gap in our understanding of how Siglecs modulate cellular functions in the immune system is

70 the identification of endogenous ligands and counter-receptors on relevant cell populations. Here, the

71 ligand is defined as the oligosaccharide structure recognised by Siglec-1 and the counter-receptor is

72 the composite of the ligand(s) attached to an appropriate protein or lipid carrier (Crocker & Feizi, 1996).

73 Using defined glycans, Siglec-1 has been found to prefer α2,3-linked Neu5Ac over α2,6- and α2,8-

74 linked Neu5Ac (Crocker et al., 1991; Crocker, Vinson, Kelm, & Drickamer, 1999). Certain other types

75 of sialic acid, including Neu5Gc and Neu5,9(Ac)2, were not recognised by Siglec-1 (Kelm, Schauer,

76 Manuguerra, Gross, & Crocker, 1994). Like many membrane lectins, Siglec-1 exhibits low binding

77 affinities for its glycan ligands, with Kd values in the millimolar range (Crocker et al., 1999). Cell

78 adhesion mediated by Siglec-1 therefore depends on the clustering of both Siglec-1 and its ligands on

79 cell surfaces to obtain high avidity interactions. As a result, the molecular basis for Siglec-1 binding to

80 Tregs is complex and determined by multiple factors. On the one hand it can be affected by global

81 factors involved in the synthesis of glycan ligands, such as the production and transport of sugar donors

82 and the expression of sialyltransferases and other glycosyl transferases. On the other hand, it can also

83 be affected by specific factors, such as the expression and localisation of particular glycoprotein and

84 glycolipid counter-receptors which carry the glycan ligands.

85 The aim of this study was to investigate the global and specific factors that lead to Siglec-1 binding to

86 activated Tregs using RNA-Seq, glycomics and proteomics. A proximity labeling strategy, combined

87 with proteomics, was used to identify glycoprotein counter-receptors for Siglec-1 expressed by activated

88 Tregs.

90 Results

91 Siglec-1 binding to Tregs depends completely on Treg activation

92 FoxP3-positive CD4 Tregs are a small subset of the total pool of CD4 T cells but can be induced from

93 FoxP3-negative CD4 T cells under defined culture conditions. In order to obtain a sufficient number of

94 Tregs for RNA-Seq, glycomics and proteomics, CD4 T cells were isolated from mouse spleens and

95 lymph nodes and induced to become Tregs as illustrated in Figure 1. After induction for 4 - 5 days, the

96 proportion of Tregs increased from about 4% to about 90%, and the cell count increased 1- to 3-fold.

97 After expansion for a further 4 days, the cell count increased another 2- to 3-fold, without affecting the

98 proportion of Tregs (Figure 1). Siglec-1 binding to these Tregs was analysed by flow cytometry. The

99 freshly-induced and activated Tregs exhibited strong Siglec-1 binding (Figure 2A). When these cells

100 were cultured under resting conditions in the absence of anti-CD3 antibody, Siglec-1 binding

101 disappeared completely, but was fully restored when the cells were re-stimulated with anti-CD3 antibody

102 (Figure 2A). Alternatively, when the freshly-induced Tregs were kept activated with anti-CD3, they

103 continued to exhibit strong Siglec-1 binding until anti-CD3 was removed from the cell culture medium

104 (Figure 2A). The extent of induction of Siglec-1 ligands on Tregs by anti-CD3 antibody and its kinetics

105 were dose-dependent over a range of anti-CD3 concentrations (Figure 2B). These observations reveal

106 that Siglec-1 binding to Tregs in vitro depends completely on Treg activation. This is consistent with

107 previous in vivo studies showing that Siglec-1 bound only to activated, but not resting Tregs (Kidder,

108 Richards, Ziltener, Garden, & Crocker, 2013; C. Wu et al., 2009).

109

110 Siglec-1 binding to activated Tregs is not due to a global change of sialylation

111 The striking induction of Siglec-1 ligands on activated Tregs suggests that activation could be

112 accompanied by global changes in glycan sialylation, as observed previously with CD4+ effector T cells

113 (Naito-Matsui et al., 2014; Redelinghuys et al., 2011). These include increased expression of α2,3-

114 linked sialic acids and a switch from the NeuGc to NeuAc form of sialic acid, both changes leading to

115 increased binding of Siglec-1. To investigate this, we used plant lectins MAL II and SNA to probe the

116 overall α2,3-linked and α2,6-linked sialylated glycans respectively, and anti-NeuGc antibody to measure

117 NeuGc levels on Tregs. However, unlike effector T cells, there were no obvious changes in either lectin

118 or antibody binding upon Treg activation (Figure 3).

119 Glycomics was used to directly examine the structures and relative quantities of Treg N-glycans and

120 glycolipids. N-glycans showed an overall similar glycosylation and sialylation profile when resting and

121 activated Tregs were compared (Figure 4). A specific change was found at m/z 3026, which is a bi-

122 antennary glycan with core fucose and two NeuGc sialic acids. Relative to the other glycans, this glycan

123 had a decreased intensity when Tregs became activated. Gal-α-Gal terminal structures were

124 specifically found on mono-sialylated core fucosylated glycans at m/z 2809 and m/z 2839. Relative to

125 the other biantennary glycans, the two glycans had a minor increase upon Treg activation. For

126 glycolipids, MS (Figure 5) and MS/MS (Supplementary Figures 1-8) analyses showed different glycan

127 profiles in resting and activated Tregs, with a trend of NeuGc switching to NeuAc upon activation. This

128 can be seen by the higher proportions of NeuAc-containing glycans at m/z 1288, 1533 and 1649 versus

129 their NeuGc-containing counterparts at m/z 1318, 1563 and 1709 in activated Tregs (Figure 5). This

130 was especially striking for the α2,3-sialylated GM1b structures at m/z 1288 (NeuAc) and m/z 1318

131 (NeuGc) (Figure 5). These observations suggest that GM1b (NeuAc) is upregulated on activated Tregs

132 where it could potentially serve as a ligand for Siglec-1.

133

134 RNA-Seq analysis does not indicate a global change of sialylation in activated Tregs

135 RNA-Seq was used to profile the gene expression patterns, comparing resting and activated Tregs

136 prepared from 4 independent biological replicates. The peak expression of Siglec-1 ligands was

137 observed between 24 and 48 hours following Treg activation (Figure 2). Therefore we selected a 36

138 hour time point to isolate mRNA in order to maximise the chances of seeing clear changes in gene

139 expression relevant to Siglec-1 ligand expression. The Pearson’s correlation coefficient for each pair of

140 replicates is shown in the distance matrix in Figure 6A, which shows good quality data with reproducible

141 replicates and major changes between resting and activated Tregs. This is further illustrated by

142 clustering and PCA analyses (Figure 6B,C). To focus on genes involved in glycosylation, we assembled

143 a dataset of 263 genes including glycosyltransferases, glycosidases, enzymes involved in amino sugar

144 and nucleotide sugar metabolism, and sugar transporters (Supplementary Table 1). Whilst, overall,

145 these genes did not show a dramatic global log2 fold change there were many specific changes upon

146 Treg activation (Figure 6D, Supplementary Table 1). Mapping of the data to KEGG pathways points to

147 overall increased N-glycosylation in activated Tregs (Supplementary Figure 9) and increased O-

148 glycosylation for proteins that depend on expression of Galnt3, a GalNAc transferase that initiates O-

149 glycosylation on serine and threonine residues (Supplementary Figure 10).

150 The expression patterns of genes involved in Treg sialylation are shown in Figure 7. The

151 sialyltransferases (STs) are a family of ~20 enzymes that transfer sialic acids to acceptor sugars in

152 α2,3-, α2,6- and α2,8-glycosidic linkages (Takashima, 2008). For α2,3-sialylation, the preferred linkage

153 for Siglec-1, Treg activation was associated with decreased expression of St3gal1, St3gal2 and St3gal6,

154 increased expression of St3gal5 and no change for St3gal3 and St3gal4 (Figure 7A). Similarly, for α2,6-

155 sialylation, differential expression was observed, with a 5-fold decrease of St6gal1 and a 3-fold increase

156 in expression of both St6galnac4 and St6galnac6 following activation (Figure 7B). The net effect of

157 these alterations on overall α2,3 and α2,6 sialylation appears to be minimal as MALII and SNA staining

158 for these respective linkages showed no changes on Treg activation, as described above (Figure 3A,B).

159 For α2,8-sialylation, St8sia1 and St8sia4 were both decreased upon Treg activation, but expression of

160 St8sia6 was slightly increased (Figure 7C). We also analysed genes involved in modifications of sialic

161 acids including conversion of NeuAc to 9-O-acetyl NeuAc and Neu5Gc, neither of which is recognised

162 by Siglec-1 (Kelm et al., 1994). 9-O-acetylation is controlled by Casd1 and Siae which catalyse the

163 addition or removal of acetyl groups to NeuAc respectively (Baumann et al., 2015; Orizio et al., 2015).

164 Expression of both genes was reduced following Treg activation, suggesting no net change in levels of

165 9-O-acetyl NeuAc. (Figure 7D). Expression of Cmah, which is responsible for converting CMP-NeuAc

166 to CMP-NeuGc (Kawano et al., 1995), did not change significantly upon Treg activation. This is

167 consistent with the anti-NeuGc Ab binding results shown above (Figure 3C) which showed no

168 differences between resting and activated Tregs. Finally, we analysed expression of endogenous

169 sialidases with the potential to remove sialic acids from the cell surface. Of the 4 sialidase genes (Monti

170 et al., 2010), only Neu1 and Neu3 were expressed in Tregs. Expression of Neu1 increased slightly in

171 activated Tregs while Neu3 did not change significantly (Figure 7D). In conclusion, the results of RNA-

172 Seq revealed changes in expression of many genes that affect glycosylation of multiple proteins and

173 lipids, but did not reveal specific changes predicted to have major effects on Siglec-1 recognition

174 following Treg activation.

175

176 Identification of glycoprotein counter-receptors for Siglec-1

177 A recently-described proximity labelling strategy (Chang et al., 2017; G. Wu, Nagala, & Crocker, 2017),

178 coupled with quantitative proteomics, was used to identify Treg glycoproteins that could interact with

179 Siglec-1. The experimental design is illustrated in Figure 8. It essentially involves preparation of

180 Siglec-1-horse radish peroxidase (Sn-HRP) multimers (Figure 8A) that can bind to Tregs and, in the

181 presence of tyramide-SS-biotin and H2O2, generate short-range biotin radicals that label neighbouring

182 proteins (Figure 8B). Labelled proteins are then enriched with streptavidin and identified by quantitative

183 proteomics (Figure 8C). To increase the chances of identifying diverse counter-receptors, two types of

184 Siglec-1-HRP multimer were prepared, either in-solution, or attached to 50 nm microbeads (Figure 8A).

185 Because of the potential for non-specific labelling and streptavidin binding, it was important to include

186 similarly-prepared multimers of a non-binding negative control Siglec-1, (SnR97A) alongside the active

187 Siglec-1. Proteins that were selectively enriched using the Sn-HRP complexes over the SnR97A-HRP

188 complexes represent potential Siglec-1 counter-receptors (Figure 8C).

189 Three biological replicates of activated Tregs were prepared for independent proximity labelling

190 experiments using both in-solution and on-bead multimers, resulting in 6 data sets. For each data set,

191 the log2 fold change of each protein relative to its negative control was calculated and visualized

192 according to its cellular compartmentalisation (Figure 9A). A subset of membrane proteins with higher

193 log2 fold changes were observed in the histogram, resulting in a small but clear tail, which we describe

194 as a ‘proximity labeling tail’. This tail was only found for membrane proteins but not for cytoplasmic,

195 nuclear or other proteins and suggests that only certain membrane proteins were biotinylated and

196 selectively enriched. Five out of the 6 data sets showed the proximity labelling tail (Supplementary

197 Figure 11) and these were selected for statistical analysis using volcano plots (Figure 9B). A small

198 subset of proteins was found to have significant log2 fold changes. As expected, these were mainly

199 membrane proteins.

200 The glycosylated proteins from the significant hits on the volcano plot were selected for further data

201 filtering; they were mapped back to the individual histogram of total membrane proteins (Supplementary

202 Figure 12), and those which were outside the proximity labelling tail were filtered out. The final Siglec-1

203 counter-receptor list of 49 membrane proteins is shown in Table 1. We successfully identified proteins

204 that make up the Siglec-1-HRP multimer complex, namely HRP, human IgG Fc and Siglec-1, with gene

205 names PRXC1A, IGHG1 and Siglec1 respectively. We also identified CD43 which was proposed

206 previously to function as a counter-receptor for Siglec-1 (van den Berg et al., 2001). These

207 identifications serve as a proof-of-principle for the proximity labelling approach. Interestingly, Siglec-1

208 counter-receptors included a wide range of glycoproteins involved in a variety of functions, including

209 the regulation of T cell activation and proliferation, such as CD80, CD200, CD69, CD150, PD-1 and

210 PD-L1, adhesion molecules like CD166 and integrins, the IL-2 receptor (CD25) and transporters like

211 the L-type amino acid transporter, 4F2 (Supplementary Table 2).

212

213 Characterization of Siglec-1 counter-receptors

214 To determine the proportion of membrane proteins constituted by the 49 Siglec-1 counter-receptors,

215 proteomics analyses were undertaken on activated Tregs. To maximise the number of membrane

216 proteins identified, two approaches were used, either by performing proteomics of whole cell lysates or

217 following cell surface biotinylation and enrichment of labelled proteins with streptavidin beads. This

218 combined approach led to identification of 943 membrane proteins, suggesting that the 49 Siglec-1

219 counter-receptors comprise approximately 5% of the total membrane proteins on activated Tregs

220 (Figure 10A).

221 We next asked whether the Siglec-1 counter-receptors were distributed amongst the more abundant

222 membrane glycoproteins using both the RNA-Seq and proteomics datasets (Figure 10B,C). This

223 revealed that Siglec-1 counter-receptors were distributed across the range of proteins from low to high

224 abundancy, but were more enriched amongst the high abundancy proteins. To investigate whether

225 Siglec-1 counter-receptors might be more heavily glycosylated than other membrane proteins, we

226 compared 16 counter-receptors belonging to the Ig superfamily with 56 non-counter-receptors of the Ig

227 superfamily identified by proteomics. The results showed that most of the counter-receptors had more

228 than 1 predicted N-glycosylation site per Ig-like domain, while most of the non-counter-receptors had

229 less than 1 site per domain (Figure 10 D,E). This suggests that glycosylation density is an important

230 determinant of functioning as a Siglec-1 counter-receptors, presumably by allowing them to mediate

231 higher avidity binding to clustered Siglec-1. Next, we asked if the counter-receptors were upregulated

232 on activated Tregs compared to resting cells. This was investigated at both the protein level using flow

233 cytometry (Figure 11A) and at the mRNA level using RNA-Seq data (Figure 11B). Several proteins,

234 including CD80, PD-1 and CD274 (PD-L1), showed increased protein expression in activated Tregs,

235 which correlated with increased mRNA levels, but for about half of the counter-receptors, mRNA levels

236 were either not changed or were decreased (Figure 11B).

237 As a first step to investigate glycosylation changes in Siglec-1 counter-receptors, we performed western

238 blotting on CD48, as an example of a protein that did not change expression, and PD-1, as an example

239 of a protein that is upregulated on activated Tregs. Compared to resting Tregs, both CD48 and PD-1

240 from activated Tregs displayed a more heterogeneous smear and increased MW by SDS-PAGE,

241 indicating increased glycosylation (Figure 11C,D). For PD-1, this was confirmed following treatment of

242 affinity-purified PD-1 with PNGase F to remove N-glycans which resulted in PD-1 from resting and

243 activated Tregs migrating similarly by SDS-PAGE (Figure 11E). Finally, affinity-purified PD-1 from

244 resting and activated Tregs was treated with sialidase, leading to increased migration by SDS-PAGE

245 showing that it carries sialylated glycans with potential to be recognised by Siglec-1 (Figure 11F).

246

247

248 Discussion

249 The major aim of this study was to obtain insights into the molecular basis of Siglec-1-dependent

250 interactions of macrophages with Tregs and thus improve our understanding of how this lectin promotes

251 inflammatory responses in certain autoimmune diseases. Consistent with in vivo and in vitro studies

252 (Kidder et al., 2013; C. Wu et al., 2009), we found Siglec-1 binding to Tregs depended completely on

253 Treg activation. Based on this finding, a key part of our strategy was to analyse glycosylation changes

254 by performing side-by-side comparisons of resting and activated Tregs using a range of unbiased and

255 targeted approaches. These included RNA-Seq, glycomics, proteomics and staining with lectins and

256 anti-glycan antibodies. Furthermore, we used a recently-described proximity labelling strategy (Chang

257 et al., 2017; G. Wu et al., 2017) to identify membrane proteins on activated Tregs that could function as

258 Siglec-1 counter-receptors. This comprehensive combined approach has provided a wealth of new

259 information regarding glycomics changes and Siglec-1 recognition following Treg activation. Clear

260 changes in glycosylation were observed for both N-glycans and glycolipids and many glycogenes were

261 altered in expression following Treg activation. Although the precise mechanisms by which these

262 changes lead to increased Siglec-1 binding remain to be determined, they are likely to involve complex

263 protein- and lipid-dependent modification of sialylation at an individual carrier level. The identification

264 of 49 Siglec-1 glycoprotein counter-receptors provides important clues as to how Siglec-1 can mediate

265 adhesion and signalling to activated Tregs. These counter-receptors were dominated by adhesion and

266 signalling glycoproteins mainly related to T cell activation and proliferation (Table 1, Supplementary

267 Table 2). Importantly, several counter-receptors showed increased expression at the protein level and

268 preliminary analyses showed increased glycosylation following Treg activation. The combined effect of

269 all changes noted above could lead to increased Siglec-1 binding and signalling to activated Tregs.

270 Although the focus of this study was on the lectin Siglec-1, there are many other endogenous lectins

271 such as galectins and C-type lectins whose binding could be greatly affected by the glycosylation

272 changes observed leading to altered Treg functions (van Kooyk & Rabinovich, 2008). The glycan

273 remodelling of Tregs could also be important in intrinsic functions of these cells since it is well known

274 that glycosylation has complex pleiotropic effects on cell function and behaviour (Varki, 2017).

275 Previous studies with effector CD4 T cells have also demonstrated activation-dependent increases in

276 Siglec-1 binding, and these were ascribed to a switch from NeuGc to NeuAc and increased α2,3-linked

277 sialic acids following activation (Naito-Matsui et al., 2014; Redelinghuys et al., 2011). In the present

278 study, we were unable to see obvious global changes in either of these parameters using anti-NeuGc

279 antibody staining and MALII plant lectin that binds α2,3 linked sialic acids. The unaltered expression of

280 NeuGc at the cell surface was consistent with RNA-Seq analysis of Cmah, which encodes the enzyme

281 responsible for converting CMP-NeuAc to CMP-NeuGc, did not show a significant change of expression

282 in resting and activated Tregs. It appears, therefore, that regulation of Siglec-1 ligand expression by

283 Tregs is fundamentally different from effector CD4 T cells. However, glycomics analysis did identify

284 minor changes in NeuGc capped glycans upon Treg activation. We also detected clear reductions in

285 NeuGc expression in glycolipid analysis following activation, but as these gangliosides are likely to be

286 relatively minor carriers of sialic acids compared to glycoproteins, their altered levels are probably not

287 detectable with anti-NeuGc antibody and their impact on Siglec-1-dependent binding is unclear.

288 Interestingly, a previous study has found that ST6Gal I showed 4-7 times higher affinity for CMP-NeuGc

289 than CMP-NeuAc, while ST3Gal I showed no significant difference between them (Hamamoto,

290 Kurosawa, Lee, & Tsuji, 1995). This implies that sialyltransferases can have different activities towards

291 CMP-NeuGc and CMP-NeuAc, which could lead to different incorporation of NeuGc and NeuAc to

292 target glycans. It is possible that the specific changes of NeuGc capped glycans were due to altered

293 sialyltransferase gene expression upon Treg activation.

294 While there was no overall change in levels of α2,3-sialylation measured with MALII lectin in activated

295 Tregs, RNA-Seq showed reduced expression of sialyltransferases St3gal1, 2 and 6 and increased

296 expression of St3gal5. These observations suggest that individual glycoconjugates that function as

297 acceptors for these enzymes may differ significantly in α2,3 sialylation between resting and activated

298 Tregs. For example, expression of St3gal6 has been associated with generation of the selectin ligand

299 sialylLex (Yang, Nussbaum, Grewal, Marth, & Sperandio, 2012) and its reduced expression in activated

300 Tregs could be important for modulating homing of these cells to inflamed sites. ST3Gal5 is also known

301 as GM3 synthase which converts lactosyl ceramide to GM3. GM3 can be further converted to α2,8-

302 disialylated GD3 by ST8Sia1 (GD3 synthase). The increased expression of St3gal5 and decreased

303 expression of St8sia1 suggests that GM3 is upregulated on activated Tregs where it could function as

304 a ligand for Siglec-1. GM3 is known to be a dominant ligand for Siglec-1 interactions with retroviruses

305 such as HIV and is able to mediate recognition and internalisation of viral particles by Siglec-1-

306 expressing macrophages (Izquierdo-Useros et al., 2014; Puryear et al., 2013). While future studies are

307 required to investigate expression of GM3 following Treg activation and its potential role in Siglec-1

308 recognition, we were unable to detect GM3 in the glycolipid analysis. This may be due to the low level

309 of St3Gal5 expression relative to other St3Gals, resulting in synthesis of GM3 at levels below our

310 detection threshold.

311 An important aim of this study was to identify glycoproteins on activated Tregs that could function as

312 counter-receptors for Siglec-1 and mediate the biological effects, namely suppression of Treg

313 expansion during autoimmune inflammatory responses. Although pulldown approaches are widely used

314 to identify high affinity protein binding partners, these may not be reliable for identifying counter-

315 receptors for lectins such as Siglec-1 which rely on high avidity interactions with clustered glycan

316 ligands. Clustering could result from organisation of membrane glycoproteins and glycolipids within

317 microdomains of the membrane, as well as through a high density of glycan ligands on individual

318 glycoproteins. Pull-down approaches depend on detergents to lyse cells and solubilise membrane

319 proteins. As such, they disrupt the organization and clustering of glycoproteins in the cell membrane

320 that may be critical for Siglec-1 binding.

321 Proximity labelling is a recently developed method, which has been used widely to study protein-protein

322 interactions in living, intact cells (Branon et al., 2018; Lam et al., 2015; Li et al., 2014; Rees, Li, Perrett,

323 Lilley, & Jackson, 2015, 2017; Roux, Kim, Raida, & Burke, 2012). In general, this approach uses

324 enzyme conjugates as baits that bind cellular target proteins. Addition of suitable substrates leads to

325 the generation of highly reactive radicals that tag neighbouring proteins which can then be enriched and

326 identified by mass spectrometry. This method can also be used to identify Siglec counter-receptors by

327 building Siglec-HRP multimers. We previously prepared in-solution Siglec-1-HRP multimers by mixing

328 Siglec-1-huIgG-Fc fusion protein with HRP-conjugated polyclonal anti-huIgG-Fc and successfully

329 identified glycophorin as a Siglec-1 counter-receptor on human erythrocytes (G. Wu et al., 2017).

330 Another type of in-solution Siglec-HRP multimer was independently developed by another group which

331 identified CD22 counter-receptors on B cells and Siglec-15 counter-receptors on RAW264.7-derived

332 osteoclasts (Chang et al., 2017). In this study, we used two types of multimer for proximity labeling: in-

333 solution multimers and on-bead multimers (Figure 8). Glycoproteins identified by both types of multimer

334 were selected as Siglec-1 counter-receptors.

335 An initial characterization of Siglec-1 counter-receptors revealed they represent 5% of the total

336 membrane proteins and tend to be amongst the more abundantly expressed membrane glycoproteins.

337 We did not see evidence for strong enrichment of any minor proteins which might be expected if Siglec-1

338 had high affinity binding partners requiring protein-protein interactions as well as protein-glycan

339 interactions. Although most lectins rely solely on high avidity glycan interactions for protein binding, P-

340 selectin is an example of a lectin that can use additional, non-glycan interactions to mediate high affinity

341 binding to P-selectin glycoprotein ligand-1 (McEver & Cummings, 1997). Consistent with a glycan-

342 dependent, protein-independent mode of interaction to clustered glycans, Siglec-1 counter-receptors

343 carry a higher density of predicted N-glycosylation sites than non-counter-receptors. O-glycosylation

344 could also be important for Siglec-1 binding, especially for mucin-like proteins that contain a high density

345 of O-glycans. Previously, using a pull-down approach, the mucin-like proteins, CD43 and Muc-1, were

346 identified as Siglec-1 counter-receptors on a T cell line and breast cancer cells respectively (Nath et al.,

347 1999; van den Berg et al., 2001). In the present study, we also identified CD43 as a Siglec-1 counter-

348 receptor on activated Tregs, alongside 48 other glycoproteins. Unlike N-glycosylation, O-glycosylation

349 does not have consensus sequences, making it more difficult to compare O-glycan densities on the

350 counter-receptors versus non-counter-receptors.

351 Identification of potential binding partners allowed us to ask if these were upregulated on activated

352 Tregs which could then help explain the increased binding of these cells to Siglec-1 compared to resting

353 Tregs. Indeed, flow cytometry of several counter-receptors showed increased expression following Treg

354 activation but there were clear exceptions such as CD48 whose expression remained unchanged.

355 RNA-Seq data also indicated that the expression of many Siglec-1 counter-receptors was either

356 unaltered or even reduced following Treg activation. Even if expression of counter-receptors is not

357 changed, increased glycosylation and sialylation following activation could lead to enhanced Siglec-1

358 binding. In support of this possibility, KEGG pathway analysis of the RNA-Seq data indicated that Treg

359 activation was accompanied by increased flux through the N-glycosylation pathway. In particular,

360 activated Tregs had enhanced expression of several components of the OST complex which can be

361 rate-limiting for N-glycosylation of proteins (Rinis et al., 2018). Direct evidence for increased

362 glycosylation of counter-receptors following Treg activation was seen for CD48 and PD-1, both of which

363 showed an increase in apparent MW and in the case of PD-1 this was shown to be sensitive to

364 PNGase F that specifically cleaves N-glycans. Further work is required to investigate the glycosylation

365 changes in more detail and determine if these lead to increased Siglec-1 binding for individual counter-

366 receptors. Glycoprotemics is a powerful strategy which can map glycan structures to each glycosylation

367 site of a glycoprotein (Tissot et al., 2009). However, glycoproteomics requires relatively large amounts

368 of purified target glycoproteins, which, for primary cells such as Tregs, is a major technical challenge

369 (Plomp et al., 2014; G. Wu et al., 2016).

370 The glycan-dependent interaction of Siglec-1 on macrophages with the set of counter-receptors

371 expressed on Tregs could trigger a range of biological activities, with the net outcome being suppression

372 of Treg expansion, as observed previously (C. Wu et al., 2009). The identification of CD25/IL-2 receptor

373 could be relevant in this regard since Tregs do not produce IL-2 but they require IL-2 for survival and

374 proliferation (Horwitz, Zheng, Wang, & Gray, 2008). Modulation of CD25 function via Siglec-1 binding

375 could therefore affect Treg cell expansion under inflammatory conditions. Of particular interest from this

376 perspective are regulatory receptors such as PD-1 which is an important immune-inhibitory receptor. It

377 can be exploited by pathogens and cancer cells to escape T-cell–mediated immune responses

378 (Boussiotis, 2016). Antibodies targeting PD-1 and its ligands are used to treat human cancers in

379 checkpoint immunotherapy (Chen & Han, 2015). PD-1 also plays a vital role in the maintenance of

380 peripheral tolerance by thwarting autoreactive T cells (Francisco, Sage, & Sharpe, 2010). Two PD-1

381 ligands have been identified: PD-L1 (CD274) and PD-L2 (CD273). The results of our proximity labeling

382 studies suggests Siglec-1 could act as an additional PD-1 ligand. PD-1 is a heavily glycosylated protein;

383 both mouse and human PD-1 contain a single Ig-like domain with 4 N-glycosylation sites (Tan et al.,

384 2017). As shown in the present study, deglycosylated PD-1 has a molecular weight of about 30 kDa,

385 which can increase up to 65 kDa when glycosylated. Interestingly, the involvement of Siglec-1 in PD-1

386 signalling has been reported previously (Kirchberger et al., 2005). Human monocyte-derived dendritic

387 cells treated with human rhinoviruses showed upregulated expression of PD-L1 and induced expression

388 of Siglec-1. These dendritic cells had an inhibitory phenotype, which diminished their capacity to

389 stimulate alloreactive T cells and induced a promiscuous and deep T cell anergy. The inhibitory

390 phenotype of these dendritic cells could be reversed by blocking both PD-L1 and Siglec-1 (Kirchberger

391 et al., 2005). In contrast to the function of PD-1 in effector T cells, PD-1 is important for Treg

392 development and activity (Gianchecchi & Fierabracci, 2018). The impact of Siglec-1 on PD-1 function

393 in T cells is an important area for future studies.

394 In conclusion, we provide the first comprehensive analysis of glycan changes in activated Tregs that

395 lead to recognition by the macrophage lectin, Siglec-1. We furthermore provide insights into

396 glycoprotein counter-receptors expressed by these cells that are likely to be important in mediating the

397 biological functions of Siglec-1, by promoting inflammatory responses via suppression of Treg

398 expansion during autoimmune disease of the nervous system.

399

400 Materials and Methods

401 Generation and culture of Tregs

402 RPMI 1640 medium with L-glutamine (Gibco™), FBS (Gibco™), Penicillin-Streptomycin (Gibco™), 2-

403 mercaptoethanol (Gibco™), functional grade anti-mouse CD28 (eBioscience™, clone: 37.51),

404 functional grade anti- mouse IL-4 (eBioscience™, clone: 11B11), functional grade anti-mouse INF-γ

405 (eBioscience™, clone: XMG1.2) were from ThermoFisher, Loughborough, UK. Anti-mouse CD3 (clone:

406 145-2C11) was from Biolegend, London, UK. CD4+ T Cell Isolation Kit was from Miltenyi Biotec Ltd.,

407 Surrey, UK. Mouse IL-2 and human TGF-β were from PeproTech, London, UK. TPP® 12 well plate was

408 from Merck, Dorset, UK.

409 Mouse T cells were isolated from spleen and lymph nodes using CD4+ T Cell Isolation Kits following

410 the supplier’s protocol. Mouse iTregs were generated and grown in 12-well plates. The wells were

411 coated with 1 ml PBS per well containing 10 ug/ml anti-CD3 for 2 hours at 37 °C. The non-bound

412 antibody was removed by washing the plate twice using PBS. The isolated cells were suspended at a

413 concentration of 1-2 X 106/ml in culture medium which is RPMI 1640 with L-glutamine, 10% FBS, 100

414 U/ml Penicillin-Streptomycin, and 50 uM 2-mercaptoethanol. The cells were induced for 4-5 days at 1

415 ml of cell suspension per well with 2 ug/ml functional grade anti-CD28, 10 g/ml functional grade anti-

416 IL-4, 10 g/ml functional grade anti-INF-γ, 20 ng/ml IL-2 and 5 ng/ml TGF-β. Fresh culture medium,

417 anti-CD28, anti-IL-4, anti-INF-γ, IL-2, and TGF-β were supplemented when the cell culture medium

418 became yellow. After induction, the cells were washed twice with culture medium. The cells were re-

419 suspended at a concentration of 106/ml in the culture medium and expanded for 4 days with 20 ng/ml

420 IL-2 and 5 ng/ml TGF-β. Fresh culture medium, IL-2 and TGF- β were supplemented when the culture

421 medium became yellow. To obtain activated Tregs, the expanded Tregs were suspended at a

422 concentration of 106/ml in culture medium with 20 ng/ml IL-2 and 5 ng/ml TGF-β, and were cultured at

423 1 ml cell suspension per well in 12 well plate pre-coated with different concentrations of anti-CD3 in

424 PBS. The anti-CD3 concentration for activated Tregs for proximity labelling, proteomics and RNA-Seq

425 was 0.125 g/ml. Tregs were activated for 3 days for proximity labelling and protemics experiments,

426 and for 36 hours for RNA-Seq experiments.

427

428 Flow cytometry

429 Anti-mouse CD4-PE-Cy7 (eBioscience™, clone: GK1.5), anti-mouse Foxp3-APC (eBioscience™,

430 clone: FJK-16s), anti-mouse PD-1-FITC (clone J43), anti-mouse MHC Class II-PE (eBioscience™,

431 clone: AF6-120.1), anti-mouse CD150-PE (eBioscience™, clone: mShad150),Fixable Viability Dye

432 eFluor™ 450 (eBioscience™), Foxp3 / Transcription Factor Staining Buffer Set, HyClone™

433 FetalClone™ II serum were from ThermoFisher, Loughborough, UK. Anti-mouse CD80-PE (clone: 16-

434 10A1), anti-mouse CD274-PE (clone: MIH7), anti-mouse CD18-PE (clone: M18/2), anti-mouse CD11a-

435 PE (clone: I21/7), anti-mouse CD48-PE (HM48-1), streptavidin-FITC, anti-Neu5Gc antibody kit were

436 from Biolegend, London, UK. FITC conjugated goat anti-human IgG Fc was from Merck, Dorset, UK.

437 Biotinylated plant lectin MAL II and SNA were from Vector® Laboratories, Peterborough, UK. Mouse

438 Siglec-1-human IgG Fc chimera was expressed using stably-transfected Chinese Hamster Ovary cells

439 (Kidder et al., 2013).

440 Before staining for the presence of antigens and glycans, cells were labelled with the Fixable Viability

441 Dye eFluor™ 450, following the supplier’s instructions, and live cells were gated for flow cytometry

442 analysis. All antigen and glycan staining steps were carried out on ice. For staining Foxp3 and NeuGc,

443 Foxp3 / Transcription Factor Staining Buffer Set and the buffer from anti-Neu5Gc antibody Kit were

444 used as staining buffer and washing buffer respectively. For the other staining experiments, 1%

445 HyClone™ FetalClone™ II serum in PBS was used as staining buffer, antibody diluting buffer, washing

446 buffer and cell storage buffer. Foxp3 staining and NeuGc staining were done according to the kit

447 supplier’s instructions. For staining antigens, the cells were washed with staining buffer, stained on ice

448 for 30 min, and non-bound washed for flow cytometry analysis. For staining Siglec-1 ligands, Siglec-1-

449 human IgG Fc chimera was mixed with FITC-conjugated goat anti-human IgG Fc at a final concentration

450 of 1 g/ml in staining buffer, and incubated on ice for 30 min to prepare the pre-complex. The pre-

451 complex was used according to the antibody staining procedure to stain glycan ligands for flow

452 cytometry analysis. Biotinylated MAL II and SNA staining also followed the antibody staining procedure.

453 and detected by flow cytometry using streptavidin-FITC. All flow cytometry data were analysed using

454 FlowJo. A Siglec-1-Fc mutant (SnR97A-Fc) was used as a negative control for Siglec binding

455 experiments. The chicken IgY isotype in the anti-Neu5Gc antibody Kit was used as a negative control

456 for NeuGc staining experiments. Streptavidin-FITC was used as a negative control for MAL II and SNA

457 binding experiments.

458

459 Proximity labelling

460 Protein A beads and µ columns were from Miltenyi Biotec Ltd., Surrey, UK. Tyramide-SS-biotin was

461 from Iris Biotech GmbH, Marktredwitz, Germany. HRP conjugated goat anti-human IgG Fc was from

462 Abcam, Cambridge, UK. Catalase was from Merck,Dorset, UK. H2O2 was from VWR, Leicestershire,

463 UK.

464 Proximity labelling was done using activated Tregs induced from independent experiments, and the gap

465 between these experiments was set no less than 2 weeks. All proximity labelling steps were carried out

466 on ice. In-solution Siglec-1-HRP multimers were prepared by incubating Siglec-1-human IgG Fc

467 chimera with HRP-conjugated goat anti-human IgG Fc at a final concentration of 1 g/ml and 3 g/ml

468 in labelling buffer (1%HyClone™ FetalClone™ II serum in PBS) for 30 min. 5X106 Tregs were washed

469 twice with labelling buffer, mixed with 300 l Siglec-1-HRP multimer and incubated for 30 min.

470 For proximity labelling using on-bead Siglec-1-HRP multimer, 270 l 10 g/ml Siglec-1-human IgG Fc

471 chimera was incubated with 30 l Protein A nanobeads for 60 min. The non-bound were removed by

472 washing the beads on a µ column. The beads were eluted from the µ column using 300 l labelling

473 buffer, mixed with 5X106 Tregs and incubated for 30 min. HRP-conjugated goat anti-human IgG Fc was

474 added to the cells to a final concentration of 3 g/ml and incubated for another 30 min.

475 HRP substrate, Tyramide-SS-biotin and H2O2, were added to a final concentration of 95 µM and 0.01%

476 respectively. The sample was left on ice for 2 min and the reaction was quenched by washing 3 times

477 with 3 ml 100 U/ml catalase in labelling buffer.

478

479 Glycomics

480 PNGase F (cloned from Flavobacterium meningosepticum and expressed by E.coli), CHAPS and DTT

481 were from Roche, Welwyn Garden City, UK. Sep Pak C18 Cartridges were from Waters, London, UK.

482 Slide A Lyzer ® dialysis cassettes (3.5 kDa molecular weight cut off), TMT10plex™ Isobaric Label

483 Reagent Set, TCEP, TEAB were from ThermoFisher, Loughborough, UK. Protein DeglycosylationMix

484 II Kit was from New England Biolabs, Hitchin, UK. LysC was from Alpha Laboratories Ltd, Eastleigh,

485 UK. Trypsin was from Promega, Southampton. UK. Paramagnetic bead (SP3 bead) and other general

486 chemical reagents were from Merck, Dorset, UK.

487 Glycomic analysis of glycoproteins was done following the protocol published previously (Jang-Lee et

488 al., 2006). Briefly, Tregs were homogenized by sonication in 25 mM Tris, 150 mM NaCl, 5 mM EDTA,

489 and 1% CHAPS, pH 7.4, dialysed in dialysis cassettes, reduced by DTT, carboxymethylated by IAA,

490 and digested by trypsin. N-glycans were removed from glycopeptides by PNGase F, which were then

491 isolated by Sep Pak C18 Cartridges and permethylated for mass spectrometry analysis. Glycomic

492 analysis of glycolipids was done following a previous protocol (Parry et al., 2007). Glycomic data were

493 analysed using Data ExplorerTM version 4.6 from AB Sciex.

494

495 Proteomics

496 Proteomic sample preparation was done using paramagnetic bead (SP3 bead) technology following the

497 protocol published previously (Hughes et al., 2014). For histone ruler proteomics, the cells were lysed

498 and sonicated in cell lysis buffer (4% SDS, 10 mM TCEP, 50 mM TEAB in H2O). The proteins were

499 then alkylated using iodoacetamide, cleaned on SP3 beads, and digested to peptides using trypsin and

500 LysC. The peptides were TMT labelled according to the supplier’s instructions, cleaned on SP3 beads,

501 eluted and fractionated by high pH reversed phase chromatography for mass spectrometry analysis.

502 For Siglec-1 counter-receptor identification and membrane proteomics, the biotinylated proteins were

503 enriched and cleaned on streptavidin beads. The glycans were removed using Protein Deglycosylation

504 Mix II under Non-Denaturing Reaction Conditions following the protocol from the supplier. The samples

505 were cleaned on streptavidin beads, eluted using cell lysis buffer and processed using paramagnetic

506 bead (SP3 bead) technology for label free proteomic analysis.

507 The proteomic raw data were imported to MaxQuant Version 1.6.2.3 (Tyanova, Temu, & Cox, 2016) to

508 search protein FASTA files of mouse, human IgG Fc and HRP in Uniprot database. The LFQ intensities

509 of proteins were used for downstream analysis (Cox et al., 2014) by Perseus Version 1.6.0.7 (Tyanova,

510 Temu, Sinitcyn, et al., 2016) and R Version 3.6.3. The R scripts were uploaded to GitHub

511 (https://github.com/fromgangwu/R-scrip-for-Treg-data-analysis.git). Gene Ontology Cellular

512 Component analysis and glycosylation analysis were based on the reviewed mouse protein entries in

513 Uniprot database. Siglec-1 counter receptors were identified through 4 consecutive steps of data

514 filtration. The log2 fold change of the proteins in each sample was first visualized by histogram, and the

515 samples without a proximity labelling tail were excluded. After that, the rest of the data were visualized

516 in a volcano plot and the data points with significant changes were selected. Then, proteins without

517 glycosylation or not located on plasma membrane were removed. Finally, the proteins were mapped

518 back to each of the histogram of log2 fold change and proteins found outside the proximity labelling tail

519 in any histogram were removed. For the identification of total membrane proteins, both total cell lysate

520 and membrane enriched proteins from 3 biological replicates of activated Tregs were used for mass

521 spectrometry analysis. The proteins that identified in at least 2 of 3 biological replicates were selected

522 for Gene Ontology Cellular Component analysis to identify cell membrane proteins.

523

524 RNA Sequencing and data analysis

525 Total RNA from rested and activated regulatory T cells (four biological replicates) were extracted using

526 Qiagen RNeasy Mini kit according to the manufacturer’s instructions and quantified using the Qubit 2.0

527 Fluorometer (Thermo Fisher Scientific Inc, #Q32866) and the Qubit RNA BR assay kit (#Q10210). RNA

528 sequencing libraries were prepared from 500 ng of each total-RNA sample using the NEBNEXT Ultra

529 II Directional RNA Library Prep kit with Poly-A mRNA magnetic isolation (NEB #E7490) according to

530 the manufacturer’s instructions.

531 Poly-A containing mRNA molecules were purified using poly-T oligo attached magnetic beads.

532 Following purification the mRNA was fragmented using divalent cations under elevated temperature

533 and primed with random hexamers. Primed RNA fragments were reverse transcribed into first strand

534 cDNA using reverse transcriptase and random primers. RNA templates were removed and a

535 replacement strand synthesised incorporating dUTP in place of dTTP to generate ds cDNA. The

536 incorporation of dUTP in second strand synthesis quenches the second strand during amplification as

537 the polymerase used in the assay is not incorporated past this nucleotide. AMPure XP beads (Beckman

538 Coulter, #A63881) were then used to separate the ds cDNA from the second strand reaction mix,

539 providing blunt-ended cDNA. A single 'A' nucleotide was added to the 3' ends of the blunt fragments to

540 prevent them from ligating to another during the subsequent adapter ligation reaction, and a

541 corresponding single 'T' nucleotide on the 3' end of the adapter provided a complementary overhang

542 for ligating the adapter to the fragment. Multiple indexing adapters were then ligated to the ends of the

543 ds cDNA to prepare them for hybridisation onto a flow cell, before 11 cycles of PCR were used to

544 selectively enrich those DNA fragments that had adapter molecules on both ends and amplify the

545 amount of DNA in the library suitable for sequencing. After amplification libraries were purified using

546 AMPure XP beads.

547 Libraries were quantified using the Qubit dsDNA HS assay and assessed for quality and fragment size

548 using the Agent Bioanalyser with the DNA HS kit (#5067-4626). RNA-Sequencing was carried out by

549 The Genetics Core of Edinburgh Clinical Research Facility, University of Edinburgh using the NextSeq

550 500/550 High-Output v2 (150 cycle) Kit (# FC-404-2002) with a High Out v2.5 Flow Cell on the NextSeq

551 550 platform (Illumina Inc, #SY-415-1002). 8 libraries were combined in an equimolar pool based on

552 the library quantification results and run across one High-Output Flow Cell. Sequencing resulted in

553 paired-end reads 2 x 75 bp with a median of 50 million reads per sample.

554 The data were analysed by the Data Analysis Group, Division of Computational Biology, University of

555 Dundee. The sequencing data were processed using a snakemake script in a conda environment.

556 Reads were quality controlled using FastQC and MultiQC, mapped to GRCm38 assembly (Ensembl) of

557 the mouse genome using STAR 2.6.1a (Dobin et al., 2013)and number of reads per gene was quantified

558 in the same STAR run. Differentially expressed genes were quantified with edgeR v3.28.0 (Robinson,

559 McCarthy, & Smyth, 2010). A Benjamini-Hochberg multiple test correction was applied to test P-values.

560 The data reproducibility was tested using the distance matrix and clustering based on the read count

561 per gene. The code used to process data is available at https://github.com/bartongroup/MG_GlycoTreg.

562

563 Glycoprotein isolation, glycosidase digestion, Western blotting

564 Cyanogen bromide-activated Agarose, neuraminidase (Vibrio cholerae), and HRP-conjugated rabbit

565 anti-goat antibody were from Merck, Dorset, UK. Monoclonal anti-mouse PD-1 (clone: J43) and

566 NuPAGE SDS-PAGE system was from ThermoFisher,Loughborough, UK. Polyclonal goat anti-mouse

567 PD-1, Polyclonal goat anti-mouse CD48 was from R&D systems, Abingdon, UK. PNGase F was from

568 New England Biolabs, Herts UK.

569 For PD-1 analysis, anti-mouse PD-1 (clone: J43) was conjugated to cyanogen bromide-activated

570 Agarose according to the supplier’s instructions. PD-1 from resting and activated Tregs were affinity

571 purified using the anti-PD1 beads for glycosidase digestion and Western blot analysis. PNGase F

572 digestion was done according to the supplier’s instructions. Sialidase digestion was done in 50 mM

573 sodium acetate, 4 mM calcium chloride, pH 5.5, at 37 °C for 2 h. SDS-PAGE of PD-1 was done using

574 the NuPAGE system, and the protein was transferred to PVDF membrane, blotted with goat anti-

575 mouse PD-1 and HRP-conjugated rabbit anti-goat antibody. CD48 was blotted with goat anti-mouse

576 CD48 and HRP-conjugated rabbit anti-goat antibody.

577 Acknowledgements

578 We would like to thank the staff in our animal facility for their help in routine breeding and

579 maintenance of mice. We thank the University of Dundee Proteomics Facility and the Flow Cytometry

580 and Cell Sorting Facility at the University of Dundee for their assistance. This work was supported by

581 Wellcome Trust Investigator Award (103744) to P.R.C. Glycomics analysis was supported by the

582 Biotechnology and Biological Sciences Research Council, grant number BB/K016164/1.

583 .

584

585 Competing interests

586 The authors have no competing interests.

587

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736 Tyanova, S., Temu, T., & Cox, J. (2016). The MaxQuant computational platform for mass 737 spectrometry-based shotgun proteomics. Nat Protoc, 11(12), 2301-2319. 738 doi:10.1038/nprot.2016.136 739 Tyanova, S., Temu, T., Sinitcyn, P., Carlson, A., Hein, M. Y., Geiger, T., . . . Cox, J. (2016). The Perseus 740 computational platform for comprehensive analysis of (prote)omics data. Nat Methods, 741 13(9), 731-740. doi:10.1038/nmeth.3901 742 van den Berg, T. K., Nath, D., Ziltener, H. J., Vestweber, D., Fukuda, M., van Die, I., & Crocker, P. R. 743 (2001). Cutting edge: CD43 functions as a T cell counterreceptor for the macrophage 744 adhesion receptor sialoadhesin (Siglec-1). J Immunol, 166(6), 3637-3640. 745 doi:10.4049/jimmunol.166.6.3637 746 van Dinther, D., Veninga, H., Iborra, S., Borg, E. G. F., Hoogterp, L., Olesek, K., . . . den Haan, J. M. M. 747 (2018). Functional CD169 on Macrophages Mediates Interaction with Dendritic Cells for 748 CD8(+) T Cell Cross-Priming. Cell Rep, 22(6), 1484-1495. doi:10.1016/j.celrep.2018.01.021 749 van Kooyk, Y., & Rabinovich, G. A. (2008). Protein-glycan interactions in the control of innate and 750 adaptive immune responses. Nat Immunol, 9(6), 593-601. doi:10.1038/ni.f.203 751 Varki, A. (2017). Biological roles of glycans. Glycobiology, 27(1), 3-49. doi:10.1093/glycob/cww086 752 Varki, A., Schnaar, R. L., & Schauer, R. (2017). Sialic acids and other nonulosonic acids. In A. Varki, R. 753 D. Cummings, E. J.D., & e. al (Eds.), Essentials of Glycobiology: Cold Spring Harbor (NY). 754 Wu, C., Rauch, U., Korpos, E., Song, J., Loser, K., Crocker, P. R., & Sorokin, L. M. (2009). Sialoadhesin- 755 positive macrophages bind regulatory T cells, negatively controlling their expansion and 756 autoimmune disease progression. J Immunol, 182(10), 6508-6516. 757 doi:10.4049/jimmunol.0804247 758 Wu, G., Hitchen, P. G., Panico, M., North, S. J., Barbouche, M. R., Binet, D., . . . Haslam, S. M. (2016). 759 Glycoproteomic studies of IgE from a novel hyper IgE syndrome linked to PGM3 mutation. 760 Glycoconj J, 33(3), 447-456. doi:10.1007/s10719-015-9638-y 761 Wu, G., Nagala, M., & Crocker, P. R. (2017). Identification of lectin counter-receptors on cell 762 membranes by proximity labeling. Glycobiology, 27(9), 800-805. doi:10.1093/glycob/cwx063 763 Yang, W. H., Nussbaum, C., Grewal, P. K., Marth, J. D., & Sperandio, M. (2012). Coordinated roles of 764 ST3Gal-VI and ST3Gal-IV sialyltransferases in the synthesis of selectin ligands. Blood, 120(5), 765 1015-1026. doi:10.1182/blood-2012-04-424366 766 Zhang, Y., Roth, T. L., Gray, E. E., Chen, H., Rodda, L. B., Liang, Y., . . . Cyster, J. G. (2016). Migratory 767 and adhesive cues controlling innate-like lymphocyte surveillance of the pathogen-exposed 768 surface of the lymph node. Elife, 5. doi:10.7554/eLife.18156 769 770

771

772 773 774 Figure 1. Flow cytometry showing induction of FoxP3+ CD4+Tregs and their expansion in vitro. Mouse 775 CD4 T cells were isolated from spleen and lymph nodes, stimulated with anti-CD3 and anti-CD28 mAbs 776 for 4-5 days in the presence of anti-IL-4, anti-IFNγ, IL-2 and TGF-β. The cells were then expanded in 777 the presence of IL-2 and TGF-β for another 4 days. The data shown are representative of more than 778 10 experiments carried out. 779

780 781 782 Figure 2. Siglec-1 binding to Tregs depends on Treg activation status. (A) Freshly isolated CD4 T cells 783 were induced to become Tregs and analysed for expression of Siglec-1 ligands using pre-complexed 784 Siglec-1-Fc (Sn-Fc). Compared to the negative control non-binding mutant, Siglec-1-R97A-Fc 785 (SnR97A-Fc), the induced and activated Tregs showed strong Siglec-1 binding. The binding was lost 786 when the cells were rested in IL-2 and TGF-β for 4 days, but was fully recovered when the cells were 787 reactivated with anti-CD3 mAb for 3 days (upper panels). The freshly induced Tregs continued to show 788 strong Siglec-1 binding when kept under activating conditions in the presence of anti-CD3 mAb, but 789 binding was lost when anti-CD3 mAb was withdrawn for 3 days (lower panels). The whole set of 790 experiments was performed twice with similar results. Analysis of Siglec-1 binding to resting and 791 activated Tregs was repeated more than 20 times and in each case binding was much higher to the 792 activated cells. (B) Siglec-1 binding to Tregs depends on the T cell receptor signal strength. Freshly 793 induced Tregs were rested for 4 days and then stimulated using different concentrations of anti-CD3 794 mAb and analysed for Siglec-1 binding at 24, 48 and 72 hours. Binding was both dose- and time- 795 dependent. Similar results were observed in 2 independent experiments.

796

797 798 799 Figure 3. Treg activation does not lead to a global change of cell surface sialylation. α2,3-linked (A) and 800 α2,6-linked sialic (B) acids on Tregs were probed using biotinylated plant lectins MAL II (A) or SNA (B), 801 which were then stained using FITC conjugated streptavidin for flow cytometry analysis. Tregs stained 802 only with FITC conjugated streptavidin were used as a negative control. (C) NeuGc expression was 803 analysed by flow cytometry using chicken IgY anti-NeuGc antibody. A chicken IgY isotype control 804 antibody was used as a negative control. (D) Expression of Siglec-1 ligands was measured using 805 complexes of Siglec-1-huIgG-Fc chimera mixed with FITC-conjugated goat anti-huIgG-Fc. Siglec-1- 806 R97A-huIgG-Fc was used as a negative control. The same batch of Tregs was used for measurement 807 of NeuGc and Siglec-1 ligand expression. The MAL II, SNA and anti-NeuGc experiments were 808 performed twice and similar results were observed. Experiments to measure Siglec-1 binding to resting 809 and activated Tregs were repeated more than 20 times and similar results were observed consistently.

810 811 812 Figure 4. Glycomic analysis of resting and activated Tregs. N-glycans from the Tregs were 813 permethylated and analysed by MALDI-TOF mass spectrometry. The data were acquired in the form of 814 [M+Na]+ ions. Peaks representing hybrid and complex glycans were annotated according to the 815 molecular weight and N-glycan biosynthetic pathways. Treg activation did not result in an overall change 816 of glycosylation, except the glycan at m/z 3026, which had a decreased intensity relative to other 817 glycans following activation of Tregs. A minor increase in Gal-α-Gal structure was observed upon Treg 818 activation at m/z 2809 and m/z 2839, relative to the other biantennary glycans.

819

820 821 Figure 5. Glycomic analysis of glycolipid glycans on resting and activated Tregs. The data were 822 annotated according to the molecular weight, biosynthetic pathways and MS/MS analysis. The NeuAc 823 capped GM1b (m/z 1288) showed a much higher signal relative to other glycans in activated Tregs 824 compared to resting Tregs. 825

826 827 Figure 6. Overview of RNA-Seq data. Four biological replicates of resting and activated Tregs were 828 prepared for RNA-Seq analysis. (A-C) Quality check of the RNA-Seq data shows high data quality 829 with reproducible replicates and major changes between resting and activated Tregs. (D) Volcano plot 830 of the RNA-Seq results. The black dots show glycosylation-related genes, while the grey dots show all 831 other genes. 832

833 834 Figure 7. Normalized mRNA counts for genes involved in sialylation. Resting and activated Tregs from 835 4 biological replicates were analzyed by RNA-Seq. Genes involved in synthesis of (A) α2,3-linked sialic 836 acid, (B) α2,6-linked sialic acid, (C) α2,8-linked sialic acid and (D) modification or removal of sialic acid 837 are listed.

838 839 840 Figure 8. Design of the proximity labelling experiments. (A) Preparation of two types of Siglec-1-HRP 841 (Sn-HRP) multimers. The in-solution multimers were prepared by mixing Siglec-1-huIgG Fc chimera 842 (Sn-Fc) with HRP-conjugated polyclonal anti-huIgG Fc, while the on-bead multimers were prepared by 843 immobilizing Siglec-1-huIgG Fc chimera (Sn-Fc) and HRP conjugated polyclonal anti-huIgG Fc on 50 844 nm protein A beads. (B) Mechanism of proximity labelling. After Siglec-1-HRP multimers bind to

845 Siglec-1 counter-receptors on Treg membrane, tyramide-SS-biotin and H2O2 were added. In the 846 presence of HRP, generation of short-range biotin radicals results in biotinylation of proteins in the 847 immediate vicinity of the multimer. Siglec-1R97A-huIgG Fc (SnR97A-Fc) was used a negative control. 848 (C) Identification of Siglec-1 counter-receptors. Only biotinylated proteins (coloured in red) can be 849 selectively enriched by streptavidin beads and show significant quantitative changes in LFQ intensities 850 in label free quantitative proteomic analysis.

851 852 853 Figure 9. Quantitative proteomic analysis of proteins from proximity labelling experiments. After 854 proximity labelling, the cells were lysed and streptavidin beads were used to enrich biotinylated proteins 855 for proteomic analysis. (A) Histogram of log2 fold changes of proteins between a proximity labelling 856 experiment and its negative control. Only membrane proteins contained a subset with higher log2 fold 857 changes, resulting in a small proximity labelling tail. (B) Volcano plots of the proteomic data shown as 858 log2 fold change and -log10 p values from paired t-test. A small subset of proteins was found to have 859 significant log2 fold changes, which were mainly membrane proteins.

860 861 Figure 10. Characterization of Siglec-1 counter-receptors. (A) Total membrane proteins on activated 862 Tregs were identified by combining the data from proteomics of total cell-surface proteins and 863 proteomics of total cell lysates. Membrane proteins identified in at least 2 of 3 biological replicates were 864 selected. Siglec-1 counter-receptors make up 5.2% of the total membrane proteins. (B) Counter- 865 receptor mRNAs were mapped to the mRNAs of membrane glycoproteins on activated Tregs. The 866 mRNAs with normalized counts above 100 were used for the histogram plot. Each red dot represents 867 a counter-receptor. (C) Siglec-1 counter-receptors were mapped to the histogram of copy number per 868 cell of membrane glycoproteins on activated Tregs. Each red dot represents a counter-receptor. (D) 869 Predicted N-glycosylation sites per Ig like domain for membrane glycoproteins which are not Siglec-1 870 counter-receptors. (E) Predicted N-glycosylation sites per Ig like domain for Siglec-1 counter-receptors.

871 872 Figure 11. Expression and glycosylation of Siglec-1 counter-receptors on resting and activated Tregs. 873 (A) Eight Siglec-1 counter receptors were randomly selected for cytometry analysis. All experiments 874 were performed 3 times and similar results were observed. (B) Normalized mRNA counts of Siglec-1 875 counter receptors were obtained from Treg RNA-Seq data. The result is visualized in a volcano plot. 876 Only a small subset of counter-receptors showed strongly increased gene expression following Treg 877 activation. The counter-receptors selected for flow cytometry analysis are highlighted in red. (C) 878 Western blot showing that CD48 had a higher molecular weight in activated Tregs. (D) Western blot of 879 PD-1 affinity-purified from resting and activated Tregs.. PD-1 showed a dramatic decrease of 880 molecular weight after PNGase F digestion (D), PD-1 from activated Tregs had higher molecular 881 weight (E) and following sialidase treatment, PD-1 from resting and activated Tregs migrated at a 882 slightly reduced molecular weight indicating the presence of sialic acids (F).

883 Table 1. List of Siglec-1 counter-receptors identified by proximity labelling Gene names log2 fold change p value Gene names log2 fold change p value Plxnb2 3.408307894 0.00244823 Atp1b1 1.76026351 0.000101095 Tnfsf11 3.200035166 0.018665401 Trac;Tcra 1.701583438 5.86E-05 Emb 3.075917964 0.007876617 Cd44 1.691378866 0.000167253 Nt5e 3.024345063 0.005280796 St14 1.681174874 0.00233923 Cd200 2.775011745 3.28E-05 Slc4a7 1.678065082 0.003163946 Slc7a5 2.73475518 0.000811395 Itgb7 1.630313414 0.000741549 Tmem2 2.476666371 0.010418948 Amica1 1.609105761 0.002620728 Entpd1 2.451121422 2.31E-05 Tlr2 1.602045805 0.005176914 Icam1 2.39946905 0.001301713 Itgal 1.506602363 5.91E-05 Cd36 2.36357507 0.000360733 Adgre5;Cd97 1.484718926 0.000147198 Alcam 2.349663889 0.001106104 Itgb6 1.45152913 0.000181207 Slc3a2 2.312643859 0.000110602 Itgb2 1.438960296 0.000402497 Cd80 2.26982976 0.001791933 Prnp 1.419583945 0.004513303 Cd69 2.265710589 0.005099445 Ptprc 1.414131506 0.000199609 Ly6e 2.187298039 0.000319921 Spn 1.408694655 0.004129782 Itgb3 2.174073587 0.007612442 Itgb5 1.406146072 0.000999951 Cd48 2.138949606 0.000380995 Il18r1 1.386062293 0.000560288 Bsg 2.042021555 0.001567574 Itgae 1.346952587 8.27E-05 Pecam1 1.938435284 0.011336039 Pdcd1 1.335366065 0.019382887 Slamf1 1.89767313 0.014593304 Cr1l 1.294204529 0.004629208 Thy1 1.882500884 0.000949947 Itgb1 1.266107792 0.00026695 Icos 1.864047709 0.006299044 Cd274 1.205180756 0.003790173 Ncstn 1.832939578 0.003987165 H2-D1;H2-L 1.202468601 0.001008074 Cd5 1.775109343 0.000259476 Il2ra 1.195019215 0.000200364

Atp1b3 1.764153318 0.000248229 884 885 Table shows gene names, log2 fold change compared to signal seen in control group following 886 proximity-labelling with Siglec-1-R97A-Fc complexes and significance levels for 49 counter-receptors 887 identified by proximity labelling. 888 HRP, human IgG Fc and Siglec-1 were among the top 10 significant biggest log2 fold changes, which 889 are not included in this table.