Bat Coronavirus in the Western Indian Ocean

1 Title: Bat coronavirus phylogeography in the western Indian Ocean

2 Running title: Bat coronavirus in the western Indian Ocean

3 Authors: Léa Joffrin*, Steven M. Goodman, David A. Wilkinson, Beza Ramasindrazana, Er-

4 wan Lagadec, Yann Gomard, Gildas Le Minter, Andréa Dos Santos, M. Corrie Schoeman, Ra-

5 jendraprasad Sookhareea, Pablo Tortosa, Simon Julienne, Eduardo S. Gudo, Patrick Mavingui

6 and Camille Lebarbenchon

8 Author affiliations :

9 Université de La Réunion, UMR Processus Infectieux en Milieu Insulaire Tropical (PIMIT)

10 INSERM 1187, CNRS 9192, IRD 249, Sainte-Clotilde, La Réunion, France (L. Joffrin, B. Ra-

11 masindrazana, E. Lagadec, Y. Gomard, G. Le Minter, D.A. Wilkinson, P. Tortosa, P. Mavingui,

12 C. Lebarbenchon)

13 Association Vahatra, Antananarivo, Madagascar (S.M. Goodman, B. Ramasindrazana)

14 Field Museum of Natural History, Chicago, USA (S.M. Goodman)

15 Veterinary Faculty, Eduardo Mondlane University, Maputo, Mozambique (A. Dos Santos)

16 School of Life Sciences, University of Kwa-Zulu Natal, Kwa-Zulu Natal, South Africa (M.C.

17 Schoeman)

18 National Parks and Conservation Service, Réduit, Mauritius (R. Sookhareea)

19 Seychelles Ministry of Health, Victoria, Mahe, Seychelles (S. Julienne)

20 Instituto Nacional de Saúde, Maputo, Mozambique (E.S. Gudo).

22 *Corresponding author: [email protected]

23 UMR PIMIT, 2 rue Maxime Rivière, 97490 Sainte-Clotilde, Reunion Island, France.

24 Tel: +262 262 93 88 00

25 Abstract – 147 words

26 Bats are important reservoirs of zoonotic pathogens, including coronaviruses (CoVs). The

27 Western Indian Ocean (WIO) islands are a biodiversity hotspot with more than 50 bat species.

28 Here we tested 1,099 bats belonging to 39 species from Mozambique, Madagascar, Mauritius,

29 Mayotte, Reunion Island and Seychelles. Based on molecular screening and partial sequencing

30 of the RNA-dependent RNA polymerase gene, a total of 88 bats (8.0% ± 1.6%) tested positive

31 for bat-borne coronaviruses (CoVs), with higher prevalence in Mozambican bats (19.6% ±

32 4.7%) as compared to those sampled on islands (4.2% ± 1.2%). Phylogenetic analyses revealed

33 that a large diversity of α- and β-CoVs are maintained in bat populations of the WIO, some

34 being genetically related to human CoVs (e.g. NL63, MERS). Finally, we found a strong signal

35 of co-evolution between CoVs and their bat host species with limited evidence for host-switch-

36 ing, except for bat species sharing day roost sites.

38 Keywords: bat, coronavirus, islands, tropical, evolution, ecology

39 Text – 3356 words

40 Introduction

41 The burden of emerging infectious diseases has significantly increased over the last decades

42 and is recognized as a major global health concern. In 2018, the World Health Organization

43 (WHO) established the “Blueprint priority disease list”, identifying viruses such as Ebola, Lassa

44 fever, Middle East Respiratory Syndrome (MERS), and Nipah fever as significant threats to

45 international biosecurity (1). This list also highlights the potential pandemic risk from the emer-

46 gence of currently unknown zoonotic pathogens, collectively referring to these unknown threats

47 as “disease X” (1). Investigation of the potential zoonotic pathogens in wild animals, particu-

48 larly vertebrates, is thus critical for emerging infectious diseases preparedness and responses.

49 Bats represent nearly 1,400 species and live on all continents except Antarctica. They pro-

50 vide key ecosystem services such as crop pest regulation, pollination, seed dispersal, and soil

51 fertilization. Bats are also recognized as reservoirs of many zoonotic pathogens. Several bat-

52 borne coronaviruses (CoVs) have recently emerged in humans and livestock with sometimes

53 major impacts to public health. For instance, in 2003, the Severe Acute Respiratory Syndrome

54 (SARS) outbreak in China spread to 30 countries, infecting 8,096 people and leading to 774

55 deaths in less than a year (2). The Middle East Respiratory Syndrome (MERS) is caused by a

56 camel-associated CoV that likely originated from bats, and in 2003 afflicted humans in Saudi

57 Arabia and elsewhere, infecting 2,442 people with 842 associated deaths worldwide (3).

58 Our study area spans geographic locations across the islands of the western Indian

59 Ocean (WIO) and southeastern continental Africa (SECA) (Figure 1). These land areas have

60 diverse geological origins that have influenced the process of bat colonization and species dis-

61 tributions. The ecological settings and species diversity on these islands for bats are notably

62 different. On Madagascar, more than 45 bat species are known to occur, of which more than 80

63 % are endemic to the island. The smaller studied islands of the WIO, Mauritius, Mayotte, Re-

64 union Island, and Mahé (Seychelles), host reduced bat species diversity (e.g. three species on

65 Reunion Island), whereas SECA supports a wide range of bat species. To date, several studies

66 have identified bat-infecting CoVs in countries of continental Africa, including Zimbabwe (4),

67 South Africa (5), and Kenya (6). CoVs have also been reported in fruit bat (Pteropodidae) pop-

68 ulations of Madagascar, where beta-coronaviruses belonging to the D-subgroup were identified

69 in the two bat species Eidolon dupreanum, and Pteropus rufus (7).

70 In this study, we investigated the presence of CoVs in over 1,000 individual bats be-

71 longing to 39 species and sampled on five islands (Madagascar, Mauritius, Mayotte, Reunion

72 Island, and Mahé) and one continental area (Mozambique). Based on molecular screening and

73 partial sequencing of the RNA-dependent RNA polymerase gene, we (i) estimated CoV preva-

74 lence in the regional bat populations, (ii) assessed CoVs genetic diversity, and (iii) identified

75 potential association between bat families and CoVs and evolutionary drivers leading to these

76 associations.

78 Materials and methods

79 Origin of the tested samples

80 Samples obtained from vouchered bat specimens during previous studies in Mozam-

81 bique (February to May 2015), Mayotte (November to December 2014), Reunion Island (Feb-

82 ruary 2015), Seychelles (February to March 2014), Mauritius (November 2012) and Madagas-

83 car (October to November 2014) were tested (8–11) (Technical Appendix). We also collected

84 additional swab samples from several synanthropic bat species on Madagascar, in January 2018

85 (Technical Appendix). A total of 1,099 bats were tested (Figure 1). Details on sample types, bat

86 families, species, and locations are provided in Appendix Table S1.

88 Molecular detection

89 RNA was extracted from 140 μL of each sample using the QIAamp Viral RNA mini kit

90 (QIAGEN, Valencia, California, USA), and eluted in 60 μL of Qiagen AVE elution buffer. For

91 bat organs, approximately 1 mm3 of tissue (either lungs or intestines) was placed in 750 µL of

92 DMEM medium and homogenized in a TissueLyser II (Qiagen, Hilden, Germany) for 2 min at

93 25 Hz using 3 mm tungsten beads, prior to the RNA extraction. Reverse transcription was per-

94 formed on 10 μL of RNA using the ProtoScript II Reverse Transcriptase and Random Primer 6

95 (New England BioLabs, Ipswich, MA, USA) under the following thermal conditions: 70 °C for

96 5 min, 25 °C for 10 min, 42 °C for 50 min, and 65 °C for 20 min (12). cDNAs were tested for

97 the presence of the RNA-dependent RNA-polymerase (RdRp) gene using a multi-probe Real-

98 Time (RT) PCR (13). The primer set with Locked Nucleic Acids (LNA; underlined position in

99 probe sequences) was purchased from Eurogentec (Seraing, Belgium): 11-FW: 5’-TGA-TGA-

100 TGS-NGT-TGT-NTG-YTA-YAA-3’ and 13-RV: 5’-GCA-TWG-TRT-GYT-GNG-ARC-

101 ARA-ATT-C-3’. Three probes were used: probe I (ROX): 5’-TTG-TAT-TAT-CAG-AAT-

102 GGY-GTS-TTY-AT-3’, probe II (FAM): 5’-TGT-GTT-CAT-GTC-WGA-RGC-WAA-ATG-

103 TT-3’, and probe III (HEX): 5’-TCT-AAR-TGT-TGG-GTD-GA-3’. RT-PCR was performed

104 with ABsolute Blue QPCR Mix low ROX 1X (Thermo Fisher Scientific, Waltham, MA, USA)

105 and 2.5 µL of cDNA under the following thermal conditions: 95 °C for 15 min, 95 °C for 30 s,

106 touchdowns from 56 °C to 50°C for 1 min and 50 cycles with 95 °C for 30 s and 50 °C for 60

107 s in a CFX96 Touch™ Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA).

108 Because of the limited size of the sequence generated from the RT-PCR, a second PCR

109 targeting 440 bp of the RdRp gene was performed with 5 µL of cDNA of each positive sample,

110 with the following primer set: IN-6: 5’-GGT-TGG-GAC-TAT-CCT-AAG-TGT-GA-3’ and IN-

111 7: 5’-CCA-TCA-TCA-GAT-AGA-ATC-ATC-ATA-3’ (14). PCRs were performed with the

112 GoTaq G2 Hot Start Green Master Mix (Promega, Madison, WI, USA) in an Applied Biosys-

113 tems 2720 Thermal Cycler (Thermo Fisher Scientific, Waltham, MA, USA). After electropho-

114 resis in a 1.5% agarose gel stained with 2% GelRed (Biotium, Hayward, CA, USA), the re-

115 maining amplicons of the expected size were directly sequenced on both strands by Genoscreen

116 (Lille, France). All generated sequences were deposited in GenBank under the accession num-

117 bers MN183146 to MN183273.

118

119 Statistical analysis

120 We tested the effect of bat family, species, sex, as well as sampling location and roosting

121 habitat (caves, outdoor or buildings) on the probability for detecting CoV RNA using Pearson

122 χ² tests. The effect of bat age (adult vs juvenile) and female reproductive status (pregnant vs not

123 pregnant) was investigated for species in which we detected at least 15 CoV positive individu-

124 als. Finally, the effect of sampling during the wet (February) or dry (May) season was investi-

125 gated in Mozambique. Analyses were conducted with R v3.5.1 software (15).

126

127 Phylogenetic analyses

128 Sequences obtained with the second PCR system (14) were edited with the Chromas

129 Lite Software package version 2.6.4 (16). We explored CoV diversity of the sequences with

130 pairwise identity values obtained from seqidentity function in R bio3d package v2.3-4 (17) and

131 identified the most similar CoV RdRp sequences referenced in GenBank using BLASTN

132 2.2.29+. An alignment was then generated using the 51 nucleotide sequences obtained in this

133 study and 151 reference CoV sequences from a large diversity of host family and geographic

134 origins (Europe, Asia, Oceania, America and Africa), with CLC Sequence viewer 8.0 Software

135 (CLC Bio, Aarhus, Denmark). A phylogenetic tree was obtained by maximum likelihood using

136 MEGA Software v10.0.4 (18), with 1,000 bootstrap iterations, and the best evolutionary model

137 for our dataset as selected by modelgenerator v0.85 (19).

138 Host-virus associations were investigated using the phylogeny of WIO bats and their

139 associated CoVs. Bat phylogeny was generated from an alignment of 1,030 bp of mitochondrial

140 Cytochrome b (Cyt b) gene sequences downloaded from GenBank and sequenced for this study

141 (Appendix Table S2), for each CoV positive bat species with available sequence data. Finally,

142 bat and pruned CoV phylogenies based on each 393 bp RdRp unique sequence fragment were

143 generated by Neighbor-Joining with 1,000 bootstrap iterations, using CLC Sequence viewer

144 8.0 Software (CLC Bio, Aarhus, Denmark)(20). Phylogenetic congruence was tested (21) to

145 assess the significance of the coevolutionary signal between bat host species and CoVs se-

146 quences, using ParaFit with 999 permutations in the ‘ape’ package v5.0 in R 3.5.1 (22). Tan-

147 glegram representations of the co-phylogeny were visualized using the Jane software v4.01

148 (23).

149

150 Results

151 Prevalence of CoV

152 In total, 88 of the 1,099 bat samples tested positive for CoV by RT-PCR (mean detection

153 rate ± 95% confidence interval: 8.0% ± 1.6%). The prevalence of positive bats was different

154 according to the sampling locations (χ² = 70.2; p<0.001), with a higher prevalence in Mozam-

155 bique (19.6% ± 4.7%) than on all WIO islands (4.2% ± 1.2%) (Figure 2). A significant differ-

156 ence in the prevalence of positive bats was also detected between families (χ² = 44.2; p<0.001;

157 Appendix Figure S1). The highest prevalence was observed in the families Nycteridae (28.6 %

158 ± 23.6%), Rhinolophidae (24.6% ± 10.5%), Hipposideridae (11.9% ± 6.9%), and Rhinonycter-

159 idae (10.7% ± 5.5%). Bat species (χ² = 156.27; p<0.001; Appendix Figure S2) and roosting

160 habitat (χ² = 23.76; p<0.001; Figure 3) had a significant effect on the probability of CoVs de-

161 tection, but not host sex (χ² = 2.32; p = 0.13). For Mozambican Hipposideros caffer and Mops

162 condylurus, bat age (adult vs juvenile) had no effect on the probability of CoVs detection (χ² =

163 0.1; p = 0.8 and χ² = 0.1; p = 0.7, respectively). For Chaerephon pusillus, female reproductive

164 status (pregnant or not pregnant) had no effect on the probability of CoVs detection (χ² = 2.50;

165 p = 0.3). Finally, the prevalence of CoV positive bats in Mozambique was significantly different

166 (χ²= 21.5; p<0.001; Appendix Figure S3) between February (35.1% ± 4.9%) and May (11.0%

167 ± 2.3).

168

169 RdRp sequence diversity

170 Of the 88 positive samples, we obtained 77 partial RdRp sequences using the RT-PCR

171 detection system (179 bp) and 51 longer partial RdRp sequences using the standard PCR system

172 (440 bp): this latter was subsequently used for phylogenetic analyses. Details of the sequenced

173 CoV-positive samples are given in Appendix Table S3. Pairwise comparison of these 51 se-

174 quences revealed 28 unique sequences, and sequences similarities ranging from 60.2% to

175 99.8%. The lowest sequence similarity was found in Mozambique (60.2% to 99.8%), then in

176 Madagascar (64.0% to 99.8%). No genetic variation was observed for samples from Mayotte

177 and Reunion Island.

178

179 Phylogenetic structure of CoVs

180 Sequence comparison indicated that WIO bats harbor a high diversity of both α and β-

181 CoVs, with conserved clade groups clustering mostly by bat family (Figure 4). Specifically, 25

182 sequences were identified as α-CoVs, and three sequences were genetically related to the β-

183 CoVs. For α-CoVs, all sequences detected in our study of members of the family Molossidae

184 formed a highly supported monophyletic group, including CoV sequences from Molossidae

185 bats previously detected in continental Africa (Figure 5). CoVs detected in Mops condylurus

186 (Mozambique), Mormopterus francoismoutoui (Reunion Island), Chaerephon pusillus and

187 Chaerephon sp. (Mayotte), and Mormopterus jugularis (Madagascar) shared 90% - 98% nu-

188 cleotide similarity with a CoV detected in Chaerephon sp. in Kenya (Appendix Table S4). All

189 CoVs found in Miniopteridae clustered in a monophyletic group, including Miniopteridae CoVs

190 sequences from Africa, Asia, and Oceania (Appendix Table S4). The great majority of α-CoVs

191 detected in Rhinolophidae bats clustered in two monophyletic groups (Figure 4); one with Af-

192 rican Rhinolophidae CoVs and one with Asian Rhinolophidae CoVs. We additionally detected

193 one CoV from Rhinolophus rhodesiae, which was 100% similar to a Miniopteridae CoV from

194 this study. Rhinonycteridae CoVs formed a single monophyletic group with NL63 Human

195 CoVs. The Rhinonycteridae CoVs detected clustered with NL63-related bat sequences found

196 in Triaenops afer in Kenya (Figure 6) and showed 85% similarity to NL63 Human CoVs (Ap-

197 pendix Table S4). Hipposideridae α-CoVs mainly clustered into a single monophyletic group.

198 Hipposideridae CoV sequences from this study clustered with a 229E Human CoV-related bat

199 sequence found in Hipposideros vittatus from Kenya (Figure 7) and demonstrated 93% simi-

200 larity to 229E Human CoV (Appendix Table S4).

201 Regarding the β-CoVs, two sequences obtained from Nycteris thebaica clustered in the

202 C-subgroup of β-CoVs together with other CoVs previously reported in African Nycteris sp.

203 bats (Figure 8). The sequences showed 88% nucleotide identity to a β-C CoV found in Nycteris

204 gambiensis in Ghana (Appendix Table S4). Rousettus madagascariensis CoV clustered with

205 Pteropodidae CoVs belonging to the D-subgroup of β-CoVs (Figure 9). BLAST queries against

206 the NCBI database showed 98% nucleotide identity between CoV sequences from Rousettus

207 madagascariensis and a β-D CoV sequence detected in Eidolon helvum from Kenya (Appendix

208 Table S4).

209

210 Co-phylogeny between bats and CoVs

211 Co-phylogeny tests were conducted using 11 Cyt b sequences obtained from the 11

212 CoVs positive bat species and 27 partial CoV RdRp sequences (440 bp). Results supported co-

213 evolution between the WIO bats and their CoVs (ParaFitGlobal = 0.04; p = 0.001) and a high

214 level of phylogenetic congruence (Figure 10).

215

216 Discussion

217 We provide evidence for a high diversity of CoVs in bat populations on WIO islands. The

218 overall prevalence of CoV positive bats was consistent with other studies from continental Af-

219 rica (5) and island systems in the Australasian region (24). However, in the case of WIO islands,

220 prevalence rates showed considerable variations based on bat family, species, landmass, and

221 season.

222 We found a significant effect of the roosting habitat on the probability of CoV detection,

223 with higher detection in bats occupying caves and buildings than outdoor habitats. Bats use

224 different types of day roosts sites, including caves, rock crevices, tree cavities, forest vegetation,

225 or synanthropic structures (25). Critical for this study, roosting site choice could be a risk factor

226 for infection of bats (26). This was indeed the case with higher detection rates for bats using

227 caves and buildings, as compared to other roost sites. Shelters like caves or buildings (bridges,

228 houses, etc.) may protect excreted viral particles from rainfall, temperature, humidity variation,

229 and ultraviolet radiation (27,28). Thus, these roost types may facilitate the maintenance and

230 transmission of viruses between syntopic species. Moreover, the accumulation of guano in these

231 confined environments compared to outdoor habitats could favor virus transmission in bat pop-

232 ulations. However, our investigation of infectious CoV particles in bat guano only detected

233 RNA, without demonstrating the infectious potential of viral particles from this environment

234 (29).

235 Host specificity is well known for some bat CoVs subgenera (30,31). For example, β-C

236 CoVs are largely associated with Vespertilionidae bats, whereas β-D CoVs are found mostly in

237 Pteropodidae (31,32). In our study, we showed that WIO bats harbor genetic structured CoVs,

238 of both α-CoV and β-CoV subclades, clustering mostly by bat family. In the new CoV taxon-

239 omy based on full genomes proposed by the International Committee of Taxonomy of Viruses

240 (ICTV), α-CoVs and β-CoVs are split in subgenera mostly based on host families (33), reflected

241 in the subgenera names (e.g. Rhinacovirus for a Rhinolophidae α-CoV cluster, Minuacovirus

242 for a Miniopteridae α-CoV cluster, Hibecovirus for an Hipposideridae β-CoV cluster). Alt-

243 hough our classification was based on a partial sequence of the RdRp region, we identified

244 sequences from samples belonging to four of these subgenera (Minuacovirus, Duvinacovirus,

245 Rhinacovirus, and Nobecovirus) and three that could not be classified according to this taxo-

246 nomic scheme hence representing unclassified subgenera (we propose “Molacovirus”, “Nycbe-

247 covirus”, and “Rhinacovirus2”).

248 In the context of WIO islands, we expect a strong geographical influence on CoVs diversity

249 rather than on host specificity, with independent evolution of CoVs on each island because of

250 spatial isolation and endemism. Anthony et al. (32) suggested that the dominant evolutionary

251 mechanism for African CoVs was host switching. In our study, congruence between host and

252 viral phylogenies suggests a strong signal for co-evolution between WIO bats and their associ-

253 ated CoVs. This strongly suggests that individual bat species harbor specific CoV lineages, but

254 CoV transmission between different bat species may nevertheless occur infrequently. However,

255 the geographical influence seems to occur within family specific groups, as in the WIO Molos-

256 sidae CoV clade. Endemism resulting from geographic isolation may then have favored viral

257 diversification within family specific viral lineages.

258 Although co-evolution could be the dominant mechanism, host-switching may take place

259 in certain situations. For example, in Mozambique we found a potential Miniopteridae CoV in

260 a Rhinolophidae bat co-roosting with Miniopteridae in the same cave. These host-switching

261 events could be favored when several bat species roost in the same portion of a cave in sym-

262 patria (34). A similar scenario was described on Australia where Miniopteridae CoV was de-

263 tected in Rhinolophidae bats (24). These infrequent host-switching events show that spillovers

264 can happen but suggest that viral transmission is not maintained independently within the spill-

265 over host. Thus, the co-evolution of virus and host might have resulted in strong adaptation of

266 the CoVs to the specific bat species. In addition, viral factors (mutation rate, recombination

267 propensity, replication ability in the cytoplasm, changes in the ability to bind host cells), envi-

268 ronmental factors (climate variation, habitat degradation, decrease of bat preys), and phyloge-

269 netic relatedness of host species are also critical for the viral establishment in a novel host (35).

270 Nevertheless, apparent evidence of host switching as a dominant mechanism of CoV evolution

271 could be an artifact of a lack of data for some potential bat hosts, leading to incomplete phylo-

272 genetic reconstructions (32).

273 Several bat CoVs we identified in Rhinonycteridae and Hipposideridae from Mozambique

274 had between 85% and 93% nucleotide sequence similarity with NL63 Human CoVs and 229E

275 Human CoVs, respectively. These two human viruses are widely distributed in the world and

276 associated with mild to moderate respiratory infection in humans (36). Tao et al. established

277 that the NL63 Human CoVs and 229E Human CoVs have a zoonotic recombinant origin from

278 their most recent common ancestor, estimated to be about 1,000 years ago (37). During the past

279 decade, they were both detected in bats in Kenya, and in Ghana, Gabon, Kenya, and Zimbabwe,

280 respectively (4,6,38,39). Moreover, CoVs notably similar to both NL63 and 229E have been

281 described in Kenya and Mozambique bats, suggesting an East African bats origin of these hu-

282 man viruses. Intermediate hosts are important in the spillover of CoVs, despite gaps in direct

283 and indirect transmission routes of bat infectious agents to secondary hosts (40). This hypothe-

284 sis has been formulated for the 229E Human CoV, with an evolutionary origin in Hippo-

285 sideridae bats and with camelids as intermediate hosts (39). Similarly, the spillover of NL63

286 from Rhinonycteridae bats to humans might have occurred through a currently unidentified

287 intermediate host (6,41,42). Because receptor recognition by viruses is the first essential cellular

288 step to infect host cells, CoVs may have spilt over into humans from bats through an interme-

289 diate host possibly due to mutations on spike genes (6,43). Further investigations of CoVs in

290 Kenyan and Mozambican livestock and hunted animals could potentially provide information

291 on the complete evolutionary and emergence history of these viruses before their establishment

292 in humans.

293 MERS-like CoV, with high sequence similarity (>85%) to human and camel strains of

294 MERS-CoV, have been detected in Neoromicia capensis in South Africa and Pipistrellus cf.

295 hesperidus in Uganda, suggesting a possible origin of camel MERS-CoV in Vespertilionidae

296 bats (5,32,44). This family has been widely studied, with 30% of all reported bat CoVs se-

297 quences from the past 20 years coming from vespertilionids (45), including MERS-like CoVs.

298 No members of this family were positive for CoV in our study, which may be associated with

299 the low number of individuals sampled; additional material is needed to explore potential

300 MERS-like CoV on Madagascar.

301 CoV transmission risk also depends on ecological factors and human actions such as en-

302 croachment, landscape uses, and cultural traditions. On Madagascar, for example, bats are

303 hunted for commercial or personal consumption (46,47). Further, certain Malagasy ethnic

304 groups have cultural rituals associated with caves, including those with bat roosts (47). Indirect

305 contact with contaminated environments such as bat guano may also increase infection risk.

306 Given the strong co-evolution of CoV and bats in the WIO, there is the potential of human

307 populations being exposed to CoV spillovers, which in part depends on contact with day roost

308 sites and different ecological contexts, as well as the bat families present. Moreover, there is

309 evidence of a relationship between biodiversity loss and the risk of infectious disease emer-

310 gence (48).

311 At a worldwide scale, epidemiological studies with serological surveys on human popula-

312 tions are necessary to investigate potential CoV transmission from bats to humans (49,50) and

313 should be considered on WIO islands. Even if CoV prevalence in bats is higher on Madagascar

314 than other oceanic islands around the world, the high CoV genetic diversity in Malagasy bats

315 could complicate the development of detection protocols in human populations. On oceanic

316 islands, however, the development of serological tests for particular coronaviruses found in

317 endemic bat species is achievable, and underlines the need to develop virus surveillance in local

318 populations based on results from bat studies.

319

320 Acknowledgments

321 We are very grateful to S. Muradrasoli for recommendations on the optimization of the multi-

322 probe PCR protocol as well as for providing PCR controls and to C. Cordonin for providing bat

323 Cyt b sequences. L. Biscornet, C. Dionisio, L. Domergue, M. Dietrich, T. Mbohoahy, T.

324 Nekena, J. Rakotoarivelo, M. Rakotomanga, C. F. Rakotondramanana, and A. Randrenjarison

325 are thanked for their assistance in the field. We also thank S. Bos and A. Hoarau for their help

326 in the laboratory, and K. Dellagi and H. Pascalis for the development and the management of

327 the ‘partenariat Mozambique-Réunion dans la recherche en santé: pour une approche intégrée

328 d’étude des maladies infectieuses à risque épidémique (MoZaR)’ research program.

329

330 Funding

331 Field research was funded by the ‘Pathogènes associés à la Faune Sauvage Océan Indien (FS-

332 OI)’, the ‘Leptospirose Ocean Indien (LeptOI)’, the ‘Paramyxovirus Océan Indien (Para-

333 myxOI)’, and the ‘Partenariat Mozambique-Réunion dans la recherche en santé : pour une ap-

334 proche intégrée d’étude des maladies infectieuses à risque épidémique (MoZaR)’ programs

335 (Fond Européen de Développement Régional, Programme Opérationnel de Coopération Terri-

336 toriale). Fieldwork on Mayotte was funded by the ‘Centre National de la Recherche Scien-

337 tifique’ (Projets Exploratoires Premier Soutien BATMAN). Molecular analyses were finan-

338 cially supported by tutorship institutions of the UMR PIMIT. LJ is a recipient of a ‘Région

339 Réunion, European Regional Development Funds (FEDER 2014-2020)’ PhD fellowship. BR’s

340 post-doctoral fellowship was supported by the ‘Run Emerge’ European Union’s Seventh

341 Framework Program (FP7/2007–2013; Grant agreement NO 263958), the ‘Fonds de Coopé-

342 ration Régionale, Prefecture de La Réunion’, and The Field Museum of Natural History, Chi-

343 cago, through the Ralph and Marian Falk Medical Research Trust. CL is supported by a ‘Chaire

344 mixte : Université de La Réunion – INSERM’. The funding agencies were not involved in the

345 study design, implementation or publishing of this study, and the research presented herein

346 represents the opinions of the authors but not necessarily the opinions of the funding agencies.

347

348 References

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477 50. Wang L-F, Anderson DE. Viruses in bats and potential spillover to animals and hu- 478 mans. Curr Opin Virol. 2019;34:79–89. 479

480 Data Accessibility: 481 - DNA sequences: Genbank accessions MN183146 to MN183273

482

483 Author Contributions: CL and LJ conceived and designed the study. BR, CL, DAW, EL, 484 LJ, PT, SMG and YG collected biological material on Madagascar. SMG, RS and YG col- 485 lected biological material on Mauritius. BR, EL and GLM, collected biological material on 486 Mayotte. SMG, GLM, ADS and MCS, collected biological material in Mozambique. BR, 487 DAW, EL and GLM collected biological material on Reunion Island. EL, GLM, SJ and YG 488 collected biological material in the Seychelles. LJ performed the molecular analyses. CL and 489 DAW analyzed the data. CL and LJ wrote the paper. SMG, ESG, MP, PM and SJ contributed 490 to the project management in Malagasy, Mauritian, Seychelles, Mozambican, and French in- 491 stitutions. All authors edited, read, and approved the final manuscript. 492

493

494

495 Figures

496

497 Figure 1. Geographic distribution of the tested samples. N: number of bats sampled for each 498 locality.

499

500 Figure 2. Mean CoV prevalence (± 95% confidence interval) in bats in the western Indian 501 Ocean. Pairwise test; ***: p<0.001; NS: p>0.05, not significant.

502

503 Figure 3. Mean CoV prevalence (± 95% confidence interval) as function of the type of roost- 504 ing habitat. Pairwise test; ***: p<0.001; **: p<0.01; NS: p>0.05, not significant.

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518 Figure 4. Maximum Likelihood (ML) consensus tree derived from 202 coronavirus (CoV) 519 RNA-dependent RNA-polymerase partial nucleotide sequences (393 bp). Colored circles at 520 the end of branches indicate bat family origin. Sequences in bold refer to bat CoVs detected in 521 this study. Only bootstrap values >0.7 are indicated on the tree. Scale bar indicates mean 522 number of nucleotide substitutions per site. The tree was generated with the General Time Re- 523 versible evolutionary model (GTR+I+Г, I = 0.18, α = 0.64) and 1000 bootstrap replicates.

bioRxiv preprint doi: https://doi.org/10.1101/742866; this version posted September 4, 2019. 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. ● Bat_CoV_FMNH_229303_Mozambique_ Mo.condylurus _2015_MN183182 1● Bat_CoV_FMNH_229296_Mozambique_ Mo.condylurus _2015_MN183179 ● Bat_CoV_FMNH_229266_Mozambique_ Mo.condylurus _2015_MN183180 ● Bat_CoV_FMNH_229293_Mozambique_ Mo.condylurus _2015_MN183181 ● Hipposideridae 1 ● Chaerephon_bat_coronavirus_KY22_ Ch.sp _Kenya_2006_HQ728486 ● BtCoV_NCL_MCO1_ Mo.condylurus _South_Africa_2012_KF843853 ● Megadermatidae ● PREDICT_GVF_CM_ECO70102_ Mo.condylurus _Cameroon_2013_KX284982 1 ● Bat_CoV_MAY027_Mayotte_ Ch.pusillus _2014_MN183176 ● Miniopteridae ● Bat_CoV_MAY025_Mayotte_ Ch.pusillus _2014_MN183175 ● Bat_CoV_MAY051_Mayotte_ Ch.sp _2014_MN183174 ● Molossidae 1● Bat_CoV_MAY015_Mayotte_ Ch.pusillus _2014_MN183173 ● Bat_CoV_MAY033_Mayotte_ Ch.pusillus _2014_MN183178 ● Mormoopidae 1 ● Bat_CoV_MAY004_Mayotte_ Ch.pusillus _2014_MN183177 ● Nycteridae ● Bat_CoV_UADBA_33728_Madagascar_ Mo.jugularis _2013_MN183183 ● Bat_CoV_UADBA_33735_Madagascar_ Mo.jugularis _2013_MN183186 ● Phyllostomidae ● Bat_CoV_FMNH_222698_Madagascar_ Mo.jugularis _2013_MN183185 1● Bat_CoV_FMNH_222691_Madagascar_ Mo.jugularis _2013_MN183187 ● Pteropodidae ● Bat_CoV_UADBA_33733_Madagascar_ Mo.jugularis _2013_MN183184 0.9 ● Bat_CoV_RB369_Reunion_ Mo.francoismoutoui_2015_MN183188 ● Rhinolophidae ● Bat_coronavirus_POA_ Uknown _Brazil_2012_144_KC110778 ● Bat_coronavirus_2173_ Cy.planirostris _Brazil_2014_KU552078 ● Rhinonycteridae ● Bat_CoV_FMNH_228953_Mozambique_ Rh.rhodesiae _2015_MN183148 ● Vespertillionidae ● Bat_CoV_FMNH_228945_Mozambique_ Rh.rhodesiae _2015_MN183146 ● Bat_CoV_FMNH_228957_Mozambique_ Rh.rhodesiae _2015_MN183150 1● Bat_CoV_FMNH_228962_Mozambique_ Rh.rhodesiae _2015_MN183147 ● Bat_CoV_FMNH_228955_Mozambique_ Rh.rhodesiae _2015_MN183151 Human CoV ● Bat_CoV_FMNH_228956_Mozambique_ Rh.rhodesiae _2015_MN183149 1 ● Bat_CoV_FMNH_228951_Mozambique_ Rh.sp _2015_MN183152 1● Bat_CoV_FMNH_228939_Mozambique_ Rh.lobatus _2015_MN183154 1 1● Bat_CoV_FMNH_228952_Mozambique_ Rh.rhodesiae _2015_MN183153 ● Bat_CoV_FMNH_228940_Mozambique_ Rh.lobatus _2015_MN183155 ● Bat_CoV_FMNH_228941_Mozambique_ Rh.lobatus _2015_MN183156 ● Kenya bat_coronavirus_BtKY83_ Rh.sp _Kenya_2007_GU065427 ● Bat_coronavirus_KY43_ Ca.cor _Kenya_2006_HQ728480 ● Bat_coronavirus_BB98−15_ Rh.blasii _Bulgaria_2008_GU190232 1● Bat_COV_HKU10_TLC1347A_ Hi.pomona _China_2010_JQ989273 ● Bat_COV_HKU10_175A_ Ro.leschenaultii _China_2005_JQ989271 0.9 ● Bat_coronavirus_2231_ Hi.diadema _Philippines_2010_AB683971 1● BtRf_AlphaCoV_HuB2013_ Rh.ferrumequinum _China_2013_KJ473807 ● BtMs_AlphaCoV_GS2013_ My.sp _China_2013_KJ473810 ● BtCoV_4307_1_ Rh.affinis _China_2013_KP876527 0.9● Bat_coronavirus_Aju21_ Ar.jamaicensis _Costa_Rica_2012_KC779226 1● BtCoV_KP565_ Ar.jamaicensis _Panama_2010_JQ731786 ● BtCoV_KP256_ Ar.jamaicensis _Panama_2010_JQ731784 0.9● Bat_CoV_FMNH_228980_Mozambique_ Mi.mossambicus _2015_MN183160 0.9● Bat_CoV_FMNH_228949_Mozambique_ Rh.rhodesiae _2015_MN183159 0.9● Bat_CoV_FMNH_228978_Mozambique_ Mi.mossambicus _2015_MN183157 1● Bat_CoV_FMNH_228969_Mozambique_ Mi.mossambicus _2015_MN183158 ● Miniopterus bat_coronavirus_KY27_ Mi.natalensis _Kenya_2006_HQ728484 ● Bat_CoV_FMNH_228971_Mozambique_ Mi.mossambicus _2015_MN183161 1 0.7● BtMf_AlphaCoV_AH2011_ Mi.fuliginosus _China_2011_KJ473795 1● Bat_coronavirus_1A_ Mi.magnater _China_2005_DQ666337 10.8● BtMf_AlphaCoV_HuB2013_ Mi.fuliginosus _China_2013_KJ473803 ● Miniopterus bat_coronavirus_1A_ Mi.sp _HongKong_2008_EU420138 ● Bat_coronavirus_1B_ Mi.sp _HongKong_2008_EU420137 0.9 1● Bat_coronavirus_Anlong_171_ Mi.schreibersii _China_2012_KF294275 ● Bat_coronavirus_HKU7_ Mi.magnater _DQ249226 1● Bat_coronavirus_CoV100_ Rh.megaphyllus _Australia_2007_EU834953 ● Bat_coronavirus_CoV132_ Mi.australis _Australia_2007_EU834954 ● Bat_coronavirus_HKU8_ Mi.pusillus _China_2004_DQ249228 ● Alphacoronavirus_BtCoV_MSTM2_ Mi.natalensis _South_Africa_2010_KF843851 1 ● BNM98_30_ Ny.leisleri _Bulgaria_2008_GU190239 ● Alphacoronavirus_ Ny.lasiopterus _Spain_2007_HQ184055 ● Pip1_Cr_ Pi.pipistrellus _France_2014_KT345294 1 ● BtCoV_VH_NC2_ Ne.capensis _South_Africa_2012_KF843854 ● BtNv_AlphaCoV_SC2013_ Ny.velutinus _China_2013_KJ473809 alpha ● Alphacoronavirus_Iprima_ Pi.kuhlii _Spain_2007_HQ184058 1 ● Bat_coronavirus_VM105_ My.dasycneme _Netherlands_2006_GQ259968 1 ● Bat_coronavirus_D5.70_ Pi.pygmaeus _Germany_2007_EU375867 ● Bat_coronavirus_Moxy4235_IT_16_ My.oxygnathus _Italy_2016_KY780395 ● Bat_coronavirus_HKU6_ My.pilosus _China_2004_DQ249224 ● CoV_75_55_L01_R1_CoV_BAT_VN_ Sc.kuhlii _Vietnam_KX092163 1● Bat_coronavirus_ My.lucifugus _Canada_2010_KY799179 ● Rocky_Mountain_Bat_Coronavirus_11_ My.lucifugus _USA_2011_EF544563 1 ● Bat_coronavirus_P1ab_ Mu.leucogaster _China_2013_KU182966 ● Lushi_Ml_bat_CoV_Neixiang_23_ Mu.leucogaster _China_2012_KF294375 ● PEDV_Porcine_Epidemic_COV_ Su.domesticus _1993_NC003436 1● BtCoV_LUX15_A_46_ My.emarginatus _Luxembourg_2015_KY502383 1 ● BtCoV_LUX15_A_59_ My.emarginatus _Luxembourg_2015_KY502386 1 ● Alphacoronavirus_13rs384_31_ My.blythii _Italy_2012_KF312400 1● Bat_coronavirus_Anlong_46_ Rh.pusillus _China_2013_KY770855 ● Bat_coronavirus_Anlong_60__ Ia.io _China_2013_KY770857 1 ● BtCoV_BRA182_ Mo.rufus _Brazil_2009_JQ731799 ● BtCoV_BRAP103_ Mo.currentium _Brazil_2009_JQ731800 ● Bat_CoV_FMNH_228887_Mozambique_ Tr.afer _2015_MN183162 ● NL63_related_bat_coronavirus_BtKYNL63_15_ Tr.afer _Kenya_2008_KY073746 ● Bat_CoV_FMNH_228891_Mozambique_ Tr.afer _2015_MN183163 ● Bat_CoV_FMNH_229232_Mozambique_ Tr.afer _2015_MN183165 ● Bat_CoV_FMNH_229212_Mozambique_ Tr.afer _2015_MN183164 1● Bat_CoV_FMNH_229241_Mozambique_ Tr.afer _2015_MN183166 ● Bat_CoV_FMNH_228892_Mozambique_ Tr.afer _2015_MN183167 0.7 ● Bat_CoV_FMNH_228888_Mozambique_ Tr.afer _2015_MN183168 1 Human_coronavirus_NL63_isolate_HCOV_005_ Ho.sapiens sapiens _ Kenya_2009_KP112154 Human_Cov_NL63_ Ho.sapiens sapiens_Netherlands_2004_NC005831 1● NL63_related_bat_coronavirus_BtKYNL63_9a_ Tr.afer _Kenya_2010_KY073744 ● Bat_CoV_FMNH_229243_Mozambique_ Tr.afer _2015_MN183169 0.8 ● Bat_CoV_FMNH_228933_Mozambique_ Hi.caffer _2015_MN183172 1● Bat_CoV_FMNH_228926_Mozambique_ Hi.caffer _2015_MN183171 ● Bat_CoV_FMNH_228981_Mozambique_ Hi.caffer _2015_MN183170 1 Alpaca_respiratory_coronavirus_isolate_CA08_1_ Vi.pacos _USA_2008_JQ410000 0.7 Camel229E_CoV_AC04_ Ca.dromedarius _SouthKorea_2014_KT253327 1● 229E_related_bat_CoV_BtCov_KY229E_8_ Hi.vittatus _Kenya_2010_KY073748 1 ● 229E_related_bat_CoV_BtCoV_AT1A_F45_ Hi.abae _Ghana_2010_KT253259 Human_coronavirus_229E_ Ho.sapiens sapiens_NC002645 ● 229E_related_bat_CoV_BtCoV_KW1C_F161_ Hi.ruber _Ghana_2010_KT253264 1 ● Bat_coronavirus_4292_ De.rotundus _Brazil_2013_KU552072 0.7 1 ● BtCoV_KP816_ Ph.discolor _Panama_2011_JQ731782 1 ● Bat_coronavirus_strain_328_11_ Mo.molossus _Brazil_2011_KX094982 ● Bat_coronavirus_1CO7BA_ Gl.soricina _Trinidad_2007_EU769558 0.8 ● BtCoV_KP534_ Ar.jamaicensis _Panama_2010_JQ731787 1 ● BtCoV_KCR253_ Ca.perspicillata _Costa_Rica_2010_JQ731789 1 ● Bat_cov_309_ Ca.castanea _Costa_Rica_2014_KM215147 1 ● Bat_coronavirus_1599_ Ca.perspicillata _Brazil_2012_KT717385 ● BtCoV_BRA118_ Ca.perspicillata _Brazil_2009_JQ731796 0.8 Porcine_enteric_alphacoronavirus_strain_GDS04_ Su.domesticus _China_2017_MF167434 1● SADS_related_coronavirus_ Rh.sp _China_MF094687 1 ● Bat_coronavirus_HKU2−1_ Rh.sp _China_2004_DQ249235 1 ● Bat_coronavirus_HKU2_GD−430_ Rh.sinicus _China_2004_NC009988 1● BtCoV_4307−2_ Rh.affinis _China_2013_KP876528 1 ● Bat_coronavirus_MLHJC4_ Rh.sinicus _China_2012_KU182954 ● BtRf_AlphaCoV_YN2012_ Rh.ferrumequinum _China_2012_KJ473808 1● Bat_CoV_FMNH_228963_Mozambique_ Rh.rhodesiae _2015_MN183190 ● Bat_CoV_FMNH_228954_Mozambique_ Rh.lobatus _2015_MN183189 0.9 Porcine_respiratory_coronavirus_ Su.domesticus _USA_2014_KR270796 1 Transmissible_gastroenteritis_virus_ Su.scrofa _China_2015_KX083668 1 Canine_coronavirus_strain_171_ Ca.lupus familiaris_Germany_1971_KC175339 0.9 Feline_coronavirus_UU15_ Fe.catus _Netherlands_2007_FJ938057 0.9 Mink_coronavirus_ Mu.vison _USA_1998_HM245925 ● Bat_coronavirus_1573_ St.lilium _Brazil_2012_KT717392 MERS_COV_Korea_Seoul_SNU1_035_Human_ Ho.sapiens sapiens_South_Korea_2015_KU308549 MERS_COV_Hu_Riyadh_KSA_12160_Human_ Ho.sapiens sapiens_South_Korea_2016_KX154688 MERS_COV_KFU_HKU_19Dam_ Ca.dromedarius _Saudi_Arabia_2013_KJ650296 MERS_COV_2c_EMC_Human_ Ho.sapiens sapiens_2012_JX869059 1 MERS_COV_D2731.3_14_ Ca.dromedarius _United_Arab_Emirates_KT751244 0.9 MERS_COV_ Ca.dromedarius _Egypt_2013_KJ477102 1 ● MERS_COV_related_5038_ Ne.capensis _South_Africa_2015_MF593268 1 1● BtCoV_UKR_G17_ Pi.nathusii _Ukraine_2011_KC243392 ● BtCoV_8_724_ Pi.pygmaeus _Romania_2009_KC243390 1 ● Bat_coronavirus_16BF104_ Ep.serotinus _South_Korea_2016_KY432468 0.9 ● Bat_Coronavirus_HKU5_ Pi.abramus _China_2007_NC009020 beta C CoV_2012_174_ Er.europaeus _Germany_2012_NC039207 ● Bat_Coronavirus_HKU4_ Ty.pachypus _NC009019 1 1● Bat_CoV_FMNH_228900_Mozambique_ Ny.thebaica _2015_MN183193 0.7 1● Bat_CoV_FMNH_228903_Mozambique_ Ny.thebaica _2015_MN183195 ● Bat_CoV_FMNH_228899_Mozambique_ Ny.thebaica _2015_MN183194 1 ● Bat_CoV_FMNH_228901_Mozambique_ Ny.thebaica _2015_MN183196 ● BtCoV_KW2E_F82_ Ny.gambiensis _Ghana_2011_JX899382 1● BtCoV_KW2E_F93_ Ny.gambiensis _Ghana_2010_JX899383 ● BtCoV_KW2E_F53_ Ny.gambiensis _Ghana_2011_JX899384 0.7● BtCoV_KCR230_ Pt.parnellii _CostaRica_2010_JQ731779 1● BtCoV_KCR180_ Pt.parnellii _CostaRica_2012_KC633193 1 ● BtCoV_KCR15_ Pt.parnellii _Costa_Rica_2012_KC633195 ● BtCoV_KCR155_ Pt.parnellii _CostaRica_2012_KC633194 ● Bat_coronavirus_49_ Pt.davyi _Mexico_2012_KC886322 SARS_COV_HKU39849_Human_ Ho.sapiens sapiens_United_Kingdom_2003_JN854286 1 SARS_coronavirus_SZ3_Civet_ Pa.larvata HongKong_2003_AY304486 ● SARS_coronavirus_Tor2_Human_ Ho.sapiens sapiens_Canada_2003_NC004718 1 ● Bat_SARS_CoV_Rf1_ Rh.ferrumequium _China_2004_DQ412042 0.9 ● Bat_SARS_COV_HKU3_ Rh.sinicus _China_2006_DQ022305 ● SARS_like_CoV_SL_CoVZXC21_ Rh.sinicus _China_2015_MG772934 0.8 ● Bat_SARS_coronavirus_Rm1_ Rh.macrotis _China_2004_DQ412043 ● SARS_bat_coronavirus_Is39_ Rh.sp _Japan_2013_AB889995 beta B 1 1 ● SARS_related_BtCoV_ Rh.ferrumequinum _Luxembourg_2016_KY502395 ● Bat_coronavirus_BB98−16_ Rh.blasii _Bulgaria_2008_GU190217 ● Betacoronavirus_187632_2_ Rh.hipposideros _Italy_2012_KF500952 1 ● Bat_coronavirus_292_ Hi.caffer _Gabon_2009_JX174638 1 ● Zaria_bat_coronavirus_ Hi.commersoni _Nigeria_2008_HQ166910 ● Bat_Hp_betacoronavirus_Zhejiang2013_ Hi.pratti _2013_China_KF636752 11 Sable_Antelope_Cov_ Hi.niger _USA_2003_EF424621 1 Bovine_COV_ Bo.sp _NC_003045 0.8 Human_coronavirus_OC43_ Ho.sapiens sapiens_AY391777 Rabbit_COV_HKU14_ Or.cuniculus _China_2006_NC_017083 beta A 1 MHV_COV_StrainA59_ Mu.musculus _USA_2014_MF618253 Human_coronavirus_HKU1_N11_genotype_A_ Ho.sapiens _DQ415908 1 ● Bat_coronavirus_GLCC1_ Cy.sphinx _China_2005_KU182962 0.9 ● Bat_coronavirus_Diliman1525G2_ Cy.brachyotis _Philippines_2008_AB539082 0.9 ● Bat_coronavirus_2265_ Pt.jagori _Philippines_2010_AB683971 1 ● Bat_coronavirus_KY06_ Ro.aegyptiacus _Kenya_2006_HQ728484 0.9 1 ● Bat_coronavirus_HKU9_ Ro.leschenaultii _China_NC009022 ● Bat_coronavirus_HKU9_PREDICT_LAP11_K0006_ Eo.spelaea _Laos_2011_KX284911 ● Bat_coronavirus_NWDC188_ Me.kusnotoi _China_2013_KU182986 1● PREDICT_CoV_22_LAP11_D0063_ Eo.spelaea _Laos_2011_KX284906 1 ● Bat_coronavirus_RK074_ Eo.spelea _China_2010_KX520659 0.7● BtKY89_ Ei.helvum _Kenya_2007_GU065433 beta D 1● Bat_coronavirus_KY24_ Ei.helvum _Kenya_2006_HQ728482 1● Bat_CoV_UADBA_50636_Madagascar_ Ro.madagascariensis _2014_MN183192 1 ● Bat_CoV_UADBA_50625_Madagascar_ Ro.madagascariensis _2014_MN183191 ● PREDICT_CD116088_ Ep.franqueto _Congo_2014_KX285095 1 ● PREDICT_CD116004_ Mi.pusillus _Congo_2014_KX285087 ● PREDICT_GVF_CM_ECO70527_ Ep.gambianus _Cameroon_2013_KX285008 ● PREDICT_AATHA_ Ep.sp _Tanzania_2013_KX285299 White_eye_COV_HKU16_ Zo.sp _China_2007_NC016991 1 Coronavirus_HKU15_ Su.domesticus _China_2014_LC216914 0.8 Bulbul_coronavirus_HKU11_934_ Py.jocosus _China_2007_FJ376619 Munia_COV_HKU13_3514_ Lo.striata _China_2007_NC011550 delta 1 Wigeon_COV_HKU20_ Ma.penelope _China_2008_NC016995 Night_Heron_COV_HKU19_ Ny.nycticorax _China_2007_NC_016994 0.7 1 Infectious_bronchitis_virus_isolate_YN_ Ga.gallusdomesticus _China_2005_JF893452 1 Duck_coronavirus_isolate_DK_CH_HN_ZZ2004_ An.platyrhynchos _China_2004_JF705860 0.7 Turkey_coronavirus_strain_TCoV_VA_74_03_ Me.gallopavo _GQ427173 1 Beluga_Whale_coronavirus_SW1_ De.leucas _NC010646 gamma Bottlenose_dolphin_coronavirus_HKU22_isolate_CF090331_ Tu.sp _KF793826 0.1 bioRxiv preprint doi: https://doi.org/10.1101/742866; this version posted September 4, 2019. 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.

525 Figure 5. Detail of the alpha CoV clade. Molossidae CoVs generated in the study are indi- 526 cated in bold. This sub-tree is a zoom on Molossidae CoV clade from the tree depicted in Fig- 527 ure 4. Only bootstrap values >0.7 are indicated on the tree. Scale bar indicates mean number 528 of nucleotide substitutions per site.

529

530

531 Figure 6. Detail of the alpha CoV clade. NL63-like CoVs generated in the study are indicated 532 in bold. This sub-tree is a zoom on NL63 CoV clade from the tree depicted in Figure 4. Only 533 bootstrap values >0.7 are indicated on the tree. Scale bar indicates mean number of nucleotide 534 substitutions per site.

535

536 Figure 7. Detail of the alpha CoV clade. 229E-like CoVs generated in the study are indicated 537 in bold. This sub-tree is a zoom on NL63 CoV clade from the tree depicted in Figure 4. Only 538 bootstrap values >0.7 are indicated on the tree. Scale bar indicates mean number of nucleotide 539 substitutions per site.

540

541 Figure 8. Detail of the beta-C CoV clade. CoVs generated in the study are indicated in bold. 542 This sub-tree is a zoom on beta-C CoV clade from the tree depicted in Figure 4. Only boot- 543 strap values >0.7 are indicated on the tree. Scale bar indicates mean number of nucleotide 544 substitutions per site.

545

546 Figure 9. Detail of the beta-D CoV. CoVs generated in the study are indicated in bold. This 547 sub-tree is a zoom on beta-D CoV clade from the tree depicted in Figure 4. Only bootstrap 548 values >0.7 are indicated on the tree. Scale bar indicates mean number of nucleotide substitu- 549 tions per site.

550

551 552 Figure 10. Tanglegram representing host-virus co-evolution between bats of the western In- 553 dian Ocean and their associated CoVs. Phylogeny of bats (left) was constructed with an align- 554 ment of 11 Cyt b sequences of 1,030 bp by Neighbor-Joining with 1,000 bootstrap iterations. 555 Pruned phylogeny of SWIO bats CoVs (right) was constructed with an alignment of 27 556 unique sequences of 393 bp from SWIO bats CoVs, by Neighbor-Joining with 1,000 boot- 557 strap iterations.