Coronavirus surveillance in wildlife from two Congo basin countries detects RNA of multiple species circulating in bats and rodents

Charles Kumakamba; Fabien R Niama; Francisca Muyembe; Jean-Vivien Mombouli; Placide Mbala Kingebeni; Rock Aime Nina; Ipos Ngay Lukusa; Gerard Bounga; Frida N’Kawa; Cynthia Goma Nkoua; Joseph Atibu Losoma; Prime Mulembakani; Maria Makuwa; Ubald Tamufe; Amethyst Gillis; Matthew LeBreton; Sarah H Olson; Kenneth Cameron; Patricia Reed; Alain Ondzie; Alex Tremeau-Bravard; Brett R Smith; Jasmine Pante; Bradley S Schneider; David J McIver; James A Ayukekbong; Nicole A Hoff; Anne W Rimoin; Anne Laudisoit; Corina Monagin; Tracey Goldstein; Damien O Joly; Karen Saylors; Nathan D Wolfe; Edward M Rubin; Romain Bagamboula MPassi; Jean J Muyembe Tamfum; Christian E Lange

doi:10.1371/journal.pone.0236971

. 2021 Jun 9;16(6):e0236971. doi: 10.1371/journal.pone.0236971

Coronavirus surveillance in wildlife from two Congo basin countries detects RNA of multiple species circulating in bats and rodents

Charles Kumakamba ¹, Fabien R Niama ², Francisca Muyembe ¹, Jean-Vivien Mombouli ², Placide Mbala Kingebeni ¹, Rock Aime Nina ³, Ipos Ngay Lukusa ¹, Gerard Bounga ⁴, Frida N’Kawa ¹, Cynthia Goma Nkoua ², Joseph Atibu Losoma ¹, Prime Mulembakani ¹, Maria Makuwa ^1,⁵, Ubald Tamufe ⁶, Amethyst Gillis ^7,^¤a, Matthew LeBreton ⁸, Sarah H Olson ⁴, Kenneth Cameron ^4,^¤b, Patricia Reed ⁴, Alain Ondzie ⁴, Alex Tremeau-Bravard ⁹, Brett R Smith ⁹, Jasmine Pante ⁹, Bradley S Schneider ^7,^¤c,^¤d, David J McIver ^10,^¤e, James A Ayukekbong ^10,^¤f, Nicole A Hoff ¹¹, Anne W Rimoin ¹¹, Anne Laudisoit ¹², Corina Monagin ^7,⁹, Tracey Goldstein ⁹, Damien O Joly ^4,^10,^¤g, Karen Saylors ^5,⁷, Nathan D Wolfe ⁷, Edward M Rubin ⁷, Romain Bagamboula MPassi ¹³, Jean J Muyembe Tamfum ¹⁴, Christian E Lange ^5,^10,^*

Editor: Wanda Markotter¹⁵

¹Metabiota Inc, Kinshasa, Kinshasa, Democratic Republic of the Congo

²National Laboratory of Public Health, Brazzaville, Republic of the Congo

³Ministry of Agriculture and Livestock, Brazzaville, Republic of the Congo

⁴Wildlife Conversation Society, Bronx, New York, United States of America

⁵Labyringth Global Health St. Petersburg, Florida, United States of America

⁶Metabiota Cameroon Ltd, Yaoundé, Centre, Cameroon

⁷Metabiota Inc, San Francisco, California, United States of America

⁸Mosaic, Yaoundé, Centre, Cameroon

⁹One Health Institute, School of Veterinary Medicine, University of California, Davis, California, United States of America

¹⁰Metabiota Inc, Nanaimo, British Columbia, Canada

¹¹Fielding School of Public Health, University of California, Los Angeles, California, United States of America

¹²EcoHealth Alliance, New York, New York, United States of America

¹³Ministry of National Defense, Brazzaville, Republic of Congo

¹⁴Institut National de Recherche Biomédicale, Kinshasa, Kinshasa, Democratic Republic of the Congo

¹⁵University of Pretoria, SOUTH AFRICA

Competing Interests: Metabiota (https://metabiota.com), Labyrinth Global Health (https://www.labyrinthgh.com), and Mosaic (https://mosaic.cm) are private contractors who receive funds from international donor organizations to conduct technical assistance and research. Metabiota employs or employed CK, FM, PMK, INL, FNK, JAL, PM, MM, UT, AG, BSS, DJM, JAA, CM, DOJ, KS, NDW, EMR, and CEL, Mosaic employs ML, and Labyrinth Global Health employs KS, MM and CEL. There are no patents, products in development or marketed products associated with this research to declare. The association with commercial entities that some of the authors have does not alter our adherence to PLOS ONE policies on sharing data and materials.

^¤a

Current address: Development Alternatives, Inc., Washington DC, United States of America

^¤b

Current address: Unites States Fish and Wildlife Service, Bailey’s Crossroads, Virginia, United States of America

^¤c

Current address: Etiologic, Oakland, California, United States of America

^¤d

Current address: Pinpoint Science, San Francisco, California, United States of America

^¤e

Current address: Institute for Global Health Sciences, University of California, San Francisco, California, United States of America

^¤f

Current address: Southbridge Care, Cambridge, Ontario, Canada

^¤g

Current address: British Columbia Ministry of Environment and Climate Change Strategy, Victoria, British Columbia, Canada

^✉

* E-mail: clange_virology@gmx.de

Roles

Charles Kumakamba: Data curation, Formal analysis, Investigation, Project administration, Supervision, Writing – original draft, Writing – review & editing

Fabien R Niama: Investigation, Project administration, Supervision, Writing – review & editing

Francisca Muyembe: Investigation, Writing – review & editing

Jean-Vivien Mombouli: Investigation, Supervision, Writing – review & editing

Placide Mbala Kingebeni: Data curation, Investigation, Supervision, Writing – review & editing

Rock Aime Nina: Investigation, Writing – review & editing

Ipos Ngay Lukusa: Investigation, Writing – review & editing

Gerard Bounga: Investigation, Writing – review & editing

Frida N’Kawa: Investigation, Writing – review & editing

Cynthia Goma Nkoua: Project administration, Supervision, Writing – review & editing

Joseph Atibu Losoma: Investigation, Writing – review & editing

Prime Mulembakani: Project administration, Supervision, Writing – review & editing

Maria Makuwa: Data curation, Investigation, Project administration, Supervision, Writing – review & editing

Ubald Tamufe: Funding acquisition, Project administration, Writing – review & editing

Amethyst Gillis: Data curation, Investigation, Project administration, Supervision, Writing – original draft, Writing – review & editing

Matthew LeBreton: Data curation, Formal analysis, Methodology, Supervision, Writing – review & editing

Sarah H Olson: Data curation, Project administration, Supervision, Validation, Writing – review & editing

Kenneth Cameron: Data curation, Project administration, Supervision, Writing – review & editing

Patricia Reed: Project administration, Supervision, Writing – review & editing

Alain Ondzie: Project administration, Writing – review & editing

Alex Tremeau-Bravard: Investigation, Writing – review & editing

Brett R Smith: Investigation, Writing – review & editing

Jasmine Pante: Investigation, Writing – review & editing

Bradley S Schneider: Project administration, Supervision, Writing – review & editing

David J McIver: Data curation, Project administration, Visualization, Writing – review & editing

James A Ayukekbong: Data curation, Project administration, Supervision, Writing – review & editing

Nicole A Hoff: Project administration, Supervision, Writing – review & editing

Anne W Rimoin: Investigation, Methodology, Project administration, Supervision, Writing – review & editing

Anne Laudisoit: Project administration, Supervision, Writing – review & editing

Corina Monagin: Project administration, Resources, Supervision, Writing – review & editing

Tracey Goldstein: Data curation, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing – review & editing

Damien O Joly: Formal analysis, Funding acquisition, Investigation, Project administration, Software, Supervision, Validation, Writing – review & editing

Karen Saylors: Investigation, Project administration, Resources, Supervision, Writing – review & editing

Nathan D Wolfe: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – review & editing

Edward M Rubin: Project administration, Resources, Supervision, Writing – review & editing

Romain Bagamboula MPassi: Project administration, Supervision, Writing – review & editing

Jean J Muyembe Tamfum: Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing

Christian E Lange: Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Writing – original draft, Writing – review & editing

Wanda Markotter: Editor

PMCID: PMC8189465 PMID: 34106949

Abstract

Coronaviruses play an important role as pathogens of humans and animals, and the emergence of epidemics like SARS, MERS and COVID-19 is closely linked to zoonotic transmission events primarily from wild animals. Bats have been found to be an important source of coronaviruses with some of them having the potential to infect humans, with other animals serving as intermediate or alternate hosts or reservoirs. Host diversity may be an important contributor to viral diversity and thus the potential for zoonotic events. To date, limited research has been done in Africa on this topic, in particular in the Congo Basin despite frequent contact between humans and wildlife in this region. We sampled and, using consensus coronavirus PCR-primers, tested 3,561 wild animals for coronavirus RNA. The focus was on bats (38%), rodents (38%), and primates (23%) that posed an elevated risk for contact with people, and we found coronavirus RNA in 121 animals, of which all but two were bats. Depending on the taxonomic family, bats were significantly more likely to be coronavirus RNA-positive when sampled either in the wet (Pteropodidae and Rhinolophidae) or dry season (Hipposideridae, Miniopteridae, Molossidae, and Vespertilionidae). The detected RNA sequences correspond to 15 alpha- and 6 betacoronaviruses, with some of them being very similar (>95% nucleotide identities) to known coronaviruses and others being more unique and potentially representing novel viruses. In seven of the bats, we detected RNA most closely related to sequences of the human common cold coronaviruses 229E or NL63 (>80% nucleotide identities). The findings highlight the potential for coronavirus spillover, especially in regions with a high diversity of bats and close human contact, and reinforces the need for ongoing surveillance.

Introduction

Coronaviruses are relatively large enveloped viruses with a single-stranded positive-sense RNA genome of 26–32 kilobases that form their own taxonomic family within the Nidovirales order of viruses [1]. There are two Coronaviridae subfamilies, Letovirinae and Orthocoronavirinae, and the latter contains the genera Alpha- and Betacoronavirus, with viruses infecting mammalian species as well as the genera Gamma- and Deltacoronavirus that primarily contain viruses found in birds [2]. Although known for decades as important enteric and respiratory pathogens in domestic animals, and as causative agent of mild respiratory infections in humans, it was only the emergence of severe acute respiratory syndrome coronavirus (SARS-CoV) in humans in 2002 that brought coronaviruses broader attention [3]. The emergence and sporadic re-emergence of Middle East respiratory syndrome coronavirus (MERS-CoV) since 2012 and the global COVID-19 pandemic caused by SARS-CoV-2 have highlighted the enormous importance of this viral family in the context of global public health [4–6].

Coronaviruses identical or closely related to SARS-CoV-1, MERS-CoV and SARS-CoV-2 have been found in civets, camels, and bats, supporting zoonotic events as the most likely source of the respective outbreaks in humans [5, 7–11]. Studies to identify the origin of these zoonotic viruses also led to the discovery of many other, related or completely novel, animal coronaviruses in the process; in particular they have detected an astonishing diversity of alpha- and betacoronaviruses in bats. Bats (Chiroptera) are the second most diverse order of mammals second only to rodents and with its two suborders Yinpterochiroptera and Yangochiroptera represent approximately 20% of all known mammalian species [12].

Coronaviruses detected in bats include relatives of coronaviruses previously identified in other hosts, which led to the hypothesis that bats are a reservoir for coronaviruses and that these viruses are crossing into other non-bat species on a somewhat regular basis [13–16]. As a result, they may establish a novel permanent virus-host relationship, as in the case of MERS-CoV and camels, or a transient relationship as in the case of SARS-CoV-1 and civets. However most interspecies transmissions are likely dead ends for the virus and remain undetected [16–18]. The human common cold viruses HCoV-229E and HCoV-NL63 are most likely animal origin viruses that succeeded in establishing a permanent relationship with humans after crossing species barriers directly or indirectly from bats [13, 14, 19, 20]. Coronaviruses OC43 and HKU-1, which also cause common cold in humans, are likewise expected to have originated in animals, though likely in rodents rather than bats [6, 21].

In sum, there is thus mounting biological evidence that spillover has happened repeatedly in the past, continues to happen today, and will likely continue to occur in the future. Hence it is important to study animal coronaviruses to characterize the risks posed by these potentially emerging viruses, to understand the dynamics of the emergence of these pathogens, and to make informed decisions concerning prevention and risk mitigation [16, 22].

However, the virus’ biology is only one piece of the puzzle. We know that the emergence and epidemic spread of SARS-CoV-1 and likely SARS-CoV-2 are linked to human behavioral factors, such as close contact with wild animals, and with factors such as biodiversity and wildlife abundance, important prerequisites for virus diversity. Hotspots for zoonotic disease emergence generally exist where humans are actively encroaching on such animal habitats [23, 24], as is happening in Southeast Asia and Central Africa. While potential sources of zoonotic coronaviruses are increasingly being explored, a great deal remains to be documented in most parts of the biodiverse African continent, especially in Central Africa. Findings from countries such as Gabon, Kenya, Madagascar, Rwanda, South Africa and others suggest there are many coronaviruses circulating, primarily in bats, including species related to pathogens such as SARS-CoV-1, MERS-CoV, HCoV-229E and HCoV-NL63 [16, 25–32].

In the Democratic Republic of the Congo (DRC) and the Republic of Congo (ROC) contact with wildlife is common for large parts of the population via the value chain (food or otherwise), as pests in house and fields, at peri-domestic and co-feeding interfaces, or in the context of conservation and tourism [33, 34]. This close contact does not only involve risks for humans, but also potentially for endangered animal species such as great apes [35]. To explore the coronavirus presence in wildlife in this region representing one of the most biodiverse places on the African continent, we launched large scale sampling of primarily bats, rodents and non-human primates (NHPs). Our goal was to determine the diversity of coronaviruses circulating, using a consensus Polymerase Chain reaction (PCR) approach, coupled with the collection of, and coupling with, ecological data.

Materials and methods

Sample acquisition at multiple locations in DRC and ROC took place between 2006 and 2018 and differed depending on the species and interface (Fig 1, S1 Fig, S1 Table). Animals in peri-domestic settings were captured and released after sampling (bats, rodents and shrews only), while samples from the (bushmeat) value chain were collected from freshly killed animals voluntarily provided by local hunters upon their return to the village following hunting, or by vendors at markets. Fecal samples were collected from free-ranging NHPs [36]. Some NHP samples were also collected during routine veterinary exams in zoos and wildlife sanctuaries. Hunters and vendors were not compensated, to avoid incentivizing hunting. Sampling of animals was conducted based on specifically identified “sites”, where it was determined that there was considerable interaction between humans and wildlife, with this interaction ranging from hunting, to consumption, to interactions with animals as pests. Across both DRC and ROC, sites were visited at varying frequencies, based on ease of access to sites, return on effort (number of animals sampled for amount of effort expended in collection), and seasonal considerations. Efforts were made to visit each site at least twice each year, corresponding to a sampling event in both the wet and dry seasons. While repeat visits were made to most sites, no repeat sampling of individual animals occurred for bushmeat (as they are used or consumed quickly). It is possible that live animals which were captured and released were sampled more than once, as no tags or identifying methods were used, though repeat sampling is unlikely given the population of the collected species, individual life span, and the relatively small number of individuals captured. Identification was done in the field by trained field ecologists as well as retrospectively based on various field guides and other resources including those by including Kingdon and Monadjem [37–39]. Sample collection focused on oral/respiratory and intestinal/fecal organ systems and transmission routes, but other samples were collected when available and tested alongside, to detect possible infections of other organ systems.

Fig 1 — Geographical map indicating all sampling sites within the Republic of Congo and the Democratic Republic of the Congo. Locations where coronaviruses were detected are highlighted with blue triangles for bats and red circles for rodents. Sampling sites without viral RNA detection are marked by black dots (see also S1 Fig). Base map and data from OpenStreetMap and OpenStreetMap Foundation.

Animal capture and specimen collection was approved by the Institutional Animal Care and Use Committee (UCDavis IACUC, Protocol #s 16048, 16067, 17803, and 19300), the Institute Congolais pour la Conservation de la Nature (0374/ICCN/DG/ADG/ADG/KV/2011) in DRC, and the Ministry of Forest Economy and Sustainable Development (1102/MEFDD/DGEFDFAP-SPR) and the Ministry of Scientific Research and Technical Innovation (permit number 014/MRS/DGRST/DMAST, 018/MRSIT/DGRST/DMAST) in the RoC.

Oral and rectal swab samples were collected into individual 2.0 ml screw-top cryotubes containing 1.5 ml of either Universal Viral Transport Medium (BD), RNA later, lysis buffer, or Trizol® (Invitrogen), while pea-sized tissue samples were placed in 1.5ml screw-top cryotubes containing 500ul of either RNA later or lysis buffer (Qiagen), or without medium. All samples were stored in liquid nitrogen as soon as practical. Sample collection staff wore dedicated clothing: N95 masks, nitrile gloves, and protective eyewear during animal capture, handling and sampling.

RNA was extracted either manually using Trizol®, with an Qiagen AllPrep kit (tissue), Qiagen Viral RNA Mini Kit (swabs collected prior to 2014), or with a Zymo Direct-zol RNA kit (swabs collected after 2014) and stored at -80°C. Afterwards RNA was converted into cDNA using a Maxima H Minus First Strand cDNA Synthesis Kit (Thermo Scientific) or GoScript™ Reverse Transcription kit (Promega) and stored at -20°C until analysis. Two conventional nested broad range PCR assays, both targeting conserved regions within the RNA-Dependent RNA Polymerase gene (RdRp) were used to test the samples for coronavirus RNA. The first PCR as published by Quan et al. amplifies a product of approximately 286nt between the primer binding sites and was used as published. The first round (CoV-FWD1: CGT TGG IAC WAA YBT VCC WYT ICA RBT RGG and CoV-RVS1: GGT CAT KAT AGC RTC AVM ASW WGC NAC ATG) and second round (CoV-FWD2: GGC WCC WCC HGG NGA RCA ATT and CoV-RVS2: GGW AWC CCC AYT GYT GWA YRT C) primers of this PCR were specifically designed for the detection of a broad range of coronaviruses [40]. The second PCR as published by Watanabe et al. was used in two modified versions: one of them specifically targeting a broad range of coronaviruses in bats, the second one broadly targeting coronaviruses of other hosts [41]. In both cases, the first round of the semi nested PCR utilized the primers CoV-FWD3 (GGT TGG GAY TAY CCH AAR TGT GA) and CoV-RVS3 (CCA TCA TCA SWY RAA TCA TCA TA). In the second round, either CoV-FWD4/Bat (GAY TAY CCH AAR TGT GAY AGA GC) or CoV-FWD4/Other (GAY TAY CCH AAR TGT GAU MGW GC) were used as forward primers, while the reverse primer was again CoV-RVS3. Both versions amplify 387nt between the primer binding sites. A plasmid with binding sites for both the Quan and the Watanabe assays but otherwise lacking coronavirus sequence was used as positive control.

PCR products were subjected to gel electrophoresis on a 1.5% agarose gel and products of the expected amplicon sizes were excised. DNA was extracted using the Qiagen QIAquick Gel Extraction Kit and either sequenced by Sanger sequencing at the UC Davis DNA sequencing facility or was sent for commercial Sanger sequencing (GATC or Macrogen). Extracts with low DNA concentrations were cloned prior to sequencing. All results from sequencing were analyzed in the Geneious 7.1 software, and primer trimmed consensus sequences compared to the GenBank database (BLAST N, NCBI).

Viral sequences were deposited in the GenBank database under submission numbers KX284927-KX284930, KX285070-KX285095, KX285097-KX285105, KX285499-KX285513, KX286248-KX286258, KX286264-KX286286, KX286295-KX286296, KX286298-KX286322, MT064119-MT064126, MT064226, MT064272, MT081973, MT081997-MT082004, MT082032, MT082059-MT082060, MT082072, MT082123-MT082136, MT082145, MT082299, MT222036- MT222037.

Maximum likelihood phylogenetic trees were constructed including different genera (Alpha, Beta and Gamma) and species of known coronaviruses, as well as species/sub-species detected in DRC and ROC during the PREDICT project. Only a single sequence was included representing sequences with nucleotide identities of more than 95%. Multiple sequence alignments were made in Geneious (version 11.1.3, ClustalW Alignment). Bayesian phylogeny of the polymerase gene fragment was inferred using MrBayes (version 3.2) with the following parameters: Datatype = DNA, Nucmodel = 4by4, Nst = 1, Coavion = No, # States = 4, Rates = Equal, 2 runs, 4 chains of 5,000,000 generations. The sequence of an avian Gamma Coronavirus (NC_001451) served as outgroup to root the trees, and trees were sampled after every 1,000 steps during the process to monitor phylogenetic convergence [42]. The average standard deviation of split frequencies was below 0.006 for the Watanabe PCR amplicon based analysis and below 0.0029 for the Quan PCR amplicon based analysis (MrBayes recommended final average <0.01). The first 10% of the trees were discarded and the remaining ones combined using TreeAnnotator (version 2.5.1; http://beast.bio.ed.ac.uk) and displayed with FIGTREE (1.4.4; http://tree.bio.ed.ac.uk/) [43].

Ecological data related to the locality and the host animals was compiled and analyzed with respect to a correlation with the frequency of virus detection. The data included sex, human interface at which the animals were collected and sampled (value chain or other), and local calendric season (wet/dry), and were evaluated using two-tailed Chi-square tests with Yates correction (C²Y). Season for each sampling site was based on the next closest location represented in the climate-data.org data set, and defined as dry for months with average rainfall below 100mm/month and as wet for months above.

Results

A total of 3,554 animals (2,630 from DRC and 931 from RoC) were sampled and tested, of which 1,356 were bats (24 genera), 1,347 rodents (33 genera), 829 NHPs (14 genera), and 22 shrews, (Fig 1, S1 Fig, S1 Table). The majority of the 5,579 collected samples were oral (2,258) or rectal (2,238) swabs, with others being tissue samples, including liver and spleen (385), lung (184) or intestinal tract (175), as well as feces (160), blood, serum or plasma (140), kidney (24) and brain samples (4). Coronavirus RNA was detected in one or more samples from 121 animals, with 102 of the coronavirus RNA positive samples being rectal and 23 being oral swabs as well as one pooled liver and spleen sample. The CoV positive rate in intestinal/fecal samples (rectal swabs, intestinal tissue, feces) was with 3.96% significantly higher (N<0.0001 C2Y) than in oral/respiratory samples (oral swabs, lung tissue) with 0.94% or blood/immune system samples (liver, spleen, blood, serum or plasma) with 0.19%. The difference between the latter two was not significant. Only a single coronavirus (n = 1) was solely detected in an oral swab, in all other cases detection in rectal swabs exceeded detection in oral swabs by at least factor of two.

Viral RNA in afore mentioned 121 animals was amplified with either both PCRs (n = 33), the Watanabe PCR assay only (n = 48) or the Quan PCR assay only (n = 40) (S2 Table). The Watanabe assay amplified alphacoronavirus RNA in 13 and betacoronavirus RNA in 70 cases, of which 9 and 4 respectively differed by more than 5% from each other, while the Quan assay amplified alphacoronavirus RNA in 17 and betacoronavirus RNA in 52 cases, of which 8 and 5 respectively differed by more than 5% from each other. The difference between the tests with regards to alpha- and betacoronavirus RNA detection were not statistically significant.

Two of the animals with detected coronavirus RNA were rodents (<1% of sampled rodents), while 119 were bats (8.8% of sampled bats). Coronavirus RNA positive animals were found in 25% (27/106) of bat sampling events (same location and same day) and <1% (2/235) of rodent sampling events (S1 and S2 Tables, S2 Fig). In 10 of the bat sampling events, a single coronavirus RNA positive bat was among the tested animals, while in 17 events the number of bats positive for coronavirus ranged from 2 to 16 (S2 Table). RNA was detected in two species of rodents, one Deomys ferrugineus (1/1) and one Malacomys longipes (1/38), and in at least 14 different bat species, namely Chaerephon pumilus (4/62), Chaerephon sp. (2/6), Eidolon helvum (23/103), Epomops franqueti (22/146), Hipposideros caffer (1/5), Hipposideros gigas (1/2), Hipposideros ruber (3/21), Hipposideros sp. (1/4), Megaloglossus woermanni (11/118), Micropteropus pusillus (29/417), Miniopterus inflatus (3/6), Mops condylurus (8/105), Myonycteris sp. (4/4), Rhinolophus sp. (1/62), Scotophilus dinganii (1/29) and Triaenops persicus (5/26) (Table 1, S2 Table). Among the five bat species from which more than 100 individuals were sampled and tested, Eidolon helvum had the highest rate of coronavirus RNA positives (22.3%), followed by Epomops franqueti (15.8%), Megaloglossus woermanni (8.5%), Mops condylurus (7.6%), and Micropteropus pusillus (7%) (Table 1). With 10.2% Yinpterochiroptera bats had a significantly (N = 0.015 C²Y) higher rate of coronavirus RNA positive animals than Yangochiroptera bats with 5.0% (Table 1). No coronavirus RNA positive animals were detected among the sampled NHPs or shrews.

Table 1. PCR results by species and season (bats).

Suborder, family and species (>10 sampled individuals)	Wet Season	Dry Season	Total
Suborder, family and species (>10 sampled individuals)	PCR positives	PCR positives	PCR positives
Yinpterochiroptera total^**	13.3% (78/586)	5.6% (23/408)	10.2% (101/994)
Pteropodidae total^**	13.6% (77/567)	4.0% (12/303)	10.2% (89/870)
Micropteropus pusillus^**	10.3% (27/263)	1.3% (2/153)	7% (29/416)
Epomops franqueti	16.5% (18/109)	13.5% (5/37)	15.8% (23/146)
Megaloglossus woermanni	11.9% (5/42)	6.6% (5/76)	8.5% (10/118)
Eidolon helvum	22.3% (23/103)	- (0/0)	22.3% (23/103)
Myonycteris torquata	0% (0/11)	0% (0/11)	0% (0/22)
Rhinolophidae total^**	100% (1/1)	0% (0/61)	1.6% (1/62)
Hipposideridae total^*	0% (0/18)	25% (11/44)	17.7% (11/62)
Hipposideros ruber	0% (0/12)	33.3% (3/9)	14.3% (3/21)
Triaenops persicus	- (0/0)	13.8% (4/29)	13.8% (4/29)
Yangochiroptera total^**	0.6% (1/167)	8.7% (17/194)	5.0% (18/361)
Miniopteridae total	0% (0/1)	20% (3/15)	18.8% (3/16)
Pipistrellus nanus	0% (0/1)	0% (0/10)	0% (0/11)
Molossidae total^**	1.1% (1/92)	14.8% (13/88)	7.8% (14/180)
Chaerephon pumilus	0% (0/33)	12.5% (4/32)	6.2% (4/65)
Mops condylurus	1.9% (1/52)	13.2% (7/53)	7.6% (8/105)
Vespertilionidae total	0% (0/74)	1.1% (1/91)	0.6% (1/165)
Scotophilus dinganii	0% (0/18)	9% (1/11)	3.4% (1/29)
Total^**	10.5% (79/754)	6.6% (40/602)	8.8% (119/1356)

Open in a new tab

* Significant difference between calendric seasons P<0.05 (Chi-square with Yates correction)

** Highly significant difference between calendric seasons P<0.01 (Chi-square with Yates correction)

Significant seasonal differences for the rate of coronavirus RNA positive animals were detected across the bats with a 10.5% PCR positive rate in the wet season and a 6.6% rate in the dry season (p = 0.0176) (Table 1, S2 Fig). Bats that were associated with the (bushmeat) value chain were more frequently positive for coronavirus RNA (25.4%) than bats sampled at other human animal (peri-domestic) interfaces (5%), and this difference was highly significant (p < 0.0001). Male bats were significantly overrepresented among the Coronavirus RNA positives p = 0.0183), while there was insufficient data to analyze an influence of age (S1 Table).

Upon phylogenetic analysis, the sequences fall into 13 separate clusters based on the Quan PCR amplicon and 13 separate clusters based on the Watanabe PCR amplicon. Based on amplicons obtained with both PCRs from the same sample or animal, the respective Quan and Watanabe sequence clusters Alpha 5, 6, and 7 (Q7 = W2), as well as Beta 1, 2, and 3 correspond to each other. In one bat, RNA corresponding to two different alphacoronaviruses was detected in the oral and the rectal sample by the same PCR assay (ZB12030), while in another bat one PCR assay amplified RNA indicating an alpha- and the other assay an RNA indicating a betacoronavirus (GVF-RC-1006) (S2 Table). Given the overall results, RNA of 15 different alpha- and 6 betacoronaviruses was detected in the study population. In 22 of the sampling events, only a single type/strain of these coronaviruses was detected, two in two events, and three, five or eight in one event each. Identical or very similar coronavirus sequences were found with a spatial distance of up to 1975 km apart and a temporal distance of up to 1708 days (S3 Table).

Although the two coronavirus sequences we detected in rodents were clustering with known sequences from bat alphacoronaviruses, there were no sequences in GenBank that shared more than 80% identities with either of them (Fig 2, S2 Table). The detected bat coronavirus sequences on the contrary mostly clustered closely with known ones, that to a large part were detected in hosts from the same genus (Figs 2 and 3). The majority of the detected sequences were closely related to only two known viruses. Sequences with nucleotide identities of 97% or higher to Kenya bat coronavirus BtKY56 were found in 53 individual bats of 9 different species sampled on 14 occasions (Q-/W-Beta 2), while sequences with identities of 99% or higher to Eidolon bat coronavirus/Kenya/KY24 were detected in 30 individual bats of 3 different species sampled on 8 occasions (Q-/W-Beta 3) (S2 and S3 Tables). Bat coronavirus sequences in clusters Q-Alpha 1, 2, 7, and 8, W-Alpha 2 and 7, Q-/W-Beta 1, and Q-Beta 4 and 5 had identities of below 95% with known coronaviruses.

Fig 2 — Maximum likelihood phylogenetic tree of coronavirus sequences presented as a proportional cladogram, based on the RdRp region targeted by the PCR by Watanabe et. al. [41]. The tree includes the sequences detected during the project (red boxes) and indicates the number of sequences sharing more than 95% nucleotide identities in brackets. GenBank accession numbers are listed for previously published sequences, while sequences obtained during the project are identified by cluster names (compare S2 Table). Black font indicates coronavirus sequences obtained from bats, brown font indicates rodents, blue humans and gray other hosts. The host species and country of sequence origin are indicated for bats and rodents if applicable. In case of clusters W-Alpha-1 sequences were detected in *Mops condylurus* and *Chaerephon sp*., host species in cluster W-Beta-1 were *Megaloglossus woermanni* and *Epomops franqueti* and in case of cluster W-Beta-2 *Micropteropus pusillus*, *Epomops franqueti*, *Rhinolophus sp*., *Myonycteris sp*., *Mops condylurus*, *Megaloglossus woermanni*, and *Eidolon helvum* (compare S2 Table). Numbers at nodes indicate bootstrap support.

Fig 3 — Maximum likelihood phylogenetic tree of coronavirus sequences presented as a proportional cladogram, based on the RdRp region targeted by the PCR by Quan et. al. [40]. The tree includes the sequences detected during the project (red boxes) and indicates the number of sequences sharing more than 95% nucleotide identities in brackets. GenBank accession numbers are listed for previously published sequences, while sequences obtained during the project are identified by cluster names (compare S1 Table). Black font indicates coronavirus sequences obtained from bats, brown font indicates rodents, blue humans and gray other hosts. The host species and country of sequence origin are indicated for bats and rodents if applicable. In case of clusters Q-Alpha-4 sequences were detected in *Mops condylurus* and *Chaerephon sp*., host species in cluster Q-Alpha-7 were *Epomops franqueti* and *Chaerephon pumilus*, in case of cluster Q-Beta-2 *Micropteropus pusillus* and *Epomops franqueti*, and for cluster Q-Beta-3 *Megaloglossus woermanni*, *Eidolon helvum*, and *Epomops franqueti* (compare S2 Table). Numbers at nodes indicate bootstrap support.

In three cases (Q-Alpha 1, W-Alpha 7 and 8), sequences were closest to coronaviruses found in bats and camels with a high similarity (>90% nucleotide identities) to human coronavirus 229E (Figs 2 and 3). Similarly, the viral sequences in clusters Q-Alpha 2 and Q-/W-Alpha 6 were most closely related to bat coronaviruses with some similarity (>80% nucleotide identities) to human coronavirus NL63 (Figs 2 and 3).

Discussion

We detected coronavirus RNA in a significant proportion of the sampled bats (8.8%), but only in a small proportion of rodents (<1%) and none in NHPs or shrews. Finding relatively high numbers of coronavirus RNA positive bats is consistent with what has been previously reported; continuous high circulation of coronaviruses seems to be common especially in bats in tropical and subtropical climates [16, 32]. The specific PCR positive rates need to be approached with caution though, since factors such as species, season, location and others could play a role, as well as sample material and assays used for detection in comparison to other studies. Even within this study, as a result of its long duration and technological advances, some elements such as the collection medium or the brand of RNA extraction kits changed. Though minor in nature, we cannot completely rule out that they did have some kind of effect on the yield and thus detection rate. Our data suggest that intestinal/fecal samples might be best suited for screening, as all but one (20/21) single coronavirus in our data set were also and continuously more often detected in rectal swabs. This might be a result of prolonged shedding via feces compared to respiratory shedding. Only in one case did we detected coronavirus RNA in other tissue (spleen), which might indicate either viremia or an infection of the spleen itself, however a contamination with feces during necropsy cannot be ruled out with certainty.

Both assays used in the study were similarly effective in detecting both alpha- and betacoronavirus RNA in general, however, in most cases only one of the two was successful in amplifying viral RNA in a given sample. This indicates different sensitivities depending on the sequence (Subgenus/Species), and is to be expected given the nature of the degenerate primers. This confirms the benefits of using a combination of the two over using any of them alone for Coronavirus screening.

Our data suggest that coronavirus circulation in bats, at least in the Congo Basin, may indeed depend to some extent on species and seasonality (S2 Fig). We observed a significant difference in the number of bats testing positive depending on the local calendric season (p = 0.0176), with 10.5% of coronavirus RNA positive bats in the wet season but only 6.6% in the dry season at similar sample sizes for both seasons (Table 1). Interestingly, when looking at the family and species level, this holds true only for the Pteropodidae and Rhinolophidae species (p < 0.0001) while Hipposideridae, Miniopteridae, Molossidae, and Vespertilionidae species are more likely to be positive for coronavirus RNA in the dry season (p < 0.0001) (Table 1). The latter, though not for those specific families but for bats in general, has been proposed to be the correlation on a global scale [16]. We can only speculate as to the reasons of the apparent seasonality, but family and species seem to be important determinants. The birthing season of a species, which is often dependent on the local characteristics of the calendric season, has been suggested as a determinant for coronavirus spikes in bat populations, as juvenile bats become susceptible to infection once maternal antibody levels wane [32, 39]. Due to the diverse set of species in our sample set, individual sample numbers for most species are too small to draw definite conclusions, however the significant seasonal difference between Yinpteorchiroptera and Yangochiroptera are largely supported by respective trends in the individual species. We tested if the results from any particular species might be responsible for the observed correlation between season and the rate of positive coronavirus RNA animals. The only species that turned out to have a strong influence on the outcome was Eidolon helvum. However, the effect of dropping it from the analysis did only influence the outcome for bats in total, while it was not strong enough to negate the observed statistical significances for season within the Pteropodidae family or the Yinpterochiroptera suborder.

We did find Eidolon helvum, a bat usually roosting in large colonies, to be significantly overrepresented among the coronavirus positive bats (p = 0.0005), and higher detection rates in this species have been reported before [16, 44]. However, samples from Eidolon helvum in this study were collected from animals sold at two different markets on seven different days, and although we detected coronavirus RNA in some Eidolon helvum bats obtained at each of those occasions, it is possible that many of these bats came from the same roosts. The fact that all but one of the Eidolon helvum bats were found to be positive for the same coronavirus type (Q-/W-Beta-3) supports the assertion that there may be a connection between those bats. Our dataset does contain evidence that bat coronaviruses are readily shared within local bat populations. In fact, 109 out of 119 coronavirus positive bats were from sampling events with at least one other coronavirus RNA positive bat, and in all but six of these cases there was another bat with the same coronavirus type in the event-cohort (S2 and S3 Tables). Even though we cannot pinpoint the exact roosting relationship between all of these bats, this does confirm that coronaviruses are readily shared among the bats in an area, even across species boundaries. It also highlights that several different coronaviruses can circulate in parallel, including occasional double infections (S2 Table).

We found a much higher percentage of bats that were part of the bushmeat value chain to be positive for coronavirus RNA, which could have significant implications for the risk of coronavirus spillover from bats into humans. In our data set, 81% of the value chain samples were collected in the wet season, and this group also contained all of the Eidolon helvum samples. This suggests that seasonality and preferentially hunted species (80% Pteropodidae) are likely responsible for the higher rate of coronavirus RNA positive animals in the value chain. According to our data, it also seems that male animals are overrepresented among bats with coronavirus positive samples. It is possible that behavioral differences between males and females play a role, such as reduced activity of females during the time of birthing and breastfeeding or higher stress levels among males during the breeding season [45]. Further investigation is required to confirm and assess the reasons for this observation.

It appears clear from our findings, that bats rather than rodents or primates are sustaining a significant circulation of coronaviruses in the Congo Basin. Evidence for coronavirus circulation in wild animals other than bats is generally much scarcer, even though civets, raccoon, dogs, and camels have been shown to be involved in outbreaks of SARS and MERS [8, 11, 16].

We estimate that the 121 detected sequences correspond to 21 different coronaviruses based on the differences between the amplified sequences, considering the conserved nature of the amplified fragments within the RdRp open reading frame (ORF). These 21 coronaviruses include some that appear to only be distantly related to already described coronaviruses, and others that have already been found elsewhere, such as Kenya bat coronavirus BtKY56 and Eidolon bat coronavirus/Kenya/KY24 (Figs 2 and 3, S2 Table). In several instance the coronaviruses detected here are closely related to ones found in neighboring countries such as Gabon and the Central African Republic as well, which could be expected, as the political borders in Central Africa largely do not coincide with natural barriers that would prevent bats from crossing (Figs 2 and 3).

RNA of either Kenya bat coronavirus BtKY56 or Eidolon bat coronavirus/Kenya/KY24 was detected in ~70% (83) of the positive bats in this study and in several hundred bats reported previously (GenBank). Interestingly Kenya bat coronavirus BtKY56 appears to be a common virus species in the Congo Basin, while elsewhere it appears to be Eidolon bat coronavirus/Kenya/KY24 that is more common. These observations are undoubtedly susceptible to a sampling bias, for example due to the species composition of sample sets, particularly with Eidolon helvum, which can be sampled in large numbers when colonies are present or when they are present in markets [46]. A study conducted in neighboring Congo Basin country Gabon, for which Miniopterus, Rousettus, Hipposideros, Macronycteris, and Coleura bats were sampled found primarily RNA closely related to human coronaviruses 229E [32]. However, we do find evidence of Kenya bat coronavirus BtKY56 and Eidolon bat coronavirus/Kenya/KY24 in a relative wide array of bat hosts, indicating that species barriers may not be a limiting factor for sharing these specific betacoronaviruses (Figs 2 and 3, S2 Table). In contrast, most of the other sequences that we detected with related sequences in GenBank were detected in bats of the same genus by us and previously by others, supporting some degree of general species specificity and virus host co-evolution despite the latent ability of at least some coronaviruses to jump species barriers within and outside of the taxonomic order of hosts [15, 16, 18]. How often these events occur is not fully understood, but it is generally assumed that bats serve as a reservoir for coronaviruses [17]. With SARS-CoV-1 and SARS-CoV-2, the available evidence suggests that they were successfully transmitted from bats into humans, either directly or indirectly [21]. When we add to these two coronaviruses MERS that originated in bats and established a sustained reservoir in camels with occasional spillover into humans, we have witnessed three coronavirus spillover events with a bat origin in less than two decades. Considering our increased awareness and abilities to detect the emergence of novel viruses, it can be assumed that there may have been multiple coronavirus zoonotic events in the past that either led to some degree of either self-limiting outbreaks, or may have established a permanent virus host relationship with a new host [47]. MERS-CoV and SARS-CoV-1 may represent examples for the former, while the latter may be represented by human coronaviruses 229E and NL63; the ultimate outcome with regards to SARS-CoV-2 remains undetermined however. In our study we also detected viral RNA related to human coronaviruses 229E and NL63 in eight bats, and in a recent study from Gabon RNA closely related to human coronaviruses 229E was found in 12 out of 18 coronavirus RNA positive bat samples [32]. Whether or not these relatives of human pathogens or other strains of the coronaviruses currently circulating in bats can and will jump into humans in the future is difficult to predict at present. Progress in the understanding of molecular processes such as RNA polymerase proofreading capability, receptor usage, as well as in the field of human behavior are however certainly helping our understanding of risk [48]. The close contact of humans with wildlife including bats in the Congo Basin, especially in the context of hunting and wild animal trade, are certainly factors contributing to a higher risk for zoonotic events involving coronaviruses or other infectious agents.

The two sequences we detected in rodents (Deomys ferrugineus and Malacomys longipes) likely correspond to novel alphacoronaviruses. The lack of sequences closely related to the two indicates that rodent coronaviruses may be an understudied field, especially considering that rodents are the largest family of mammals.

We conclude overall, that bats and to a much smaller degree rodents in the Congo Basin harbor diverse coronaviruses, of which some might have the molecular potential for spillover into humans. Considering the close contact between wildlife and humans in the region, as part of the value chain or in peri-domestic settings, there is an elevated and potentially increasing risk for zoonotic events involving coronaviruses. Thus, continued work to understand the diversity, distribution, molecular mechanisms, host ecology, as well as consistent surveillance of coronaviruses at likely hotspots, are critical to help prevent future global pandemics.

Supporting information

S1 Fig. Sample sites around the cities of Brazzaville and Kinshasa.

Geographical map indicating all sampling sites in and around the urban centers of Brazzaville and Kinshasa on either side of the Congo river, the border between the Republic of Congo and the Democratic Republic of the Congo. Locations where coronavirus RNA was detected in bats are highlighted with blue triangles, sampling sites without viral RNA detection are marked by black dots. Base map and data from OpenStreetMap and OpenStreetMap Foundation.

(TIFF)

Click here for additional data file.^{(3.4MB, tiff)}

S2 Fig. Bat sampling effort and detection rates by calendar month.

Bat sampling effort and detection rates by calendar month (cumulative over all years). Panel A is depicting the percentage of the total samples collected in each month, relative to total bat samples collected. Panel B depicts the percentage of coronavirus RNA positive animals relative to each month’s total samples, while panel C shows the percentage of sampling events with at least one coronavirus RNA detection per month. Blue bars are indicating that all samples were collected during local rainy season, while beige indicates the same for the local dry season. Gradient colored bars indicate months in which dry and wet season samples were collected depending on the location.

(PDF)

Click here for additional data file.^{(53.7KB, pdf)}

S1 Table. Samples collected.

(XLS)

Click here for additional data file.^{(4.1MB, xls)}

S2 Table. Coronavirus RNA positive samples.

(XLSX)

Click here for additional data file.^{(61.5KB, xlsx)}

S3 Table. Geographical and temporal distance between sampling events.

Geographical and temporal distance between sampling events with detections of identical or closely related (>95% nucleotide identities) coronavirus RNA sequences (compare also S1 and S2 Tables).

(DOCX)

Click here for additional data file.^{(22KB, docx)}

Acknowledgments

The authors would like to thank: The governments of the Democratic Republic of the Congo and of the Republic of Congo for the permission to conduct this study; the staff of Metabiota, Wildlife Conservation Society, and EcoHealth Alliance who assisted in sample collection and testing and other members of the PREDICT-1 and PREDICT-2 consortium (https://ohi.vetmed.ucdavis.edu/programs-projects/predict-project/authorship).

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

The PREDICT Consortium (https://ohi.vetmed.ucdavis.edu/programs-projects/predictproject/authorship) received awards GHN-A-OO-09-00010-00 and AID-OAA-A-14-00102 from the United States Agency for International Development (https://www.usaid.gov). The contributions to/work on this manuscript of all listed authors were/was funded exclusively through these awards. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.Masters PS, Perlmann S. Coronaviridae. In: Knipe DM, Howley PM (eds) Fields Virology, 6th edn. Lippincott Williams & Wikins, Philadelphia: pp 825–858; 2013. [Google Scholar]
2.King AMQ, Adams MJ, Carstens EB, Lefkowitz EJ. Virus taxonomy. Classifcation and nomenclature of viruses, ninth report of the international committee on taxonomy of viruses. International Union of Microbiological Societies, Virology Division, Elsevier Academic Press; 2012. [Google Scholar]
3.Drosten C, Günther S, Preiser W, van der Werf S, Brodt HR, Becker S, et al. Identification of a novel coronavirus in patients with severe acute respiratory syndrome. N Engl J Med. 2003. May 15;348(20):1967–76. doi: 10.1056/NEJMoa030747 [DOI] [PubMed] [Google Scholar]
4.Zaki AM, van Boheemen S, Bestebroer TM, Osterhaus AD, Fouchier RA. Isolation of a novel coronavirus from a man with pneumonia in Saudi Arabia. N Engl J Med 2012;367:1814–20. doi: 10.1056/NEJMoa1211721 [DOI] [PubMed] [Google Scholar]
5.Zhou P, Yang XL, Wang XG, Hu B, Zhang L, Zhang W, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 2020;579:270–273. doi: 10.1038/s41586-020-2012-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Ayukekbong JA, Ntemgwa ML, Ayukekbong SA, Ashu EA, Agbor TA. COVID-19 compared to other epidemic coronavirus diseases and the flu. World J Clin Infect Dis 2020;10:1–13. [Google Scholar]
7.Li W, Shi Z, Yu M, Ren W, Smith C, Epstein JH, et al. Bats are natural reservoirs of SARS-like coronaviruses. Science 2005;310:676–9. doi: 10.1126/science.1118391 [DOI] [PubMed] [Google Scholar]
8.Kan B, Wang M, Jing H, Xu H, Jiang X, Yan M, et al. Molecular evolution analysis and geographic investigation of severe acute respiratory syndrome coronavirus-like virus in palm civets at an animal market and on farms. J Virol 2005;79:11892–11900. doi: 10.1128/JVI.79.18.11892-11900.2005 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Yip CW, Hon CC, Shi M, Lam TT, Chow KY, Zeng F, et al. Phylogenetic perspectives on the epidemiology and origins of SARS and SARS-like coronaviruses. Infect Genet Evol 2009;9:1185–96. doi: 10.1016/j.meegid.2009.09.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Corman VM, Ithete NL, Richards LR, Schoeman MC, Preiser W, Drosten C, et al. Rooting the phylogenetic tree of middle East respiratory syndrome coronavirus by characterization of a conspecific virus from an African bat. J Virol 2014;88:11297–303. doi: 10.1128/JVI.01498-14 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Sabir JS, Lam TT, Ahmed MM, Li L, Shen Y, Abo-Aba SE, et al. Co-circulation of three camel coronavirus species and recombination of MERS-CoVs in Saudi Arabia. Science 2016;351:81–4. doi: 10.1126/science.aac8608 [DOI] [PubMed] [Google Scholar]
12.Voigt CC, Kingston T. The roles of taxonomy and systematics in bat conservation. Springer Open, London: 2016 [Google Scholar]
13.Pfefferle S, Oppong S, Drexler JF, Gloza-Rausch F, Ipsen A, Seebens A, et al. Distant relatives of severe acute respiratory syndrome coronavirus and close relatives of human coronavirus 229E in bats, Ghana. Emerg Infect Dis 2009;15:1377–1384. doi: 10.3201/eid1509.090224 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Huynh J, Li S, Yount B, Smith A, Sturges L, Olsen JC, et al. Evidence supporting a zoonotic origin of human coronavirus strain NL63. J Virol 2012;86:12816–25. doi: 10.1128/JVI.00906-12 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Lau SK, Li KS, Tsang AK, Shek CT, Wang M, Choi GK, et al. Recent transmission of a novel alphacoronavirus, bat coronavirus HKU10, from Leschenault’s rousettes to pomona leaf-nosed bats: first evidence of interspecies transmission of coronavirus between bats of different suborders. J Virol 2012;86:11906–18. doi: 10.1128/JVI.01305-12 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Anthony SJ, Johnson CK, Greig DJ, Kramer S, Che X, Wells H, et al. Global patterns in coronavirus diversity. Virus Evol 2017;3:vex012. doi: 10.1093/ve/vex012 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Woo PC, Lau SK, Huang Y, Yuen KY. Coronavirus diversity, phylogeny and interspecies jumping. Exp Biol Med 2009;234:1117–1127. doi: 10.3181/0903-MR-94 [DOI] [PubMed] [Google Scholar]
18.Leopardi S, Holmes EC, Gastaldelli M, Tassoni L, Priori P, Scaravelli D, et al. Interplay between co-divergence and cross-species transmission in the evolutionary history of bat coronaviruses. Infect Genet Evol 2018;58:279–289. doi: 10.1016/j.meegid.2018.01.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Crossley BM, Mock RE, Callison SA, Hietala SK. Identification and characterization of a novel alpaca respiratory coronavirus most closely related to the human coronavirus 229E. Viruses 2012; 4:3689–3700. doi: 10.3390/v4123689 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Corman VM, Baldwin HJ, Tateno AF, Zerbinati RM, Annan A, Owusu M, et al. Evidence for an Ancestral Association of Human Coronavirus 229E with Bats. J Virol 2015;89:11858–70. doi: 10.1128/JVI.01755-15 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Cui J, Li F, Shi ZL. Origin and evolution of pathogenic coronaviruses. Nat Rev Microbiol 2019;17:181–192. doi: 10.1038/s41579-018-0118-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Jones KE, Patel NG, Levy MA, Storeygard A, Balk D, Gittleman JL, et al. Global trends in emerging infectious diseases. Nature 2008;451:990–993. doi: 10.1038/nature06536 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Wolfe ND, Daszak P, Kilpatrick AM, Burke DS. Bushmeat hunting, deforestation, and prediction of zoonotic disease. Emerg Infect Dis 2005;11:1822. doi: 10.3201/eid1112.040789 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Allen T, Murray KA, Zambrana-Torrelio C, Morse SS, Rondinini C, Di Marco M, et al. Global hotspots and correlates of emerging zoonotic diseases. Nat Commun 2017;8:1124. doi: 10.1038/s41467-017-00923-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Tong S, Conrardy C, Ruone S, Kuzmin IV, Guo X, Tao Y, et al. Detection of Novel SARS-like and Other Coronaviruses in Bats from Kenya. Emerg Infect Dis 2009;15:482–5. doi: 10.3201/eid1503.081013 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Tao Y, Tang K, Shi M, Conrardy C, Li KS, Lau SK, et al. Genomic characterization of seven distinct bat coronaviruses in Kenya. Virus Res 2012;167:67–73. doi: 10.1016/j.virusres.2012.04.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Geldenhuys M, Weyer J, Nel LH, Markotter W. Coronaviruses in South African Bats. Vector Borne Zoonotic Dis 2013;13:516–9. doi: 10.1089/vbz.2012.1101 [DOI] [PubMed] [Google Scholar]
28.Razanajatovo NH, Nomenjanahary LA, Wilkinson DA, Razafimanahaka JH, Goodman SM, Jenkins RK, et al. Detection of new genetic variants of Betacoronaviruses in Endemic Frugivorous Bats of Madagascar. Virol J 2015;12:42. doi: 10.1186/s12985-015-0271-y [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Geldenhuys M, Mortlock M, Weyer J, Bezuidt O, Seamark ECJ, Kearney T, et al. A metagenomic viral discovery approach identifies potential zoonotic and novel mammalian viruses in Neoromicia bats within South Africa. PLoS One 2018;13:e0194527. doi: 10.1371/journal.pone.0194527 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Markotter W, Geldenhuys M, Jansen van Vuren P, Kemp A, Mortlock M, Mudakikwa A, et al. Paramyxo- and Coronaviruses in Rwandan Bats. Trop Med Infect Dis 2019;4pii:E99. doi: 10.3390/tropicalmed4030099 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Nziza J, Goldstein T, Cranfield M, Webala P, Nsengimana O, Nyatanyi T, et al. Coronaviruses Detected in Bats in Close Contact with Humans in Rwanda. Ecohealth 2020;17:152–159. doi: 10.1007/s10393-019-01458-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Maganga GD, Pinto A, Mombo IM, Madjitobaye M, Mbeang Beyeme AM, Boundenga L, et al. Genetic diversity and ecology of coronaviruses hosted by cave-dwelling bats in Gabon. Sci Rep. 2020;10:7314. doi: 10.1038/s41598-020-64159-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Van Vliet N, Nebesse C, Gambalemoke S, Akaibe D, Nasi R. The bushmeat market in Kisangani, Democratic Republic of Congo: Implications for conservation and food security. Oryx 2012;46:196–203. [Google Scholar]
34.Van Vliet N, Schulte-Herbrüggen B, Muhindo J, Nebesse C, Gambalemoke S, Nasi R. Trends in bushmeat trade in a postconflict forest town: implications for food security. Ecology and Society 2017;22:35. [Google Scholar]
35.Patrono LV, Samuni L, Corman VM, Nourifar L, Röthemeier C, Wittig RM, et al. Human coronavirus OC43 outbreak in wild chimpanzees, Côte d´Ivoire, 2016. Emerg Microbes Infect 2018;7:118. doi: 10.1038/s41426-018-0121-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Seimon TA, Olson SH, Lee KJ, Rosen G, Ondzie A, Cameron K, et al. Adenovirus and herpesvirus diversity in free-ranging great apes in the Sangha region of the Republic Of Congo. PLoS One. 2015. 17;10:e0118543. [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Kingdon J. The Kingdon field guide to African mammals. Bloomsbury, London 2005.
38.Monadjem A, Taylor PJ, Cotterill FPD, Schoeman MC. Bats of Central and Southern Africa: a biogeographic and taxonomic synthesis. WITS University Press, Johannesburg: 2010. [Google Scholar]
39.Monadjem A, Taylor PJ, Denys C and Cotterill FPD. Rodents of Sub-Saharan Africa: A Biogeographic and Taxonomic Synthesis. Walter de Gruyter GmbH & Company KG; 2015. [Google Scholar]
40.Quan PL, Firth C, Street C, Henriquez JA, Petrosov A, Tashmukhamedova A, et al. Identification of a severe acute respiratory syndrome coronavirus-like virus in a leaf-nosed bat in Nigeria. mBio 2010;1 pii: e00208–10. doi: 10.1128/mBio.00208-10 [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Watanabe S, Masangkay JS, Nagata N, Morikawa S, Mizutani T, Fukushi S, et al. Bat coronaviruses and experimental infection of bats, the Philippines. Emerg Infect Dis 2010;16:1217–1223. doi: 10.3201/eid1608.100208 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Ronquist F, Teslenko M, van der Mark P, Ayres DL, Darling A, Höhna S, et al. MrBayes 3.2: Efficient Bayesian phylogenetic inference and model selection across a large model space. Syst Biol 2012;61:539–542 doi: 10.1093/sysbio/sys029 [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Bouckaert R, Vaughan TG, Barido-Sottani J, Duchêne S, Fourment M, Gavryushkina A, et al. BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLoS Comput Biol 2019;15:e1006650. doi: 10.1371/journal.pcbi.1006650 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Montecino-Latorre D, Goldstein T, Gilardi K, Wolking D, Van Wormer E, Kazwala, et al. Reproduction of East-African bats may guide risk mitigation for coronavirus spillover. One Health Outlook 2020;2:2. doi: 10.1186/s42522-019-0008-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Fayenuwo JO, Halstead LB. Breeding cycle of straw-colored fruit bat, Eidolon helvum, at Ile-Ife, Nigeria. J Mammal 1974;55:453‐454. [PubMed] [Google Scholar]
46.Kamins AO, Restif O, Ntiamoa-Baidu Y, Suu-Ire R, Hayman DT, Cunningham AA, et al. Uncovering the fruit bat bushmeat commodity chain and the true extent of fruit bat hunting in Ghana, West Africa. Biological Conservation 2011;144:3000–08. doi: 10.1016/j.biocon.2011.09.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Wang N, Li SY, Yang XL, Huang HM, Zhang YJ, Guo H, et al. Serological Evidence of Bat SARS-Related Coronavirus Infection in Humans, China. Virol Sin 2018;33:104–107. doi: 10.1007/s12250-018-0012-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Akem ES, Pemunta NV. The bat meat chain and perceptions of the risk of contracting Ebola in the Mount Cameroon region. BMC Public Health 2020;20,593. doi: 10.1186/s12889-020-08460-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0236971.r001

Decision Letter 0

Wanda Markotter

31 Dec 2020

PONE-D-20-20865

Coronavirus surveillance in Congo basin wildlife detects RNA of multiple species circulating in bats and rodents

PLOS ONE

Dear Dr. Lange,

Thank you for submitting your manuscript to PLOS ONE. Our apologies for the extensive review period. We had major difficulties in assigning reviewers and obtaining the reviews in time. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please address all comments indicated by reviewer 1 and 2 especially more detail about the methodology and including additional sequences from the geographical area in the analysis and discussions. I do appreciate that obtaining larger gene regions are complicated but if at all possible, please include this.

Please submit your revised manuscript by Feb 14 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Wanda Markotter

Academic Editor

PLOS ONE

Journal requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. In your Methods section, please provide additional location information of the collection sites, including geographic coordinates for the data set if available.

3. Your ethics statement should only appear in the Methods section of your manuscript. If your ethics statement is written in any section besides the Methods, please move it to the Methods section and delete it from any other section. Please ensure that your ethics statement is included in your manuscript, as the ethics statement entered into the online submission form will not be published alongside your manuscript.

4. We note that Figure 1 and Supplement_1 file in your submission contain map images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For these reasons, we cannot publish previously copyrighted maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth). For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright.

We require you to either (1) present written permission from the copyright holder to publish these figures specifically under the CC BY 4.0 license, or (2) remove the figures from your submission:

(1) You may seek permission from the original copyright holder of Figure 1 and Supplement_1 file to publish the content specifically under the CC BY 4.0 license.

We recommend that you contact the original copyright holder with the Content Permission Form (http://journals.plos.org/plosone/s/file?id=7c09/content-permission-form.pdf) and the following text:

“I request permission for the open-access journal PLOS ONE to publish XXX under the Creative Commons Attribution License (CCAL) CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). Please be aware that this license allows unrestricted use and distribution, even commercially, by third parties. Please reply and provide explicit written permission to publish XXX under a CC BY license and complete the attached form.”

Please upload the completed Content Permission Form or other proof of granted permissions as an "Other" file with your submission.

In the figure caption of the copyrighted figure, please include the following text: “Reprinted from [ref] under a CC BY license, with permission from [name of publisher], original copyright [original copyright year].”

(2) If you are unable to obtain permission from the original copyright holder to publish these figures under the CC BY 4.0 license or if the copyright holder’s requirements are incompatible with the CC BY 4.0 license, please either i) remove the figure or ii) supply a replacement figure that complies with the CC BY 4.0 license. Please check copyright information on all replacement figures and update the figure caption with source information. If applicable, please specify in the figure caption text when a figure is similar but not identical to the original image and is therefore for illustrative purposes only.

The following resources for replacing copyrighted map figures may be helpful:

USGS National Map Viewer (public domain): http://viewer.nationalmap.gov/viewer/

The Gateway to Astronaut Photography of Earth (public domain): http://eol.jsc.nasa.gov/sseop/clickmap/

Maps at the CIA (public domain): https://www.cia.gov/library/publications/the-world-factbook/index.html and https://www.cia.gov/library/publications/cia-maps-publications/index.html

NASA Earth Observatory (public domain): http://earthobservatory.nasa.gov/

Landsat: http://landsat.visibleearth.nasa.gov/

USGS EROS (Earth Resources Observatory and Science (EROS) Center) (public domain): http://eros.usgs.gov/#

Natural Earth (public domain): http://www.naturalearthdata.com/

5. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

6. Thank you for stating the following in the Financial Disclosure section:

"NW and TG received awards GHN-A-OO-09-00010-00 and AID-OAA-A-14-00102 from the United States Agency for International Development (https://www.usaid.gov). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."

We note that one or more of the authors are employed by a commercial company: 'Metabiota Inc', 'Labyringth Global Health', 'Development Alternatives, Inc.', 'Mosaic, Cameroon', 'Etiologic, Oakland' and 'Pinpoint Science, San Francisco'.

(1) Please provide an amended Funding Statement declaring this commercial affiliation, as well as a statement regarding the Role of Funders in your study. If the funding organization did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and only provided financial support in the form of authors' salaries and/or research materials, please review your statements relating to the author contributions, and ensure you have specifically and accurately indicated the role(s) that these authors had in your study. You can update author roles in the Author Contributions section of the online submission form.

Please also include the following statement within your amended Funding Statement.

“The funder provided support in the form of salaries for authors [insert relevant initials], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”

If your commercial affiliation did play a role in your study, please state and explain this role within your updated Funding Statement.

(2) Please also provide an updated Competing Interests Statement declaring this commercial affiliation along with any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products, etc.

Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests) . If this adherence statement is not accurate and there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.

Please include both an updated Funding Statement and Competing Interests Statement in your cover letter. We will change the online submission form on your behalf.

Please know it is PLOS ONE policy for corresponding authors to declare, on behalf of all authors, all potential competing interests for the purposes of transparency. PLOS defines a competing interest as anything that interferes with, or could reasonably be perceived as interfering with, the full and objective presentation, peer review, editorial decision-making, or publication of research or non-research articles submitted to one of the journals. Competing interests can be financial or non-financial, professional, or personal. Competing interests can arise in relationship to an organization or another person. Please follow this link to our website for more details on competing interests: http://journals.plos.org/plosone/s/competing-interests

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The paper reports on the surveillance of coronaviruses among bats, rodents, shrews and non-human primates in the congo basin. The study was performed across 12 years and opportunistically sampled 3561 animals for coronavirus RNA using two separate assays. Nearly all of the positives were from bats with the additional detection of two rodent positives. Based on the results the authors also performed some analyses to correlate detection of positives with observed ecological features such as seasonality. The paper is well written and the conclusions are thoughtful and well-reasoned.

My main request is the need to better details on the sampling methodology of the animals sampled- specifically in terms of time and frequency of sampling and repeat sampling at the same site if performed. This can be generally included in the text or better illustrated in one of the figures. The authors report that they identified no coronaviruses from non-human primates or shrews and only two from rodents. They comment that CoVs in wildlife are scarcer than in bats, and this may be true but consider the fragmented opportunistic sampling generally dedicated to sampling of “non-bats” for coronaviruses. It is difficult to discern from the way the data is reported in the manuscript or even from the supplementary table 2 how dedicated or opportunistic the sampling has been for the non-human primates across the 836 individuals from the total sampling timespan. Were some of the animals sampled more than once? If so, it would be important to highlight in text (that dedicated and repeat sampling was conducted).

My second concern is regarding the two assays used in manuscript. Supp 2 indicates a real time assay used for some samples – though this was not indicated in text- can the authors please add or clarify this? Also can the authors please clarify if the Quan assay was modified from the original? were the primers optimized? The Watanabe assay was modified as indicated. Can the authors comment on the sensitivity between the assays? Or what controls were used for the assays? The reason for my query is that these assays don’t amplify the same gene region of the genome (of course) and so the sequences cannot be directly compared so it might be a little difficult to determine if the two assays identified sequences from the same or different viruses. It seems that it was somewhat frequent that one assay was capable of detecting sequences missed by the other assay. What was the frequency of overlap that the assays were capable of detecting the same ‘virus’ vs. the frequency that the assays missed or detected very different viruses? Then, did the authors try combining the two assays on the positives to amplify the sequence region between primer regions in order to amplify both a longer gene region but also confirm identification of the same viral sequences with the different assays? Is one assay better at detecting a specific clade of coronavirus? As this was the first report of the rodent coronaviruses, full genomes would have been more informative.

I have no specific line issues except that the authors can consider using ‘alphacoronaviruses’ or ‘betacoronaviruses’ instead of Alpha coronaviruses or Beta coronaviruses (line 334 and 308).

Can the authors define the term wet season? Dry season? Exactly which months does this include? Similarly, in supplement 4 – which months are indicated by 1-12? Which years?

Its difficult to determine how the sequences were named in the study – please consider the naming convention suggested by the Coronavirus study group: The species Severe acute respiratory syndrome related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2 (page 4). Using a method that names sequences based on most related sequences as has previously been done creates significant confusion.

Please consider adding sequence identifiers to the sequences in the phylogenies? Like HKU8 to bat sequence Miniopterus from China etc. It also helps to orient the reader in the tree and relate it to previously reported coronaviruses (and species). Moreover, it is challenging to navigate between the trees so determine if similar coronaviruses were detected with the different assays, as the same reference sequences are not present in all trees- is this due to the regions availability to compare? (particularly between the 229E and NL63 clades).

Reviewer #2: Manuscript Number PONE-D-20-20865

Reviewer's Comments

General Comment :

The study provides new information on the diversity of coronavirus species circulating in the Democratic Republic of Congo and the Republic of Congo. However, at any time, this study does not refer to a recent published study on coronaviruses conducted in Gabon, a country in the Congo Basin.

In addition, full-length genomes with particularly spike gene at least will increase the importance of the work.

Specific comments :

Page 1, lines 1-2 : As the Congo Basin is not limited to these two countries, I propose that the title be more precise. For example « Coronavirus surveillance in two countries in Congo basin wildlife detects … ».

Page 4, lines 91-94 : The authors could also refer to the study by Maganga et al. (Maganga GD, Pinto A, Mombo IM, et al. 2020. Scientific Reports. 10:7314, 10.1038/s41598-020-64159-1) carried out in a Congo Basin country.

Page 5

Line 102 : To determine the degree of coronavirus circulation, a serological study would be more appropriate, therefore I suggest this sentence : « Our goal was to determine the diversity of circulating coronaviruses, using… ».

Line 111 : The authors state « Fecal samples were collected from free-ranging NHPs ». Can you explain how the collection was done?

Pages 5-6

Lines 120-121 : Why extraction with Trizol in addition to extraction using commercial kits? And why different extraction kits for swabs collected prior to and after 2014?

Lines 124-138 : The authors used two different PCR assays that amplify the same gene (RdRp), to broaden the detection spectrum of coronaviruses regardless of the animal host. However, the sizes of the amplified fragments are quite short for a highly conserved gene such as RdRp. It would be relevant to obtain sequences of a gene of interest such as the Spike gene if complete genomes cannot be obtained.

Page 7

Line 172 : The collection period should be indicated in the Materials and Methods section.

Line 177 : The authors state “others (36)“ as tissue sample. Can you specify the nature of these samples ?

Lines 178-179 : Authors should be careful not to create confusion between samples and animals. The authors indicate that 121 animals were detected positive, corresponding to 23 oral swabs, 102 rectal swabs and 1 pool of liver and spleen. However, for the 83 and 73 samples in which viral RNA was found, can we know how many individuals these numbers correspond to?

Were the 73 samples that tested positive using Quan PCR assay the same as the 83 samples that tested positive using Watanabe PCR assay ? If so, this should be specified.

Can the authors explain why the search for coronaviruses was done in the liver and spleen? These are not organs that are conventionally screened for these viruses.

Page 8

Lines 187-190 : As was done for rodents, indicate for bats, in brackets, the number of positive individuals out of the total number tested.

At any time in the materials and methods section there was no mention of how the identification of the species was done.

Lines 194-195 : In the introduction, I suggest that the classification of bats be briefly presented so that the terms Yinpterochiroptera and Yangochiroptera do not appear abruptly in the results section. Also, write throughout the document Yinpterochiroptera instead of Yinpterchiroptera. And write them in italics throughout the document.

Page 10

Lines 243-246 : The authors should discuss further the seasonal difference in positivity rates. For example, what factors related to bats might explain them? Maganga et al. 2020 had, for the species studied, identified the rainy season as the birthing season, which may justify a higher viral shedding rate.

Page 22

Table 1 : Correct Yinpterochiroptera

Figures

Figure 2 and 3 : I suggest the authors to add sequences of Alphacoronavirus of bats from Gabon and to include some sequences of Betacoronavirus of bats from this area. Especially since Figure 2 seems to show that the same viruses would circulate in Gabon and the Republic of Congo (See the first cluster of the phylogenetic tree). Moreover, the authors do not even discuss this observation.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2021 Jun 9;16(6):e0236971. doi: 10.1371/journal.pone.0236971.r002

Author response to Decision Letter 0

10 Apr 2021

Included as word document "Response_to_Reviewers".

Attachment

Submitted filename: Response_to_Reviewers_.docx

Click here for additional data file.^{(135KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0236971.r003

Decision Letter 1

Wanda Markotter

20 May 2021

Coronavirus surveillance in wildlife from two Congo basin countries detects RNA of multiple species circulating in bats and rodents

PONE-D-20-20865R1

Dear Dr. Lange,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Wanda Markotter

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: (No Response)

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: (No Response)

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: (No Response)

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: (No Response)

**********

6. Review Comments to the Author

Reviewer #1: The authors have effectively updated the manuscript to resolve issues raised by the first draft. Particularly the material and methods and results sections are much more informative and contribute interesting information.

I recommend approval of the manuscript but urge the authors to please review their host bat taxonomy as this is an expanding field (to avoid mistakes and conflicts with the manuscript in future). The species Trianeops persicus has been restricted to the middle East and according to several sources (Benda and Vallo 2009 and The African Chiropteran Report) the only species in Africa is T. afer. Similarly molossid bats have undergone taxonomic changes replacing Chaerephon with Mops, within the Pteropodidae (changes to Micropusillus), and many changes to the genera in the Vespertilionidae family (particularly to Neoromicia, Laephotis etc (Monadjem et al. 2020).

Reviewer #2: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

PLoS One. doi: 10.1371/journal.pone.0236971.r004

Acceptance letter

Wanda Markotter

3 Jun 2021

PONE-D-20-20865R1

Coronavirus surveillance in wildlife from two Congo basin countries detects RNA of multiple species circulating in bats and rodents

Dear Dr. Lange:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Prof Wanda Markotter

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Sample sites around the cities of Brazzaville and Kinshasa.

(TIFF)

Click here for additional data file.^{(3.4MB, tiff)}

S2 Fig. Bat sampling effort and detection rates by calendar month.

(PDF)

Click here for additional data file.^{(53.7KB, pdf)}

S1 Table. Samples collected.

(XLS)

Click here for additional data file.^{(4.1MB, xls)}

S2 Table. Coronavirus RNA positive samples.

(XLSX)

Click here for additional data file.^{(61.5KB, xlsx)}

S3 Table. Geographical and temporal distance between sampling events.

Geographical and temporal distance between sampling events with detections of identical or closely related (>95% nucleotide identities) coronavirus RNA sequences (compare also S1 and S2 Tables).

(DOCX)

Click here for additional data file.^{(22KB, docx)}

Attachment

Submitted filename: Response_to_Reviewers_.docx

Click here for additional data file.^{(135KB, docx)}

Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

[pone.0236971.ref001] 1.Masters PS, Perlmann S. Coronaviridae. In: Knipe DM, Howley PM (eds) Fields Virology, 6th edn. Lippincott Williams & Wikins, Philadelphia: pp 825–858; 2013. [Google Scholar]

[pone.0236971.ref002] 2.King AMQ, Adams MJ, Carstens EB, Lefkowitz EJ. Virus taxonomy. Classifcation and nomenclature of viruses, ninth report of the international committee on taxonomy of viruses. International Union of Microbiological Societies, Virology Division, Elsevier Academic Press; 2012. [Google Scholar]

[pone.0236971.ref003] 3.Drosten C, Günther S, Preiser W, van der Werf S, Brodt HR, Becker S, et al. Identification of a novel coronavirus in patients with severe acute respiratory syndrome. N Engl J Med. 2003. May 15;348(20):1967–76. doi: 10.1056/NEJMoa030747 [DOI] [PubMed] [Google Scholar]

[pone.0236971.ref004] 4.Zaki AM, van Boheemen S, Bestebroer TM, Osterhaus AD, Fouchier RA. Isolation of a novel coronavirus from a man with pneumonia in Saudi Arabia. N Engl J Med 2012;367:1814–20. doi: 10.1056/NEJMoa1211721 [DOI] [PubMed] [Google Scholar]

[pone.0236971.ref005] 5.Zhou P, Yang XL, Wang XG, Hu B, Zhang L, Zhang W, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 2020;579:270–273. doi: 10.1038/s41586-020-2012-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref006] 6.Ayukekbong JA, Ntemgwa ML, Ayukekbong SA, Ashu EA, Agbor TA. COVID-19 compared to other epidemic coronavirus diseases and the flu. World J Clin Infect Dis 2020;10:1–13. [Google Scholar]

[pone.0236971.ref007] 7.Li W, Shi Z, Yu M, Ren W, Smith C, Epstein JH, et al. Bats are natural reservoirs of SARS-like coronaviruses. Science 2005;310:676–9. doi: 10.1126/science.1118391 [DOI] [PubMed] [Google Scholar]

[pone.0236971.ref008] 8.Kan B, Wang M, Jing H, Xu H, Jiang X, Yan M, et al. Molecular evolution analysis and geographic investigation of severe acute respiratory syndrome coronavirus-like virus in palm civets at an animal market and on farms. J Virol 2005;79:11892–11900. doi: 10.1128/JVI.79.18.11892-11900.2005 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref009] 9.Yip CW, Hon CC, Shi M, Lam TT, Chow KY, Zeng F, et al. Phylogenetic perspectives on the epidemiology and origins of SARS and SARS-like coronaviruses. Infect Genet Evol 2009;9:1185–96. doi: 10.1016/j.meegid.2009.09.015 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref010] 10.Corman VM, Ithete NL, Richards LR, Schoeman MC, Preiser W, Drosten C, et al. Rooting the phylogenetic tree of middle East respiratory syndrome coronavirus by characterization of a conspecific virus from an African bat. J Virol 2014;88:11297–303. doi: 10.1128/JVI.01498-14 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref011] 11.Sabir JS, Lam TT, Ahmed MM, Li L, Shen Y, Abo-Aba SE, et al. Co-circulation of three camel coronavirus species and recombination of MERS-CoVs in Saudi Arabia. Science 2016;351:81–4. doi: 10.1126/science.aac8608 [DOI] [PubMed] [Google Scholar]

[pone.0236971.ref012] 12.Voigt CC, Kingston T. The roles of taxonomy and systematics in bat conservation. Springer Open, London: 2016 [Google Scholar]

[pone.0236971.ref013] 13.Pfefferle S, Oppong S, Drexler JF, Gloza-Rausch F, Ipsen A, Seebens A, et al. Distant relatives of severe acute respiratory syndrome coronavirus and close relatives of human coronavirus 229E in bats, Ghana. Emerg Infect Dis 2009;15:1377–1384. doi: 10.3201/eid1509.090224 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref014] 14.Huynh J, Li S, Yount B, Smith A, Sturges L, Olsen JC, et al. Evidence supporting a zoonotic origin of human coronavirus strain NL63. J Virol 2012;86:12816–25. doi: 10.1128/JVI.00906-12 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref015] 15.Lau SK, Li KS, Tsang AK, Shek CT, Wang M, Choi GK, et al. Recent transmission of a novel alphacoronavirus, bat coronavirus HKU10, from Leschenault’s rousettes to pomona leaf-nosed bats: first evidence of interspecies transmission of coronavirus between bats of different suborders. J Virol 2012;86:11906–18. doi: 10.1128/JVI.01305-12 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref016] 16.Anthony SJ, Johnson CK, Greig DJ, Kramer S, Che X, Wells H, et al. Global patterns in coronavirus diversity. Virus Evol 2017;3:vex012. doi: 10.1093/ve/vex012 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref017] 17.Woo PC, Lau SK, Huang Y, Yuen KY. Coronavirus diversity, phylogeny and interspecies jumping. Exp Biol Med 2009;234:1117–1127. doi: 10.3181/0903-MR-94 [DOI] [PubMed] [Google Scholar]

[pone.0236971.ref018] 18.Leopardi S, Holmes EC, Gastaldelli M, Tassoni L, Priori P, Scaravelli D, et al. Interplay between co-divergence and cross-species transmission in the evolutionary history of bat coronaviruses. Infect Genet Evol 2018;58:279–289. doi: 10.1016/j.meegid.2018.01.012 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref019] 19.Crossley BM, Mock RE, Callison SA, Hietala SK. Identification and characterization of a novel alpaca respiratory coronavirus most closely related to the human coronavirus 229E. Viruses 2012; 4:3689–3700. doi: 10.3390/v4123689 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref020] 20.Corman VM, Baldwin HJ, Tateno AF, Zerbinati RM, Annan A, Owusu M, et al. Evidence for an Ancestral Association of Human Coronavirus 229E with Bats. J Virol 2015;89:11858–70. doi: 10.1128/JVI.01755-15 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref021] 21.Cui J, Li F, Shi ZL. Origin and evolution of pathogenic coronaviruses. Nat Rev Microbiol 2019;17:181–192. doi: 10.1038/s41579-018-0118-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref022] 22.Jones KE, Patel NG, Levy MA, Storeygard A, Balk D, Gittleman JL, et al. Global trends in emerging infectious diseases. Nature 2008;451:990–993. doi: 10.1038/nature06536 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref023] 23.Wolfe ND, Daszak P, Kilpatrick AM, Burke DS. Bushmeat hunting, deforestation, and prediction of zoonotic disease. Emerg Infect Dis 2005;11:1822. doi: 10.3201/eid1112.040789 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref024] 24.Allen T, Murray KA, Zambrana-Torrelio C, Morse SS, Rondinini C, Di Marco M, et al. Global hotspots and correlates of emerging zoonotic diseases. Nat Commun 2017;8:1124. doi: 10.1038/s41467-017-00923-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref025] 25.Tong S, Conrardy C, Ruone S, Kuzmin IV, Guo X, Tao Y, et al. Detection of Novel SARS-like and Other Coronaviruses in Bats from Kenya. Emerg Infect Dis 2009;15:482–5. doi: 10.3201/eid1503.081013 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref026] 26.Tao Y, Tang K, Shi M, Conrardy C, Li KS, Lau SK, et al. Genomic characterization of seven distinct bat coronaviruses in Kenya. Virus Res 2012;167:67–73. doi: 10.1016/j.virusres.2012.04.007 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref027] 27.Geldenhuys M, Weyer J, Nel LH, Markotter W. Coronaviruses in South African Bats. Vector Borne Zoonotic Dis 2013;13:516–9. doi: 10.1089/vbz.2012.1101 [DOI] [PubMed] [Google Scholar]

[pone.0236971.ref028] 28.Razanajatovo NH, Nomenjanahary LA, Wilkinson DA, Razafimanahaka JH, Goodman SM, Jenkins RK, et al. Detection of new genetic variants of Betacoronaviruses in Endemic Frugivorous Bats of Madagascar. Virol J 2015;12:42. doi: 10.1186/s12985-015-0271-y [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref029] 29.Geldenhuys M, Mortlock M, Weyer J, Bezuidt O, Seamark ECJ, Kearney T, et al. A metagenomic viral discovery approach identifies potential zoonotic and novel mammalian viruses in Neoromicia bats within South Africa. PLoS One 2018;13:e0194527. doi: 10.1371/journal.pone.0194527 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref030] 30.Markotter W, Geldenhuys M, Jansen van Vuren P, Kemp A, Mortlock M, Mudakikwa A, et al. Paramyxo- and Coronaviruses in Rwandan Bats. Trop Med Infect Dis 2019;4pii:E99. doi: 10.3390/tropicalmed4030099 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref031] 31.Nziza J, Goldstein T, Cranfield M, Webala P, Nsengimana O, Nyatanyi T, et al. Coronaviruses Detected in Bats in Close Contact with Humans in Rwanda. Ecohealth 2020;17:152–159. doi: 10.1007/s10393-019-01458-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref032] 32.Maganga GD, Pinto A, Mombo IM, Madjitobaye M, Mbeang Beyeme AM, Boundenga L, et al. Genetic diversity and ecology of coronaviruses hosted by cave-dwelling bats in Gabon. Sci Rep. 2020;10:7314. doi: 10.1038/s41598-020-64159-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref033] 33.Van Vliet N, Nebesse C, Gambalemoke S, Akaibe D, Nasi R. The bushmeat market in Kisangani, Democratic Republic of Congo: Implications for conservation and food security. Oryx 2012;46:196–203. [Google Scholar]

[pone.0236971.ref034] 34.Van Vliet N, Schulte-Herbrüggen B, Muhindo J, Nebesse C, Gambalemoke S, Nasi R. Trends in bushmeat trade in a postconflict forest town: implications for food security. Ecology and Society 2017;22:35. [Google Scholar]

[pone.0236971.ref035] 35.Patrono LV, Samuni L, Corman VM, Nourifar L, Röthemeier C, Wittig RM, et al. Human coronavirus OC43 outbreak in wild chimpanzees, Côte d´Ivoire, 2016. Emerg Microbes Infect 2018;7:118. doi: 10.1038/s41426-018-0121-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref036] 36.Seimon TA, Olson SH, Lee KJ, Rosen G, Ondzie A, Cameron K, et al. Adenovirus and herpesvirus diversity in free-ranging great apes in the Sangha region of the Republic Of Congo. PLoS One. 2015. 17;10:e0118543. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref037] 37.Kingdon J. The Kingdon field guide to African mammals. Bloomsbury, London 2005.

[pone.0236971.ref038] 38.Monadjem A, Taylor PJ, Cotterill FPD, Schoeman MC. Bats of Central and Southern Africa: a biogeographic and taxonomic synthesis. WITS University Press, Johannesburg: 2010. [Google Scholar]

[pone.0236971.ref039] 39.Monadjem A, Taylor PJ, Denys C and Cotterill FPD. Rodents of Sub-Saharan Africa: A Biogeographic and Taxonomic Synthesis. Walter de Gruyter GmbH & Company KG; 2015. [Google Scholar]

[pone.0236971.ref040] 40.Quan PL, Firth C, Street C, Henriquez JA, Petrosov A, Tashmukhamedova A, et al. Identification of a severe acute respiratory syndrome coronavirus-like virus in a leaf-nosed bat in Nigeria. mBio 2010;1 pii: e00208–10. doi: 10.1128/mBio.00208-10 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref041] 41.Watanabe S, Masangkay JS, Nagata N, Morikawa S, Mizutani T, Fukushi S, et al. Bat coronaviruses and experimental infection of bats, the Philippines. Emerg Infect Dis 2010;16:1217–1223. doi: 10.3201/eid1608.100208 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref042] 42.Ronquist F, Teslenko M, van der Mark P, Ayres DL, Darling A, Höhna S, et al. MrBayes 3.2: Efficient Bayesian phylogenetic inference and model selection across a large model space. Syst Biol 2012;61:539–542 doi: 10.1093/sysbio/sys029 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref043] 43.Bouckaert R, Vaughan TG, Barido-Sottani J, Duchêne S, Fourment M, Gavryushkina A, et al. BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLoS Comput Biol 2019;15:e1006650. doi: 10.1371/journal.pcbi.1006650 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref044] 44.Montecino-Latorre D, Goldstein T, Gilardi K, Wolking D, Van Wormer E, Kazwala, et al. Reproduction of East-African bats may guide risk mitigation for coronavirus spillover. One Health Outlook 2020;2:2. doi: 10.1186/s42522-019-0008-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref045] 45.Fayenuwo JO, Halstead LB. Breeding cycle of straw-colored fruit bat, Eidolon helvum, at Ile-Ife, Nigeria. J Mammal 1974;55:453‐454. [PubMed] [Google Scholar]

[pone.0236971.ref046] 46.Kamins AO, Restif O, Ntiamoa-Baidu Y, Suu-Ire R, Hayman DT, Cunningham AA, et al. Uncovering the fruit bat bushmeat commodity chain and the true extent of fruit bat hunting in Ghana, West Africa. Biological Conservation 2011;144:3000–08. doi: 10.1016/j.biocon.2011.09.003 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref047] 47.Wang N, Li SY, Yang XL, Huang HM, Zhang YJ, Guo H, et al. Serological Evidence of Bat SARS-Related Coronavirus Infection in Humans, China. Virol Sin 2018;33:104–107. doi: 10.1007/s12250-018-0012-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0236971.ref048] 48.Akem ES, Pemunta NV. The bat meat chain and perceptions of the risk of contracting Ebola in the Mount Cameroon region. BMC Public Health 2020;20,593. doi: 10.1186/s12889-020-08460-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Coronavirus surveillance in wildlife from two Congo basin countries detects RNA of multiple species circulating in bats and rodents

Charles Kumakamba

Fabien R Niama

Francisca Muyembe

Jean-Vivien Mombouli

Placide Mbala Kingebeni

Rock Aime Nina

Ipos Ngay Lukusa

Gerard Bounga

Frida N’Kawa

Cynthia Goma Nkoua

Joseph Atibu Losoma

Prime Mulembakani

Maria Makuwa

Ubald Tamufe

Amethyst Gillis

Matthew LeBreton

Sarah H Olson

Kenneth Cameron

Patricia Reed

Alain Ondzie

Alex Tremeau-Bravard

Brett R Smith

Jasmine Pante

Bradley S Schneider

David J McIver

James A Ayukekbong

Nicole A Hoff

Anne W Rimoin

Anne Laudisoit

Corina Monagin

Tracey Goldstein

Damien O Joly

Karen Saylors

Nathan D Wolfe

Edward M Rubin

Romain Bagamboula MPassi

Jean J Muyembe Tamfum

Christian E Lange

Roles

Abstract

Introduction

Materials and methods

Fig 1. Sampling sites map.

Results

Table 1. PCR results by species and season (bats).

Fig 2. Phylogenetic tree based on the RdRp region targeted by the PCR by Watanabe.

Fig 3. Phylogenetic tree based on the RdRp region targeted by the PCR by Quan.

Discussion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Wanda Markotter

Roles

Author response to Decision Letter 0

Decision Letter 1

Wanda Markotter

Roles

Acceptance letter

Wanda Markotter

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases