{"_asset":{"@search.score":5.091675,"Name":"The genetic diversity of Nipah virus across spatial scales","AlternateTitleCommaDelimited":"","Id":"9050","AssetLocationUri":null,"Abstract":"

BACKGROUND

Nipah virus (NiV), a highly lethal virus in humans, circulates in Pteropus bats throughout South and Southeast Asia. Difficulty in obtaining viral genomes from bats means we have a poor understanding of NiV diversity.

METHODS

We develop phylogenetic approaches applied to the most comprehensive collection of genomes to date (N = 257, 175 from bats, 73 from humans) from 6 countries over 22 years (1999–2020). We divide the 4 major NiV sublineages into 15 genetic clusters. Using Approximate Bayesian Computation fit to a spatial signature of viral diversity, we estimate the presence and the average size of genetic clusters per area.

RESULTS

We find that, within any bat roost, there are an average of 2.4 co-circulating genetic clusters, rising to 5.5 clusters at areas of 1500–2000 km2. We estimate that each genetic cluster occupies an average area of 1.3 million km2 (95% confidence interval [CI], .6–2.3 million km2), with 14 clusters in an area of 100 000 km2 (95% CI, 6–24 km2). In the few sites in Bangladesh and Cambodia where genomic surveillance has been concentrated, we estimate that most clusters have been identified, but only approximately 15% of overall NiV diversity has been uncovered.

CONCLUSIONS

Our findings are consistent with entrenched co-circulation of distinct lineages, even within roosts, coupled with slow migration over larger spatial scales.

","PublicationOrPresentationDate":"2024-12-16T00:00:00Z","LastModifiedDate":"2025-01-13T22:10:22.299Z","IsDeleted":false,"DigitalObjectIdentifier":"10.1093/infdis/jiae221","ContentTypeName":"Research","ParentContentTypeName":"Journal Article","JournalName":"Journal of Infectious Diseases","ContentType":"Journal Article > Research","AvailabilityTypeId":1,"AvailabilityType":"Full Text","JournalIssue":"6","JournalVolume":"230","JournalPages":"e1235-e1244","JournalAbbreviation":"J Infect Dis","Conference":null,"ConferenceStartDate":null,"ConferenceEndDate":null,"content":"\nThe Genetic Diversity of Nipah Virus Across Spatial Scales\nOscar Cortes-Azuero,1, Noémie Lefrancq,1, Birgit Nikolay,2, Clifton McKee,3, Julien Cappelle,4, Vibol Hul,5, Tey Putita Ou,5, Thavry Hoem,5,\n\nPhilippe Lemey,6, Mohammed Ziaur Rahman,7, Ausraful Islam,7, Emily S. Gurley,3, Veasna Duong,5,a, and Henrik Salje1a,\n\n1Department of Genetics, University of Cambridge, Cambridge, United Kingdom; 2Department of Epidemiology and Training, Epicentre, Paris, France; 3Department of Epidemiology, Bloomberg \nSchool of Public Health, Johns Hopkins University, Baltimore, Maryland; 4Joint Research Unit, Animal Santé Territoires Risques Ecosystèmes, Centre de coopération internationale en recherche \nagronomique pour le développement, Montpellier, France; 5Virology Unit, Institut Pasteur du Cambodge, Pasteur Network, Phnom Penh, Cambodia; 6Department of Microbiology, Immunology and \nTransplantation, KU Leuven, Leuven, Belgium; and 7Infectious Diseases Division, icddr,b, Dhaka, Bangladesh\n\nBackground. Nipah virus (NiV), a highly lethal virus in humans, circulates in Pteropus bats throughout South and Southeast \nAsia. Difficulty in obtaining viral genomes from bats means we have a poor understanding of NiV diversity.\n\nMethods. We develop phylogenetic approaches applied to the most comprehensive collection of genomes to date (N = 257, 175 \nfrom bats, 73 from humans) from 6 countries over 22 years (1999–2020). We divide the 4 major NiV sublineages into 15 genetic \nclusters. Using Approximate Bayesian Computation fit to a spatial signature of viral diversity, we estimate the presence and the \naverage size of genetic clusters per area.\n\nResults. We find that, within any bat roost, there are an average of 2.4 co-circulating genetic clusters, rising to 5.5 clusters at \nareas of 1500–2000 km2. We estimate that each genetic cluster occupies an average area of 1.3 million km2 (95% confidence interval \n[CI], .6–2.3 million km2), with 14 clusters in an area of 100 000 km2 (95% CI, 6–24 km2). In the few sites in Bangladesh and \nCambodia where genomic surveillance has been concentrated, we estimate that most clusters have been identified, but only \napproximately 15% of overall NiV diversity has been uncovered.\n\nConclusions. Our findings are consistent with entrenched co-circulation of distinct lineages, even within roosts, coupled with \nslow migration over larger spatial scales.\n\nKeywords. Nipah virus; phylogenetics; disease modeling; Pteropus; emerging pathogens.\n\nReceived 19 December 2023; accepted 25 April 2024; published online 29 April 2024\nPresented in part: Epidemics9 Conference, Abstract number EPID2023_0652, Bologna, Italy, \n\nDecember 2023.\naV. D. and H. S. contributed equally to this work as joint senior authors.\nCorrespondence: Henrik Salje, PhD, Department of Genetics, University of Cambridge, \n\nDowning Street, Cambridge CB2 3EH, UK (hs743@cam.ac.uk).\n\nThe Journal of Infectious Diseases® 2024;230:e1235–44 \n© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases \nSociety of America. \nThis is an Open Access article distributed under the terms of the Creative Commons Attribution- \nNonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which \npermits non-commercial reproduction and distribution of the work, in any medium, provided \nthe original work is not altered or transformed in any way, and that the work is properly cited. \nFor commercial re-use, please contact reprints@oup.com for reprints and translation rights for \nreprints. All other permissions can be obtained through our RightsLink service via the \nPermissions link on the article page on our site—for further information please contact \njournals.permissions@oup.com.\nhttps://doi.org/10.1093/infdis/jiae221\n\nNipah virus (NiV) is a bat-borne virus and a World Health \nOrganization priority pathogen [1]. Most infections in humans \nare fatal, and while most of them occur following zoonotic spill-\nover, human-to-human transmission is responsible for around \na third of known cases [2]. There are currently no approved \ntreatments or vaccines. NiV was first identified in Malaysia in \n1999 and has since recurred almost annually throughout \nSouth Asia [3–5]. Pteropus fruit bats are its reservoir hosts, \nand spillover pathways vary [6]. In the 1999 outbreak in \nMalaysia, human infection occurred through contact with \npigs who had been infected from eating contaminated fruit \n\n[7, 8]. In Bangladesh, the primary cause of human infection \nis consumption of raw date palm sap from trees upon which in-\nfected bats have fed [4]. Infected horses have also been impli-\ncated [9], while the source of the outbreaks in Kerala, India, \nremain unknown.\n\nDespite the substantial risk to human health, little is known \nabout NiV’s underlying genetic diversity. Pteropus bats are \nfound throughout South and Southeast Asia and are commonly \ninfected with NiV, with serostudies identifying NiV antibodies \nin 3%–83% of adult bats across the region [3, 10, 11]. However, \ninfection patterns within bat populations remain unclear, in-\ncluding the number of discrete lineages circulating in roosts, \nthe spatial spread of different lineages, or NiV’s ability to trans-\nmit between different Pteropus species. We also do not under-\nstand whether specific lineages are linked to increased spillover \nrisk, including specific routes of transmission. These critical \nknowledge gaps are problematic for assessments of spillover \nrisk and vaccine development.\n\nCharacterizing NiV’s genetic diversity is difficult as many \nisolates come from human cases, which remain rare. Even \nBangladesh, the country with most spillover events, reports \nonly an estimated approximately 15 spillovers annually [12]. \nThe representativeness of viruses obtained from human infec-\ntions is also unclear. This has motivated efforts to obtain NiV \nsequences from bat populations; however, this is difficult as \nbats are asymptomatic and viral shedding is infrequent [11]. \n\nNipah Virus Diversity Across Space • JID 2024:230 (15 December) • e1235\n\nThe Journal of Infectious Diseases \n\nM A J O R A R T I C L E\nDownloaded from https://academic.oup.com/jid/article/230/6/e1235/7659519 by guest on 13 January 2025\n\nhttps://orcid.org/0009-0004-7782-8129\nhttps://orcid.org/0000-0001-5991-6169\nhttps://orcid.org/0000-0003-2816-005X\nhttps://orcid.org/0000-0002-6149-0598\nhttps://orcid.org/0000-0001-7668-1971\nhttps://orcid.org/0000-0002-2095-7235\nhttps://orcid.org/0000-0001-8179-1382\nhttps://orcid.org/0000-0003-2456-3273\nhttps://orcid.org/0000-0003-2826-5353\nhttps://orcid.org/0000-0002-4103-4835\nhttps://orcid.org/0000-0001-9608-0823\nhttps://orcid.org/0000-0002-8648-9403\nhttps://orcid.org/0000-0003-0353-1678\nhttps://orcid.org/0000-0003-3626-4254\nmailto:hs743@cam.ac.uk\nhttps://creativecommons.org/licenses/by-nc-nd/4.0/\nhttps://doi.org/10.1093/infdis/jiae221\n\n\nNiV sequencing from bats has focused mainly on a few loca-\ntions within Thailand, Bangladesh, and Cambodia [13–15]. \nWhile it is difficult to make inferences about NiV diversity \nusing sequences from any 1 location, we can obtain a more \ncomplete picture by pooling information across different loca-\ntions combined with appropriate analytical methods. Here we \nprovide a comprehensive assessment of the diversity of NiV us-\ning all publicly available sequences coming from 6 countries, \nalong with several previously unpublished sequences. We de-\nvelop methods that are robust to strong biases in where and \nwhen sequences are obtained to track the diversity of NiV \nacross spatial scales (within roost, district, country, and inter-\nnationally) and quantify the extent to which diversity has \nbeen fully identified in locations that have implemented exten-\nsive surveillance efforts.\n\nMETHODS\n\nData Collection and Alignment\n\nWe collected all available NiV genomes in GenBank (N = 301, \nSupplementary Table 1) [16] and compiled their date, host \nspecies, and place of collection. We also included several pre-\nviously unpublished sequences (N = 26), collected between \n2013 and 2016 in 2 bat roosts in Cambodia. The sampling \nand screening approach for NiV for these sequences is ex-\nplained elsewhere [15].\n\nWe aligned the sequences using MUSCLE on MEGA-X [17]. \nAmong the sequences, 175 were sampled from 6 different bat \nhost species: Pteropus lylei (n = 120), Pteropus medius (formerly \nPteropus giganteus, n = 41), Pteropus vampyrus (n = 6), \nPteropus hypomelanus (n = 6), Hipposideros larvatus (n = 1), \nand a Taphozous bat of undetermined species (n = 1). Other \nsources of sequences were humans (n = 73), pigs (n = 7), a \ndog (n = 1), and an uncertain host (n = 1). Sequence length \nvaried from 153 bp to 18.2k bp (full genome). There were 64 \nfull-length genomes, 185 genomes from the nucleocapsid (N) \ngene, and the remainder covering different parts of the NiV \ngenome (Supplementary Table 2).\n\nPhylogenetic Analyses\n\nWe evaluated temporal signal and performed model selection \nusing IQ-Tree [18, 19] and Bayesian evaluation of temporal sig-\nnal (BETS) [20, 21]. We then reconstructed a time-resolved \nphylogeny using BEAST (version 1.10.4) [22].\n\nTo assess whether some sublineages were more likely to result \nin spillovers than others, we considered only sequences from \nBangladesh, as it is the only country with human cases regularly \ndocumented. We assessed whether human cases occurred with \nhigher frequency in any 1 of the sublineages as compared to the \ndistribution of sublineages in bats using a Fisher exact test.\n\nWe next analyzed the speed at which NiV has spread across \nSouth and Southeast Asia by computing the mean spatial \n\npairwise distance as a function of the pairwise evolutionary dis-\ntance for each pair of sequences.\n\nCharacterization of NiV Genetic Clusters\n\nWe used PhyCLIP, a phylogenetic clustering Python module, to \ncluster the sequences in the tree into different genetic clusters \n[23]. We analyzed sequence cluster distribution across countries \nand bat host species. We also analyzed the spatial distribution of \nbat species in our dataset. We implemented logistic regression \nto explore genetic clusters’ spatial signature, their relationship \nwith bat host species, and bat host species spatial signature. We \nconducted sensitivity analyses where we increased or reduced \nthe number of clusters (see Supplementary Material).\n\nRarefaction Analysis\n\nWe explored how the observed number of genetic clusters \nevolved with sampling, and how it could evolve if more se-\nquences were to be sampled. Focusing on locations with ≥10 \navailable sequences and ≥2 observed genetic clusters, we imple-\nmented a rarefaction analysis using the iNEXT package [24, \n25]. We then investigated the relationship between the estimat-\ned underlying number of discrete clusters in a location and dif-\nferent ecological variables (mean pairwise spatial distance \nbetween sequences, human population density, and percentage \nof tree coverage) from that location using Poisson regression.\n\nApproximate Bayesian Computation\n\nWe implemented Approximate Bayesian Computation to char-\nacterize NiV genetic clusters’ spatial footprint (Supplementary \nFigure 1). We estimated the number of genetic clusters and \ntheir average size in areas where Pteropus bats circulate. For \neach country with Pteropus circulation, we explored different \nestimates of the geographic range of Pteropus bats.\n\nAdditional details on the methods can be found in the \nSupplementary Material.\n\nEthics Statement\n\nThis project was conducted using publicly available sequence \ndata with no identifiable information and therefore did not re-\nquire ethical approval.\n\nRESULTS\n\nWe analyzed 257 sequences from 6 countries (Figure 1A) \ncovering a 22-year time period (1999–2020, Supplementary \nFigure 2). Seventy-three (28%) of the sequences came from hu-\nman infections, 175 (68%) from bats (Figure 1B), and 5 (4%) \nfrom other sources. BETS [26] analysis supported a model in-\ncluding the sampling dates of sequences over a model in which \nthey were considered contemporaneous (Bayes factor: 415). We \nbuilt a time-resolved phylogeny and found that sequences \ncould be broadly characterized into 2 major genotypes, I and \nII, previously reported [27], and 4 minor genotypes, IA, IB, \n\ne1236 • JID 2024:230 (15 December) • Cortes-Azuero et al\nDownloaded from https://academic.oup.com/jid/article/230/6/e1235/7659519 by guest on 13 January 2025\n\nhttp://academic.oup.com/jid/article-lookup/doi/10.1093/infdis/jiae221#supplementary-data\nhttp://academic.oup.com/jid/article-lookup/doi/10.1093/infdis/jiae221#supplementary-data\nhttp://academic.oup.com/jid/article-lookup/doi/10.1093/infdis/jiae221#supplementary-data\nhttp://academic.oup.com/jid/article-lookup/doi/10.1093/infdis/jiae221#supplementary-data\nhttp://academic.oup.com/jid/article-lookup/doi/10.1093/infdis/jiae221#supplementary-data\nhttp://academic.oup.com/jid/article-lookup/doi/10.1093/infdis/jiae221#supplementary-data\nhttp://academic.oup.com/jid/article-lookup/doi/10.1093/infdis/jiae221#supplementary-data\nhttp://academic.oup.com/jid/article-lookup/doi/10.1093/infdis/jiae221#supplementary-data\n\n\nIIA, and IIB (Figure 1C). We estimated a mean nucleotide \nsubstitution rate of 4.5 × 10−4 substitutions/site/year (95% con-\nfidence interval [CI], 2.9 × 10−4 to 6.0 × 10−4), consistent with \nprevious estimates [14]. Genotypes I and II diverged in 1937 \n(95% CI, 1838–1983), and the minor genotypes diverged in \nthe 1970s. There was broad spatial structure in these genotypes, \nwith countries on the eastern (Indonesia/Malaysia) and west-\nern edges (India) of the region having only 1 circulating subli-\nneage. Cambodia, with a central position, had sequences from \nall 4 sublineages (Figure 1D).\n\nMost sequences from human cases were from Bangladesh \n(n = 55/73), where there were 2 circulating sublineages (IA \nand IB). Lineage IA was found throughout 2004–2018, whereas \nIB was detected only through 2006–2009 (Supplementary \nFigure 3A). We found that all human sequences in Bangladesh \ncame from genotype IA. In contrast, 64.9% (n = 24/37) of bat se-\nquences came from IA and 35.1% (n = 13/37) from IB \n(Supplementary Figure 3B), suggesting that some genotypes \nmay have higher probability of spilling over into human popula-\ntions (P = .007 from Fisher exact test).\n\nFigure 1. A, Country of origin sequences with location of bat roosts. The size of the circles is proportional to the number of samples from each location. B, Number of \nsequences obtained for the different bat species (total N = 175). C, Reconstructed time-resolved maximum clade credibility phylogeny; tips are colored with country of origin \nand non-bat sequences are marked with an asterisk. D, Proportion of sequences that come from each sublineage distribution for each country; the countries are ordered from \nWest to East. Abbreviations: BGD, Bangladesh; IDN, Indonesia; IND, India; KHM, Cambodia; MYS, Malaysia; THA, Thailand.\n\nNipah Virus Diversity Across Space • JID 2024:230 (15 December) • e1237\nDownloaded from https://academic.oup.com/jid/article/230/6/e1235/7659519 by guest on 13 January 2025\n\nhttp://academic.oup.com/jid/article-lookup/doi/10.1093/infdis/jiae221#supplementary-data\nhttp://academic.oup.com/jid/article-lookup/doi/10.1093/infdis/jiae221#supplementary-data\nhttp://academic.oup.com/jid/article-lookup/doi/10.1093/infdis/jiae221#supplementary-data\n\n\nWe used the time-resolved phylogeny to investigate spatial \nstructure among sequences by comparing the evolutionary \ntime and spatial separation of each pair of viruses (Figure 2). \nTo mitigate sampling bias, we randomly sampled 1 sequence \nper bat roost per year, and we sampled 1 sequence per human \ncase cluster. On average, each 10-year increase in evolutionary \ntime was associated with a 186 km (95% CI, 161–212 km) in-\ncrease in spatial separation, equivalent to 19 km annually.\n\nTo provide a finer scale characterization of genetic diversity, we \nadapted a genetic clustering method, PhyCLIP, to categorize se-\nquences into different genetic clusters based on the distribution \nof pairwise evolutionary distances, resulting in 15 unique clusters \n(Figure 3B). We assessed our categorization’s robustness by reim-\nplementing the algorithm on 100 randomly selected posterior \ntrees. We found highly consistent sequence grouping (median \nAdjusted Rand Index of 0.94, where 0 would indicate random as-\nsignment and 1 perfect consistency) [28]. Genetic clusters di-\nverged between 1978 and 2002, with a mean evolutionary time \nfrom a cluster to the next-closest cluster of 15 years. Genetic clus-\nters tended to aggregate within the same country (Figure 3A, left) \nand were also separated by bat species, with 11 of the clusters \nexclusively found within single bat species (Figure 3A, center).\n\nTo investigate the spatial overlap of pteropid bat species in \nthe region, we analyzed inter-roost distances (Supplementary \nFigure 4) and compared species linked to each roost. This as-\nsumed all pteropid bats in any roost were of the same species. \nAs many countries in the region only have 1 pteropid species, \nthis is likely a reasonable assumption. We found that bat \nspecies were spatially structured at a country level, with 4 (67%) \n\ncountries having sequences from a single pteropid bat species \n(Figure 3A, right). We estimated that 96.8% (95% CI, 96.4%– \n97.2%) of bat roosts separated by <100 km were of the same \nspecies, dropping to 53.4% (95% CI, 47.9%–58.8%) for roosts \n500–1000 km apart (Supplementary Figure 5).\n\nWe found that there were an average of 2.41 (95% CI, 1.92– \n2.94) different genetic clusters per bat roost. The probability \nthat 2 sequences belonged to the same genetic cluster fell \nfrom 36.6% (95% CI, 30.6%–45.1%) when they were found \nwithin <100 km of each other to 5.7% (95% CI, 2.5%–9.7%) \nwhen they were 500–1000 km apart. Using logistic regression, \nwe found that each additional 100 km in spatial distance \nseparating roosts was associated with 0.75 (95% CI, .73–.77) \ntimes the odds of being part of the same genetic cluster \n(Figure 3C). As the probability of being from the same bat spe-\ncies is strongly linked to the distance between locations, we \ncould not disentangle the role of spatial segregation between \nlineages from being solely a spatial effect or due to the different \nPteropus species occupying different locations. However, on \naverage, we found that sequence pairs coming from the same \nbat species had 19.3 (95% CI, 15.9–23.6) times the odds of be-\nlonging to the same genetic cluster as pairs from different bat \nspecies (Figure 3D). Importantly, our characterization of the \nchanging probability of being from the same genetic cluster \nas a function of distance is robust to biased observation pro-\ncesses and can therefore be considered a spatial signature of \nNiV ecology. Our estimates of spatial dependence were robust \nto broad variations in the definition of a genetic cluster that re-\nsults in greater or fewer clusters (Supplementary Figure 6).\n\nWe estimated the average number of genetic clusters circulating \nwithin an area using Approximate Bayesian Computation to fit \nour observed spatial distribution of genetic clusters (Figure 4A). \nWe made a simplifying assumption that the spatial footprint \ncovered by a genetic cluster is equivalent throughout the re-\ngion, and that it can be captured by a multivariable normal dis-\ntribution. We estimate that, on average, 95% of infections from \na genetic cluster were found within an area of 1.3 million km2 \n\n(95% CI, .6–2.3 million km2) and that there were an average of \n14 discrete genetic clusters per land area of 100 000 km2 \n\n(95% CI, 6–24 km2) (Figure 4B).\nWe explored different approximations of the circulation area \n\nof Pteropus bats to estimate NiV diversity. For each country in \nthe region, we used estimates of Pteropus bats’ geographic range \nfrom the International Union for Conservation of Nature \n(IUCN) [29], and the Global Forest Watch’s area covered by \ntree cover as an alternative, indirect marker of geographic range. \nFor this second approach, we separately considered over 10%, \n30%, or 50% of tree cover as a proxy of the area covered by \nPteropus bats (Table 1, Supplementary Figures 7 and 8) [30]. \nFor most countries, we found little variation among estimates \nof diversity using these approaches. For example, in Thailand, \nwe estimate 15 unique genetic clusters (95% CI, 6–26) using \n\nFigure 2. Mean pairwise spatial distance (in km) in function of pairwise evolu-\ntionary distance (in years). The shaded area represents 95% confidence intervals \nobtained from nonparametric bootstrapping of the sequences.\n\ne1238 • JID 2024:230 (15 December) • Cortes-Azuero et al\nDownloaded from https://academic.oup.com/jid/article/230/6/e1235/7659519 by guest on 13 January 2025\n\nhttp://academic.oup.com/jid/article-lookup/doi/10.1093/infdis/jiae221#supplementary-data\nhttp://academic.oup.com/jid/article-lookup/doi/10.1093/infdis/jiae221#supplementary-data\nhttp://academic.oup.com/jid/article-lookup/doi/10.1093/infdis/jiae221#supplementary-data\nhttp://academic.oup.com/jid/article-lookup/doi/10.1093/infdis/jiae221#supplementary-data\nhttp://academic.oup.com/jid/article-lookup/doi/10.1093/infdis/jiae221#supplementary-data\n\n\nthe IUCN’s geographic range, and 17 unique genetic clusters \n(95% CI, 7–29) using the area with >10% of tree cover. In \nBangladesh, estimates ranged between 15 genetic clusters (95% \nCI, 7–29) and 11 genetic clusters (95% CI, 4–20), respectively. \n\nTaking South and Southeast Asia as a whole, we estimate \nbetween 66 and 118 separate NiV genetic clusters using the \ndifferent approaches (range of confidence intervals: 28– \n211), compared to 15 currently detected, suggesting that \n\nFigure 3. A, Proportion of all sequences of each genetic cluster by the country where it was detected (left) and by the bat species from which it was found (middle). The \nright plot shows the proportion of roosts that belong to each bat species for each country. B, Time-resolved phylogeny divided into 15 distinct clades using an adapted form of \nPhyCLIP. Each color represents a different genetic cluster. C, Proportion of sequence pairs belonging to the same cluster as a function of their spatial distance. Dots represent \nmedian values, and error bars represent 95% bootstrap confidence intervals (CIs). The line represents the fit of a logistic model. The green shaded region represents 95% CIs \nof the model fit. D, Odds ratio of belonging to the same genetic cluster if sequence pairs were sampled from the same bat host species or not. The error bar represents 95% \nbootstrap CIs. Abbreviations: BGD, Bangladesh; IDN, Indonesia; IND, India; KHM, Cambodia; MYS, Malaysia; OR, Odds Ratio; THA, Thailand.\n\nNipah Virus Diversity Across Space • JID 2024:230 (15 December) • e1239\nDownloaded from https://academic.oup.com/jid/article/230/6/e1235/7659519 by guest on 13 January 2025\n\n\n\napproximately 80%–90% of circulating genetic clusters re-\nmain undetected.\n\nFinally, we consider the extent to which genetic diversity has \nbeen fully uncovered in the 6 long-term established surveillance \nsites in the region (3 in Bangladesh, 2 in Thailand, and 1 in \nCambodia), and whether there exist predictors of the estimated \ntotal number of genetic clusters in any one place. There have \nbeen between 14 and 76 sequences obtained in these sites, re-\nsulting in the detection of 2–7 different genetic clusters to \ndate (Figure 4C). We estimated the number of new lineages \nthat would be detected with additional sampling. This ap-\nproach assumes equal probability of detection and similar levels \nof circulation of all clusters within an area. It also assumes \n\nstability in the clusters circulating in a location over time. We \nestimated that all of the circulating genetic clusters have been \nidentified in 5 of the 6 locations (Figure 4C, Supplementary \nFigure 4). The total number of clusters circulating within a sub-\nnational division was not significantly associated with the \nsize of the study area or with human population density \n(Supplementary Figure 9A and 9B). There was some evidence \nof an increase in genetic diversity with the percentage of forest \ncover (P = .015) (Supplementary Figure 9C).\n\nDISCUSSION\n\nWe analyzed NiV sequences, alongside host and location infor-\nmation from multiple countries, to characterize the underlying \n\nFigure 4. A, Model fit using approximate Bayesian computation on the proportion of sequence pairs that belong to the same genetic cluster in function of their spatial \ndistance. The orange dots represent the median and the lines represent the 95% confidence intervals (CIs). Median proportions calculated on Nipah virus (NiV) data and used \nto calibrate the model are represented in blue. B, Predicted number of genetic clusters in function of area (in square kilometers). The points and bars represent point estimates \nof the number of distinct genetic clusters as a function of area with 95% CIs. C, Estimated number of clusters in function of number of sampled NiV sequences for 6 regions in \nSouth and Southeast Asia. The dots represent the current number of observed samples and clusters in each region, the solid lines represent interpolated values based on \nobserved data, and the dashed lines represent predicted values if additional sampling was conducted. Abbreviations: BGD, Bangladesh; KHM, Cambodia; NiV, Nipah virus; \nTHA, Thailand.\n\nTable 1. Estimations of the Number of Genetic Clusters for the 6 Countries Represented in Our Dataset, According to Different Estimates of Pteropus \nRange\n\nCountry\n\nIUCN Pteropus Range Tree Cover (10% Threshold) Tree Cover (30% Threshold) Tree Cover (50% Threshold)\n\nArea, km2\nNo. of Clusters \n\n(95% CI) Area, km2\nNo. of Clusters \n\n(95% CI) Area, km2\nNo. of Clusters \n\n(95% CI) Area, km2\nNo. of Clusters \n\n(95% CI)\n\nIndia 2 987 747 55 (24–89) 490 910 23 (10–37) 388 304 21 (9–34) 304 653 19 (8–31)\n\nBangladesh 127 346 15 (6–25) 26 607 11 (4–20) 19 390 11 (4–20) 15 229 10 (4–19)\n\nThailand 138 538 15 (6–26) 219 195 17 (7–29) 199 624 17 (7–28) 179 669 16 (7–27)\n\nCambodia 62 957 13 (5–22) 101 985 14 (6–24) 88 099 14 (6–24) 74 823 13 (5–23)\n\nMalaysia 323 929 20 (8–32) 297 861 19 (8–31) 294 306 19 (8–31) 289 772 19 (8–31)\n\nIndonesia 1 656 164 40 (17–63) 1 650 987 39 (17–63) 1 606 412 39 (17–62) 1 550 634 38 (17–61)\n\nTable shows estimations of the number of circulating genetic clusters of NiV using the IUCN’s estimations of Pteropus geographic range and the Global Forest Watch’s estimations of tree \ncover per country above thresholds of 10%, 30%, and 50%, respectively. \n\nAbbreviations: CI, confidence interval; IUCN, International Union for Conservation of Nature; NiV, Nipah virus.\n\ne1240 • JID 2024:230 (15 December) • Cortes-Azuero et al\nDownloaded from https://academic.oup.com/jid/article/230/6/e1235/7659519 by guest on 13 January 2025\n\nhttp://academic.oup.com/jid/article-lookup/doi/10.1093/infdis/jiae221#supplementary-data\nhttp://academic.oup.com/jid/article-lookup/doi/10.1093/infdis/jiae221#supplementary-data\nhttp://academic.oup.com/jid/article-lookup/doi/10.1093/infdis/jiae221#supplementary-data\nhttp://academic.oup.com/jid/article-lookup/doi/10.1093/infdis/jiae221#supplementary-data\n\n\ngenetic diversity of a pathogen that poses a major risk to human \nhealth. We found that NiV is strongly spatially structured, with \nslow viral movement across the region and limited genetic sim-\nilarity in viruses that circulate in different countries. These \nfindings are consistent with a previous analysis using data \nfrom Bangladesh only [31]. The evolutionary time separating \nviruses sampled in the 2 extremes of the Nipah region (ie, \nIndia to Malaysia) was over 140 years, suggesting substantial \nentrenchment within communities, with greatest diversity ob-\nserved in the central region. The extent to which the spatially \nstructured nature of bat species, mixing patterns of bats across \nroosts, and preexisting immunity contribute to these observa-\ntions remains unclear.\n\nThe transmission dynamics of NiV within bat populations, \nincluding long-term immunity after infection, remains poorly \nunderstood. We found substantial overlap in the spatial foot-\nprint of genetic clusters, to the extent that even individual bat \nroosts host >1 distinct genetic cluster. Pteropus bat roost size \nhighly depends on species, typically hosting hundreds to thou-\nsands of bats at a time [15, 32–34]. Maintaining a sufficiently \nlarge susceptible population to sustain multiple independent \ntransmission chains in populations of this size likely requires \nlong durations of viral shedding, frequent reinfection, or coin-\nfection, as suggested through modeling of bat immune profiles \n[35]. As movement between bat roosts is common, the wider \npopulation across multiple roosts may also facilitate the main-\ntenance of multiple lineages. In support of a key role of roost \npopulation size in maintaining diversity, it is notable that the \nP lylei roosts in Cambodia, which had the greatest diversity \nwith the co-circulation of 3 NiV sublineages within each roost, \ntypically have thousands of bats per roost, many more than oth-\ner locations [15].\n\nThe evolutionary separation between NiV lineages has previ-\nously been suggested as 1 possible explanation for the differenc-\nes in the case fatality rate in Bangladesh (∼70%) and Malaysia \n(∼40%) [2, 36, 37]. However, it remains difficult to disentangle \ndifferences in the virus from human behavior or transmission \nroute differences. Spillover from bats into pigs through the con-\nsumption of infected fruit drove the outbreak in Malaysia, \nwhereas date palm sap consumption by humans appears key \nin Bangladesh [38]. Viral loads and inoculation routes and sites \nin these 2 transmission modes are likely to be very different, \nwhich could affect subsequent mortality. A primate model \nfound increased fatality risk in strains of Bangladeshi origin \nas compared to Malaysian origin. However, studies on other \nanimal models have provided less conclusive evidence of path-\nological or transmission differences between the 2 major clades, \nand the relevance of animal models to the human situation re-\nmains unclear [39–42]. Here we found evidence of differences \nin spillover or disease risk within a lineage. Genotype IB was \nfound in 3 different years in bat roosts in an area of \nBangladesh where human spillovers are frequently identified \n\nand where date palm sap consumption is common. However, \nno human cases were linked to this sublineage. Year-by-year \nvariability in spillover risk, linked to temperature, may explain \nthese findings, especially in the context when only a subset of \nhuman NiV cases are ever detected and have their viruses se-\nquenced [10, 12, 43].\n\nUsing genetic cluster correlation over distance as a spatial \nsignature of NiV, we estimated the spatial footprint of individ-\nual clusters and the number of circulating clusters. While \ncrude, these ballpark figures are a useful marker of what we \nmay be missing, especially in countries with little or no sam-\npling. We estimated the presence of approximately 100 genetic \nclusters across the region, suggesting substantial undetected vi-\nral diversity (∼80%–90%). Increased sampling across South \nand Southeast Asia will help uncover additional lineages, par-\nticularly in new locations. Long-term surveillance in locations \nwith established sampling remains critical to characterize the \nevolutionary dynamics of NiV within roosts. Whole-genome \nsequencing should be prioritized, where possible, to maximize \nthe signal that can be captured through genomic surveillance.\n\nOur observations of the spatial structure of NiV need to be \ntaken in the context of evolving bat ecology. Deforestation \nand land use changes across the region are leading to a more \nfragmented pattern of roosts and bringing bats closer to urban \nenvironments [7, 10, 44]. It has been shown for the related \nHendra virus that stress in bats is linked to spillover risk [45]. \nIt remains unclear how future climate changes and deforesta-\ntion could lead to further shifts in bat population distribution \nand behavior, potentially increasing stress and spillover risk.\n\nNiV remains a major threat to human health. Our findings \nhave implications for monitoring and understanding this ongo-\ning threat. We show that there is substantial undetected diver-\nsity in the region, with the potential for heterogeneity in risk of \nperson-to-person transmission and disease severity across \nstrains. Detection of changes in these characteristics will re-\nquire investment in focused and stable surveillance, particular-\nly outside Bangladesh. This is of particular importance since, \noutside Bangladesh, access to NiV testing, physician familiarity \nwith the disease, and NiV disease surveillance are very limited \n[46]. Moreover, future changes in land use may heighten hu-\nman exposure to NiV across the region.\n\nThis study is subject to some limitations. First, we rely on the \nfew places where sequencing is conducted, which represent a \nsmall number of locations in 6 countries. While we used meth-\nods that minimize the impact of sampling biases, potential dif-\nferences in NiV ecology in unsampled locations cannot be \ndiscounted. We also could not link sequences to specific bats. \nThe most common sampling method requires the collection \nof bat urine under roosts using plastic or tarpaulin sheets that \nare polymerase chain reaction (PCR) tested, making it impos-\nsible to link urine to an individual bat and to ensure that mul-\ntiple positive samples do not all come from the same bat. This \n\nNipah Virus Diversity Across Space • JID 2024:230 (15 December) • e1241\nDownloaded from https://academic.oup.com/jid/article/230/6/e1235/7659519 by guest on 13 January 2025\n\n\n\nalso means that most available NiV sequences from bats are \nshort sequences from the PCR process. Despite their frequent \nshort nature, we were still able to consistently place sequences \nin different clades. In particular, in Cambodia, where most se-\nquences were very short (<400 nucleotides in length), we were \nable to identify multiple clades circulating within the same \nroosts. The classification of tips into genetic clusters necessarily \nrelies on thresholds of evolutionary distance. However, in sen-\nsitivity analyses, changes in these thresholds resulted in mini-\nmal changes to the overall inferences on the genetic diversity \nof viruses circulating within any area. Finally, NiV sequences \nare ultimately reliant on the PCR primers used to detect the vi-\nrus in the original sample. If the PCR primers are overly specif-\nic, they may systematically miss some viruses [47]. Future \nefforts may want to consider using broader primer sets.\n\nThis project has demonstrated that even sparsely sampled \ngenetic data, including many short sequences, from large areas \ncan provide meaningful characterization of underlying diver-\nsity in populations when considered together. We have shown \nthat even individual roosts typically have multiple circulating \ntransmission chains but with each genetic lineage covering a \nlarge spatial footprint, probably driven by bat mobility patterns. \nWhile most NiV diversity remains undetected, the surveillance \nsites that have been established appear to have uncovered a sub-\nstantial proportion of the diversity in those locations.\n\nSupplementary Data\n\nSupplementary materials are available at The Journal of \nInfectious Diseases online (http://jid.oxfordjournals.org/). \nSupplementary materials consist of data provided by the author \nthat are published to benefit the reader. The posted materials \nare not copyedited. The contents of all supplementary data\nare the sole responsibility of the authors. Questions or messages \nregarding errors should be addressed to the author.\n\nNotes\n\nAuthor contributions. Conceptualization: H. S. and \nO. C.-A. Methodology: H. S., O. C.-A., N. L., B. N., and \nC. M. Investigation: O. C.-A. and B. N. Visualization: \nO. C.-A. Supervision: H. S. and V. D. Writing—original draft \nand writing—review and editing: All authors.\n\nData availability. Data and code used in this analysis are \navailable in a GitHub repository: https://github.com/ocortaz/ \nnipah_genetic_diversity. GenBank accession numbers are pro-\nvided in Supplementary Table 1, including new accession num-\nbers for sequences not previously published. Novel sequences \nare not currently publicly accessible on GenBank, but they \nwill be upon acceptance and/or request of the reviewers.\n\nDisclaimer. The views, opinions, and/or findings expressed \nare those of the authors and should not be interpreted as rep-\nresenting the official views or policies of the Department of \nDefense or the US government.\n\nFinancial support. This work was supported by the \nEuropean Research Council (grant number 804744), the \nCoalition for Epidemic Preparedness Innovations, the National \nInstitutes of Health (grant numbers NIH R01 AI168287-01A1, \nR01AI160780, and U01AI168287), the European Commission \nInnovate program (ComAcross project, grant number \nDCI-ASIE/2013/315–047), and the Defense Advanced \nResearch Projects Agency PREEMPT program (cooperative \nagreement D18AC00031).\n\nPotential conflicts of interest. All authors: No reported \nconflicts.\n\nAll authors have submitted the ICMJE Form for Disclosure \nof Potential Conflicts of Interest. Conflicts that the editors con-\nsider relevant to the content of the manuscript have been \ndisclosed.\n\nReferences\n\n1. World Health Organization. Nipah research and develop-\nment (R&D) roadmap. 2019. https://cdn.who.int/media/ \ndocs/default-source/blue-print/nipah_rdblueprint_roadmap_ \nadvanceddraftoct2019.pdf?sfvrsn=4f0dc9ad_3&download= \ntrue. 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Cortes MC, Cauchemez S, Lefrancq N, et al. \nCharacterization of the spatial and temporal distribution \n\nNipah Virus Diversity Across Space • JID 2024:230 (15 December) • e1243\nDownloaded from https://academic.oup.com/jid/article/230/6/e1235/7659519 by guest on 13 January 2025\n\nhttps://www.iucnredlist.org\nhttps://www.iucnredlist.org\nhttp://www.seabcru.org/wp-content/uploads/2014/09/Ravon-et-al.-2014.-Cambodian-Pteropus.pdf\nhttp://www.seabcru.org/wp-content/uploads/2014/09/Ravon-et-al.-2014.-Cambodian-Pteropus.pdf\n\n\nof Nipah virus spillover events in Bangladesh, 2007–2013. J \nInfect Dis 2018; 217:1390–4.\n\n44. Chaiyes A, Duengkae P, Suksavate W, et al. Mapping risk \nof Nipah virus transmission from bats to humans in \nThailand. Ecohealth 2022; 19:175–89.\n\n45. Eby P, Peel AJ, Hoegh A, et al. Pathogen spillover driven by \nrapid changes in bat ecology. Nature 2023; 613:340–4.\n\n46. Satter SM, Aquib WR, Sultana S, et al. Tackling a \nglobal epidemic threat: Nipah surveillance in \nBangladesh, 2006–2021. PLoS Negl Trop Dis 2023; 17: \ne0011617.\n\n47. Annand EJ, Horsburgh BA, Xu K, et al. Novel Hendra virus \nvariant detected by sentinel surveillance of horses in \nAustralia. Emerg Infect Dis 2022; 28:693–704.\n\ne1244 • JID 2024:230 (15 December) • Cortes-Azuero et al\nDownloaded from https://academic.oup.com/jid/article/230/6/e1235/7659519 by guest on 13 January 2025\n\n\n\tThe Genetic Diversity of Nipah Virus Across Spatial Scales\n\tMETHODS\n\tData Collection and Alignment\n\tPhylogenetic Analyses\n\tCharacterization of NiV Genetic Clusters\n\tRarefaction Analysis\n\tApproximate Bayesian Computation\n\tEthics Statement\n\n\tRESULTS\n\tDISCUSSION\n\tSupplementary Data\n\tNotes\n\tReferences\n\n\n\n\n","UrlSlug":"genetic-diversity-nipah-virus-across-spatial-scales","collections":[],"countries":[{"Name":"Bangladesh","Id":19},{"Name":"Cambodia","Id":39},{"Name":"Indonesia","Id":103},{"Name":"Malaysia","Id":133},{"Name":"Thailand","Id":217},{"Name":"India","Id":290}],"asset_languages":[{"AlternateTitle":null,"language":[{"Id":1,"Name":"English"}],"contentTranslation":[],"contentLocation":[]}],"creators":[{"Name":"Nikolay B","Id":19841,"ac":[{"Order":2}]},{"Name":"Gurley ES","Id":20431,"ac":[{"Order":11}]},{"Name":"Salje H","Id":20575,"ac":[{"Order":13}]},{"Name":"Duong V","Id":22737,"ac":[{"Order":12}]},{"Name":"Lemey P","Id":31360,"ac":[{"Order":8}]},{"Name":"Cortes-Azuero O","Id":35771,"ac":[{"Order":0}]},{"Name":"Lefrancq N","Id":35772,"ac":[{"Order":1}]},{"Name":"McKee C","Id":35773,"ac":[{"Order":3}]},{"Name":"Cappelle J","Id":35774,"ac":[{"Order":4}]},{"Name":"Hul V","Id":35775,"ac":[{"Order":5}]},{"Name":"Ou TP","Id":35776,"ac":[{"Order":6}]},{"Name":"Hoem T","Id":35777,"ac":[{"Order":7}]},{"Name":"Rahman MZ","Id":35778,"ac":[{"Order":9}]},{"Name":"Islam A","Id":35779,"ac":[{"Order":10}]}],"keywords":[{"Name":"genetics & genomics","Id":29},{"Name":"vector borne diseases","Id":171}]}}

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The genetic diversity of Nipah virus across spatial scales