Spatio-temporal dynamics of Hendra virus in Pteropus bats in Australia reveals high evolutionary diversity linked with spillover

Spatio-temporal dynamics of Hendra virus in Pteropus bats in Australia reveals high evolutionary diversity linked with spillover | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Spatio-temporal dynamics of Hendra virus in Pteropus bats in Australia reveals high evolutionary diversity linked with spillover Vincent Munster, Claude Kwe Yinda, John-Sebastian Eden, Erica Prates, and 20 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6263655/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Apr, 2026 Read the published version in Nature Microbiology → Version 1 posted You are reading this latest preprint version Abstract Hendra virus (HeV) was first discovered in 1994 in Australia, following an outbreak of respiratory and neurological disease in horses and humans. Limited genomic data on HeV has hindered a comprehensive understanding of HeV’s evolutionary dynamics. We conducted extensive spatiotemporal sampling and whole-genome sequencing of HeV-positive samples from bats and horses and genomic analyses revealed four distinct clades and additional cryptic clades. Each clade extended over a large spatial area, with strains from different clades co-occurring within a single roost on the same day and over multiple consecutive years. This absence of spatiotemporal genotypic structuring suggests that viral shedding events are not driven by the introduction of a single lineage into a susceptible population and then strain evolution through population level immune pressure. These findings provide crucial insights into how bats generate and maintain their extraordinary viral diversity, with direct implications for zoonotic disease emergence and pandemic threats. Biological sciences/Evolution/Population genetics/Genetic variation Biological sciences/Microbiology/Virology/Viral evolution Biological sciences/Microbiology/Virology/Viral reservoirs Biological sciences/Microbiology/Virology/Viral transmission flying fox Henipavirus Hendra virus genetic diversity phylodynamics phylogeography Figures Figure 1 Figure 2 Figure 3 Figure 4 Significance Statement Limited genomic data has constrained our understanding of the genetic diversity and transmission dynamics of Hendra virus (HeV). By expanding the number of available HeV genomes from 15 to 73, this study provides a more detailed and comprehensive view of the virus’s genetic variability. The discovery of four distinct and cryptic clades, alongside the absence of spatiotemporal structuring within bat populations, indicates that HeV persistence and spillover are not driven by localized outbreaks or immune-driven strain evolution. The results demonstrate the significant genetic diversity of HeV maintained in bats and lay the foundation for ecologically informed disease surveillance, with broader implications for reducing zoonotic disease emergence and managing public health risks associated with other bat-borne viruses. Introduction Bat populations can host diverse communities of viruses, ranging from those of little or unknown significance to highly fatal zoonotic pathogens 1 , 2 . Together, targeted surveillance programs and unbiased viral discovery efforts have highlighted the breadth of viral species, genera and families hosted by bats, yet we have limited understanding of how diversity is structured within individual viral species across time and space 3 . Understanding these evolutionary patterns could provide key insights into how viruses are maintained in bat populations and which factors drive their emergence in new hosts 4 . As a WHO priority pathogen and with decades of multidisciplinary research, Hendra virus (HeV, genus Henipavirus ) provides an ideal system for addressing this gap 5 . HeV is a non-segmented, negative-strand RNA virus with an 18kb genome encoding six proteins: nucleocapsid, phosphoprotein, matrix protein, fusion glycoprotein, attachment glycoprotein, and large polymerase. The virus circulates endemically in Australian flying foxes ( Pteropus spp), with high case fatality rates when it spills over into horses and humans (75% and 57%, respectively) 6 – 11 . In the 30 years since it emerged during a disease outbreak in the Brisbane suburb of Hendra, Australia 12 , 67 spillover events have been documented 13 , 14 . This includes two spillovers of a novel genotype (HeV-g2) that is sufficiently different from the original genotype (HeV-g1) to evade routine diagnostic assays until it was discovered by sentinel surveillance in 2021 (89% nucleotide and 92.5% amino acid identity) 15 – 17 . Australian flying foxes ( Pteropus spp) are natural reservoir hosts of HeV, with serological assays, virus isolation and PCR indicating that HeV circulates among all four flying fox species in continental Australia: the black flying fox ( P. alecto ), the spectacled flying fox ( P. conspicillatus ), the little red flying fox ( P. scapulatus ), and the grey-headed flying fox ( P. poliocephalus ) 17 – 20 . These bats are nomadic and nocturnal foragers, moving large distances between roost sites 21 – 23 that comprise hundreds to hundreds of thousands of individuals 14 , 24 . The factors contributing to HeV shedding in flying foxes and spillover dynamics into horses have been extensively studied 13 , 14 , 25 – 28 , elucidating the mechanistic links between habitat loss, climatic variations, and the heightened risk of HeV spillover in Australia. However, the evolutionary dynamics of HeV remain under-explored. The passing of nearly three decades between the discovery of HeV and the identification of a second genotype underscore the gaps in understanding its genetic diversity 16 , 17 . To date, HeV genomic data remain sparse, with the majority consisting of short, incomplete genomic sequences 20 , 29 , 30 . Most of these existing genomes were obtained from horses (n = 9), with few from the natural reservoir (n = 4). This limited genomic information has hindered the ability to describe HeV diversity and explore evolutionary patterns of the virus in the reservoir host. A deeper investigation into the genetic variability of HeV across different temporal and geographical scales is crucial to understanding the virus's evolution and dynamics within its natural reservoir, including how new viral strains with altered host tropism or transmission potential emerge. Such knowledge is essential for refining predictive models of spillover events and for the development of targeted strategies to mitigate the public health risks associated with HeV. Here we describe the results of a large, genome-scale phylodynamic analysis of HeV-g1 strains in Australia. By generating and analyzing a data set of 73 whole-genome sequences over a time span of 26 years, we define circulating clades of HeV in Australian fruit bat populations and infer their temporal and spatial evolutionary dynamics. This study provides crucial insights into the evolutionary and ecological dynamics of HeV in Australian fruit bat populations, with broader implications for predicting and mitigating zoonotic disease emergence. Results HeV-g1 whole genome sequencing Of 9869 flying fox urine samples screened by PCR from 24 roosts (Supplementary (Suppl.) Table 1, Suppl. Figure 1), 703 tested positive for HeV-g1 with Ct values ≤ 40, and 253 of these had Ct values ≤ 32, making them suitable for sequencing. Overall, the success of HeV isolation from PCR-positive bat samples was low (3/177; 1.7%), with full genome sequences obtained from the three samples that generated isolates (Ct values of 24, 35, and 37). In total, we generated 48 HeV-g1 full genome sequences (6.8.0% success) with a mean coverage depth of 415× (range, 8.0× to 3,700 ×) from bat urine samples and the cycle threshold value (Ct) of these samples ranged between 21 to 32 (7.4 x 10 6 − 8.3 x 10 3 genome copies/mL) (Suppl. Table 2). This included 5 genomes from P. alecto individuals and 43 genomes from under-roost sampling sheets, sampled from roosts spanning 595km along the coastline of eastern Australia (~ 12,000 km 2 ). Host genomic sequences detected within the sequencing libraries confirmed P. alecto as the source of these samples. We also generated 10 novel HeV sequences from horses and obtained 15 published sequences from NCBI GenBank (Suppl. Table 2). Within the combined dataset of 73 HeV-g1 sequences, genetic variation across the genome was relatively low with dips in identity mostly restricted to the intergenic regions: the mean nucleotide identity was 96.9% (range, 47 to 100%) with the lowest values observed at the 3’-end (Suppl. Figure 2). Base composition was mostly stable across the genome, with a mean GC content of 39.8%. The exception was a marked decrease in GC content at the intergenic regions and at the 5’ and 3′ end of the genome, corresponding to an AT-rich untranslated region. Phylogenetics and circulating diversity of HeV-g1 in Australian Pteropus spp. The phylogenetic analysis of all 73 HeV-g1 genome sequences from humans, horses and fruit bats revealed four well-sampled monophyletic clades of bat-derived sequences tentatively named clades A, B, C, and D (Fig. 1 , Suppl. Table 3). All four clades also included HeV-g1 sequences detected in horses (Fig. 1 a, Suppl. Figure 3–4). A few cryptic sequences not belonging to clades A-D were detected in horses and humans between 1994 and 2008, during a period when limited bat sampling was conducted (Fig. 1 b, Suppl. Figure 5). The mean inter- and intra-clade distance across the genome was < 99.5% and < 99.6%, respectively. We found that clade- and sub-clade defining mutations rarely occur in the G glycoprotein (Fig. 1 a and Supp. Table 3), suggesting that HeV’s adaptation to its host entry receptors, ephrin B2 and ephrin B3, remains stable. There was limited evidence for adaptive evolution in the datasets, based on the absence of strong positive selection signals, across the viral genomes (Suppl. Table 4). In addition to generating complete genomes, we sequenced 13 additional G glycoprotein sequences and retrieved 6 more G glycoprotein sequences from GenBank (Suppl. Table 2). To verify the consistency of the phylogenetic topology, we used both the coding sequences and a more comprehensively sampled G glycoprotein gene dataset to construct the HeV-g1 phylogeny (Suppl. Figure 6). The trees constructed using the G glycoprotein sequences showed the same topology and clustering patterns as the complete genome tree. Sequences belonging to the four main clades co-circulated in time and space in the bat population. Each clade comprised sequences from flying fox roosts in both New South Wales and Queensland (Suppl. Figure 3–4) and at least three of the four clades co-circulated in each year of our intensive field sampling (2017–2020) (Fig. 1 b). The largest number of genomes were recovered in 2017 and 2018 between June and September, corresponding to the peak of HeV shedding within the natural reservoir during the Australian winter months (Fig. 1 d, Suppl. Figure 5) 28 . Clade A is made up of eight novel bat strains and two horse strains (from the 2009 and 2014 spillover events, both in Queensland). While the 2014 horse sequence (HORSE003/Horse/QLD/South_Kolan/2014-03-17) forms a monophyletic clade with the bat sequences, the 2009 strain is distantly related with a percentage nucleotide identity of 99.3–99.8% to the rest of the clade. Clade B grouped into two subclades. One subclade contains just one strain, collected in 2018 from New South Wales, that is 99.5–99.7% identical to the rest of the clade. The rest of the clade shares a nucleotide identity of 99.8–100%, comprising ten bat strains and one horse strain arising from a 2023 HeV spillover in New South Wales. Similarly, clade C is made up of 10 strains, including nine bat strains and one spillover horse strain (HORSE009/Horse/NSW/South_Arm/2013-06-06), all identified in this study, and sharing a nucleotide identity of 99.6–99.7%. Finally, clade D is the largest and contains a monophyletic group of four previously published bat strains from bats sampled in 2009 with three sharing a nucleotide percentage identity of 100%, from bats sampled in 2009 30 . This clade also comprises four strains from spillover events in horses, including two in Queensland (HORSE002/Horse/QLD/Tamborine_Mt/2017-05-25 from May 2017 and HORSE004/Horse/QLD/Currumbin_Valley/2011-08-22 from August 2011) and two in New South Wales (HORSE001/Horse/NSW/Clunes/2015-09-03 from September 2015 and HORSE007/Horse/NSW/Pimlico/2011-08-15 from August 2011). Importantly, we did not identify any examples where we could link a spillover strain directly to the sampled bat diversity (both temporally and by sequence identity); however, only 20 of 89 known horse spillovers had sequences available for inclusion within this phylogeny. Overall, the HeV-g1 phylogeny showed a spatially diffuse structure with a high diversity of strains maintained in the bat population. Co-circulation of multiple strains within and across clades was detected within the same roosts at the same time and over multiple consecutive years (Fig. 1 d, Fig. 2 ). Within each clade, detections spanned 500km, 650km, 172km and 1535km for clades A to D, respectively (or 380km and 172km for clades A and B, respectively, if only bat sequences are considered). Amongst roosts from which new genomes were recovered, we detected an average of 2.2 clades per roost. The highest number of genomes were obtained from samples collected in Toowoomba (17 genomes: as 2:4:2:9 for clades A:B:C:D, respectively), Clunes (12 genomes: 4:3:0:5), and Redcliffe roosts (5 genomes: 1:1:2:1) (Suppl. Table 2). Three genomes were recovered from Sunnybank (clades A, D and D) and Nambucca Heads (A, C and C) roosts, two from Lismore (C, D) and Simpsons Creek (C, C) roosts, and one from each of Mount Ommaney (D), Canungra (A), Currumbin (B), and Hervey Bay (D) roosts. Temporal analysis of HeV-g1 HeV-g1 sequences demonstrated strong temporal signal, indicative of clock-like evolution (Suppl. Figure 7). Linear regression revealed a strong correlation between root-to-tip genetic distance against time of sampling for complete genome and coding sequences (correlation coefficients: 0.78 and 0.74, respectively). While G glycoprotein showed a weaker temporal signal (correlation coefficient: 0.53), this may be due to reduced phylogenetic signal in this region. We next used a Bayesian Markov chain Monte Carlo (MCMC) approach to more rigorously assess the evolutionary dynamics of HeV. Based on the best fit model (Suppl. Table 5), the coding sequences showed a refined estimate for the mean rate of evolution of 1.43 × 10 − 4 (95% HPD, 1.07 × 10 − 4 to 1.80 × 10 − 4 ) subs/site/year. The time to the most recent common ancestor (TMRCA) estimate was 1975 (95% HPD, 1954.80 to 1988.40) across the whole tree. It was estimated that clades A and B have been circulating for 24–25 years (since 1995–1996) while clade C and D have been circulating for 15 years (since 2005) (Fig. 3 ). Noticeably, the evolutionary history of clades A and B is under-sampled (reflected by the ~ 15 years gap between the timing of the divergence of these clades and the sampling of representative sequences), whereas clades C and D have been more continuously sampled since their divergence (Fig. 3 ). Some spillover sequences emerged before 1994 (with branch lengths representing > 20years of evolution before detection) or around the same time as clade C and D (~ 2002–2005); however, we did not detect these in the reservoir population. Due to under-sampling in flying foxes prior to the mid 2000’s, a large proportion of horse- and human-derived strains remain unclassified and most more recently detected strains in bats erroneously appear to have evolved from horse- and human-derived strains. A notable exception here can be found in clade D, where we find a more recent (August 27, 2018) bat strain that clusters with two horse-derived strains identified on 15 and 22 August 2011 (Suppl. Figure 2). However, permutation analyses supported the recent emergence of clade B, indicating that the likelihood of clade B being present but not observed before 2018 was 0.02 (details in Suppl. Results and Suppl. Figure 8). Structure prediction and potential effect of clade defining mutations We used experimentally determined or predicted structures of these proteins for a preliminary discussion of their potential functional effects, particularly focusing on the mutations that define the most recently emerged clade, clade B (Fig. 4 , details in Suppl. Discussion). The predicted model indicates that the two identified LD mutations in the G glycoprotein, S175T and A336T, are distant from the host protein binding surface and likely neutral (Fig. 4 ). Consequently, despite these substitutions, the key interaction sites with the host receptor ephrin-B2 and the dimer interfacial contact regions remain largely unaffected. In contrast, LD mutations across the P/V/W and L proteins may have functional implications, particularly when they co-occur (Suppl. Figure 9–11, Suppl. Discussion). Discussion Understanding how viral populations are structured and maintained within bat hosts over time and space has been hindered by a lack of comprehensive genomic data, despite extensive sampling efforts in bat populations. This knowledge gap, in turn, limited our ability to predict and mitigate spillover risk of zoonotic viruses like HeV, which remains a threat to veterinary and public health Australia 13 , 14 . Our study addresses this gap by employing extensive spatiotemporal sampling and viral enrichment sequencing. We screened 9,853 flying fox urine samples collected over nearly four years, identifying over 600 HeV-positives samples. Of these, 253 samples with Ct < 32 were selected for sequencing, and through customized viral enrichment methods, we generated 48 new HeV genomes from bats. Together with an additional ten genomes from HeV-infected horses, this study increased the number of HeV genomes available by almost fivefold (from 15 to 73). Our analyses revealed the presence of four distinct HeV clades co-circulating in bats, with no distinct spatiotemporal structuring or evidence of immune-driven strain evolution, providing new insights into viral maintenance in bat populations. Theoretical expectations about viral dynamics in populations suggest that, due to selective pressures like immune evasion and transmissibility, a single or set of viral strains should become dominant at any given time, leading to clear temporal and spatial clustering 31 . Over time, this process should reduce viral diversity through adaptive evolution, driven by factors such as immune pressure or environmental changes. In contrast to these expectations, our results show that HeV variants co-exist simultaneously across wide geographic areas, with no distinct temporal or spatial clustering. While long-distance movements between roosts likely facilitates the sharing of HeV strains within our study area (~ 12,000km 2 ), immune pressure, if present, should shape the strains/variants present at any given time via adaptive evolution. The co-existence of four divergent clades in both time and space suggest that selective pressures are minimal or absent, and that adaptive evolution plays a limited role in shaping HeV diversity within these natural reservoirs. We previously hypothesized that phylogenetic signatures could distinguish between competing hypotheses of viral infection dynamics in bats 5 . Acute infections, cleared quickly, should exhibit low diversity within outbreaks (pulses) but high diversity between them, reflecting distinct viral lineages from single introductions. By contrast, persistent infections should show higher viral diversity within each pulse due to within-host evolution, but low diversity between pulses. At the time, the large-scale genomic surveillance data required to validate these hypotheses did not exist. The HeV genomic data presented here align with patterns expected under persistent infections, supporting the theory that bats can maintain HeV infections for extended periods. This suggests that intermittent shedding may be triggered by host or environmental factors. This stable co-existence of divergent HeV lineages within flying fox populations is somewhat consistent with observations in the closely related Nipah virus (NiV) but contrasts sharply with expectations from other systems where selective pressures lead to rapid turnover and spatial clustering of viral strains. For NiV, two distinct genogroups have been described (I and II), comprising four minor genotypes (IA, IB, IIA, and IIB), and a total of 15 genetic ‘clusters’ 32 . Each genotype shows broad spatial structure across the Indomalayan region, with countries on the eastern (Indonesia and Malaysia) and western (India and Bangladesh) edges of the region having only one circulating genogroup currently identified, while central countries (Thailand and Cambodia) have sequences from both genogroups 33 . At a finer genetic scale, NiV genetic clusters exhibit overlapping spatial structuring within the same country, and an average of 2.41 genetic clusters per roost (analogous to estimates of 2.2 clades per roost here) 33 . Some genetic clusters spanned > 2100km, comparable to our observations. 33 . By contrast, for bat-borne rabies virus, the phylogeny is structured by host species, with obvious temporal signal reflecting an evolutionary history of host shifts followed by predominantly within-species transmission 34 – 37 . Longitudinal sampling of vampire bats ( Desmodus rotundus ) over 14 years identified limited spatial and temporal overlap of rabies virus clades, likely due to extinction-recolonization dynamics 34 . Bat rabies viruses in monophyletic clades also show a high level of amino acid conservation, suggesting that rabies virus diversity within reservoir species is highly constrained and evolving under a model of purifying selection 38 , 39 . Lineage-defining (LD) mutations in P/V/W, L, or in the G glycoprotein, and the rare occurrence of these mutations in the G glycoprotein (Fig. 1 a and Suppl. Table 3), amongst our dataset suggests that HeV adaptation to its host entry receptors, ephrin B2 and ephrin B3, remains stable and their highly conserved binding interface across mammalian species benefits the broad host tropism of HeV. The two identified LD mutations in the G glycoprotein, S175T and A336T, are distant from the host protein binding surface and likely neutral (Fig. 4 ). Conversely, the presence of several LD mutations across P/V/W and L proteins, and their combined occurrence, may indicate potential for epistatic interactions, warranting future investigation. Furthermore, we conducted a preliminary examination of the mutations defining the newly identified clade B and discussed the potential functional effects of these mutations in Suppl. material (Suppl. Figure 10, and Suppl. Figure 11, Suppl. discussion). For example, we speculated that the mutation P352S in P/V/W may not be functionally silent. Even though it is located in the long intrinsically disordered region, which is highly variable among paramyxoviruses, it is part of a short motif that is highly conserved between HeV and NiV (Suppl. Figure 9). Both viruses employ the V protein as an immune evasion strategy to evade STAT1/STAT4-dependent interferon responses. Our observations encourage a follow-up study to evaluate the impact of this substitution on the interaction with host proteins and phenotypic differences in clade B relative to the other clades. Alongside the discovery of HeV-g2 16,17 , our results suggests that the true diversity of HeV and related viruses in flying fox populations are incomplete, and intensified surveillance endeavors would likely uncover more divergent strains and variants. Most HeV-positive samples from this study had Ct values greater than 32 (< 3 x 10 4 copies/mL) indicative of a low viral load and making the recovery of full genomes challenging. This further emphasizes the need for large, longitudinal studies and viral enrichment sequencing to obtain sufficient genomes for analyses such as we present here. In addition, there exist historical gaps in the sequence available hindering a full understanding of the HeV evolution. Conclusion We characterized the genetic diversity of HeV-g1 and identified the circulating clades, observing that individual flying fox roosts host multiple strains. The absence of genotypic structuring within the bat population, combined with minimal evidence of immune-driven selection, implies that immune pressure is insufficient to drive the evolution of these strains. The stable maintenance of diverse viral clades over four consecutive years supports a model of long-term infections in individual bats, with episodic shedding, rather than epidemic waves with strain replacement. Finally, our detection of novel circulating HeV clades in bats indicates that we have yet to fully characterize circulating viral diversity and potentially distinct phenotypic traits, highlighting the importance of enhanced surveillance and in-depth research to fully understand the implications for spillover dynamics and public health. Online Methods Sample collection Sample collection from flying foxes was covered under Griffith University Animal Ethics Committee Approval ENV/10/16/AEC and ENV/07/20/AEC, and is described in full in Peel, Yinda et al., 2022 16 . Briefly, sampling was conducted at flying fox roosts in southeast Queensland and mid- to north-coast New South Wales between December 2016 and September 2020 (Suppl. Table. 1, Suppl. Figure 1). Urine samples were collected from both individual bats captured in mist nests at their roost site and from plastic sheets placed underneath roosting trees pre-dawn 28 . For captured bats, urine samples were collected directly from the bat if it urinated while anaesthetized, or from a urine collection bag attached to its holding bag (the bottom third was lined with plastic). Information on species, sex, and age class (adult, subadult, juvenile) was also recorded. For samples collected from sheets, a single pooled urine sample was collected from each sheet, and the number and species of bats immediately above the sheet were recorded. Sheet placement was prioritized to target black flying-fox roosting locations, but other species were also present. For both sample types, urine was pipetted into a tube with AVL lysis buffer (Qiagen, target 140ul of urine into 560ul buffer), viral transport medium (VTM, between 200–1000µl of urine into 1000ul of VTM) or a plain cryovial. Samples were held on ice during collection, then transferred to a CryoShipper (< -80°C) for transport and stored at -80°C in the laboratory. RNA extraction and HeV screening of bat samples RNA was extracted using the QIAamp Viral RNA Kit using a QIAcube HT automated system (Qiagen, USA) according to the manufacturer's instructions. RNA was eluted in 150µl of TE buffer and used for HeV screening using a qRT-PCR assay targeting the viral P gene (5'-CCCAACCAAGAAAGCAAGAG (forward), 5'-TTCATTCCTCGTGACAGCAC (reverse) and 5'-TTACTGCGGAGAATGTCCAACTGAGTG (probe)). Ten µl RNA was tested with a TaqMan Fast Virus 1-Step master mix on QuantStudio 6 Flex Real-Time PCR instrument (Applied Biosystems, USA) according to instructions of the manufacturer. Samples were deemed positive with genome detection at cycle threshold (Ct) value of ≤ 40. Spatiotemporal analyses of PCR results generated as part of the same study are described in Lunn et al 28 . Positive samples with Ct values ≤ 32 were selected for further whole genome recovery. Virus isolation Where positive samples had available sample aliquots in VTM (n = 177), virus isolation was performed to enhance genome sequencing success. Vero E6 cells in a 24-well plate were inoculated with 250 µl original undiluted sample and a 1:10 dilution thereof in different wells. Diluted and undiluted samples were plated in duplicates. Plates were centrifuged for 30 minutes at 1000 rpm and incubated for 30 minutes at 37°C and 5% CO2. The inoculum was removed and replaced with 500 µl DMEM containing 2% FBS, 50 U/ml penicillin and 50 µg/ml streptomycin. Two and three days after inoculation, cytopathic effect (CPE) was scored. Blind passage was performed on samples without CPE as above; other than that, plates were not spun. Supernatants from plates with CPE present were analyzed via qRT-PCR for Hendra virus RNA to rule out other causes of CPE. RNA of positive samples was subjected to full genome recovery. Partial and full genome recovery of bat HeV-g1 Sequencing libraries were prepared using either the Kapa HyperPrep for DNA library preparation kit or the Kapa RNA HyperPrep library preparation kit according to manufacturer’s protocols (Roche Sequencing Solutions, USA, details in Suppl. materials). Then target enrichment was performed as follows: Kapa libraries were quantified using the area under the curve on Bioanalyzer traces as well as UV spectrophotometry on the Nanodrop instrument (ThermoFisher Scientific, USA). Pre-capture sequencing libraries were normalized and then combined in 4- to 12-plex reactions for solution capture hybridization using version of VirCapSeq-VERT probe set, described by Briese et al. 40 or custom myBaits probe library. For VirCapSeq-VERT probe set, Kapa libraires were enriched for virus following the SeqCap EZ HyperCap Workflow User’s Guide, version 2.3, while for custom myBaits probe library, the myBaits Hybridization Capture for Targeted NGS protocol, Version 4.01 was used. Additionally, PCR based amplicon-enrichment was performed on the samples. First, tiled primers were designed using primal scheme multiplex command line interface following existing methods 41 . Then, single-stranded cDNA was generated as described above and 2.5 µL was used as template 42 in a PCR assay using HeV-specific tiled primers that generated over-lapping 400 bp amplicons spanning the HeV genome. Even and odd multiplex PCR conditions/amplicon generation using Q5 HotStart Polymerase PCR (ThermoFishcer Scientific, USA) was optimized following the ARTIC nCoV-2019 protocol above with the following modifications: 2X primer concentration in each even/odd primer pools with 35 cycles at a 52 0 C/1-min annealing temperature and 72 0 C /30-sec extension. Products from each primer pool were combined, AmPure XP purified, and quantified with Qubit (ThermoFishcer Scientific, USA) fluorometric quantitation as per instructions, and library generation and sequencing were performed as outlined in the Suppl. Methods 2. After sequencing, adapters were removed from raw fastq files using cutadapt v 1.12 and reads were quality trimmed and filtered using the FAST-X toolkit 43 . Trimmed and filtered paired-end (PE) reads were processed through a custom viral and bacterial database screening and de novo assembly pipeline to identify and assemble reads mapping Hendra virus. Details of the bioinformatic pipeline can be found in the Suppl. Methods 3. Lastly, we subjected some of the samples to long range PCR amplification of the attachment (G) glycoprotein. Single-stranded cDNA was generated as described above 42 and long range semi-nested PCR was performed on samples for which full genomes could not be recovered according to previously established protocol (Suppl. Table 6) 44 . Sequencing was performed as outlined in the Suppl. Methods 2. Full genome recovery of HeV-g1 from horses Clinical specimens, blood, serum, and nasal swabs were also collected from horses with symptoms and submitted to the Australian Centre for Disease Preparedness (ACDP) for the diagnosis of HeV. Virus isolation was conducted at the Biosafety Level 4 laboratory at ACDP. The samples were passed on Vero cells (ATCC CCL-81) for three consecutive times of 7 days of each passage. The samples were then tested by real-time quantitative reverse transcription PCR (qRT-PCR) to confirm the presence of replicating HeV genome. Then full genome sequencing was performed as reported in Wang et al., 2021 45 . Briefly, viral RNA was extracted by using MagMax 96 Viral RNA Kit (ThermoFisher Scientific, USA) in a MagMAX Express Magnetic Particle Processor (ThermoFisher Scientific, USA) following manufacturer’s instructions. TruSeq RNA library Prep Kit V2 (Illumina, USA) was utilized for DNA library preparation, according to manufacturer’s instructions. The purified libraries were quantified using a Qubit Fluorometer and an Agilent 2100 Bioanalyzer (Agilent Technologies, USA). The concentration of the final libraries was normalized and pooled in equimolar ratios. The library pool was then loaded into a MiSeq Reagent Kit V2 (2 x 150 cycles; Illumina, USA) and sequenced in a MiSeq platform (Illumina, USA) according to the manufacturer’s instructions. The NGS sequence data was analyzed using CLC Genomic Workbench 20 (Qiagen, USA) using standard parameters. The raw reads were quality-trimmed before assembly by both reference mapping (using HeV genomes available) and de novo assembly. The resultant sequences were confirmed as HeV-g1 by blasting against the NCBI nonredundant nucleotide database ( https://blast.ncbi.nlm.nih.gov/Blast ). Phylogenetic and evolutionary analysis Phylogenetic analysis To examine the genome-wide evolution of HeV-g1 in Australia, an alignment was prepared using 72 complete genome sequences with MAFFT 46 including 58 from this study and 15 reference strains from NCBI GenBank. A second alignment was also prepared with non-coding regions removed. Finally, to improve sampling from incomplete genomes available in our own dataset and in public dataset, we prepared a third alignment of G glycoprotein sequences (coding region) that contained additional 19 sequences. The best model for distance estimates were identified with the ModelFinder function 47 in IG-TREE2 48 as the one with the lowest Bayesian information criterion (BIC). Maximum likelihood phylogenetic tree was also constructed using IG-TREE2 and branch support was assessed using both ultrafast bootstrap approximation (ufBoot, 1000 replicates) 49 and SH-like approximate likelihood ratio test (SH-aLRT). The tree was visualized in FigTree ( http://tree.bio.ed.ac.uk/software/figtree/ ), and midpoint rooted for purposes of clarity. Identification of clade-defining mutations JalView 50 was used for genome translation and for the identification of persistent mutations in the viral proteins. The oldest genome of HeV sampled from a human in our library (ID: KY425627, Supplementary Table 2) was used as reference to annotate the mutations. Experimentally determined or AlphaFold 51 structures predicted with a local installation were used to locate mutations and for a preliminary inference of potential functional impacts. Visual Molecular Dynamics was used for visual inspection 52 . Phylodynamic analysis First, we assessed the temporal signal in HeV-g1 data by linear regression of root-to-tip distances on the Maximum Likelihood phylogeny against time of sampling using the program TempEst ( http://tree.bio.ed.ac.uk/software ). Exact dates were available for all sequences generated in this study. For two samples for which the exact day of sample collection was not known mid-month dates were assigned. Given the apparent temporal structure in the genome-scale alignments data, we then made estimates of the rates of evolutionary change (i.e., nucleotide substitutions per site per year) and the time to most recent common ancestor (TMRCA) using the Bayesian Markov chain Monte Carlo (MCMC) method available in BEAST (version 1.10.4) 53 . Here, we chose to use the complete coding regions dataset to make use of the SRD06 codon substitution model 54 and tested four different temporal & demographic scenarios: relaxed and strict molecular clock each with constant population and SkyGrid coalescent model 55 with path sampling used to rank the final models. For each analysis, three independent chains of 50 million generations (sampled every 10,000 states) were run to ensure convergence and then combined with appropriate burn-in. Statistical uncertainty was reflected in values of the 95% highest probability density (HPD). The maximum clade credibility (MCC) tree was estimated from the posterior distribution of trees with node heights scaled to mean values and posterior probabilities showing the statistical support for individual nodes. Determination of the nature of selection pressures Next, we sought to determine the nature of selection pressures acting on HeV-g1 using the Datamonkey web server of the HyPhy package 56 ( http://www.datamonkey.org/ ). Accordingly, codon-based ML methods were used to estimate the ratio of nonsynonymous to synonymous substitutions per site ( dN / dS ratio; also denoted ω); fixed-effects likelihood (FEL); fast, unconstrained Bayesian approximation (FUBAR); single likelihood ancestor counting (SLAC); and random effects likelihood (REL). Those sites with P values of 0.95 were considered to provide significant evidence of positive selection. Spatiotemporal analyses We tested for temporal structure in the presence of different clades using a permutation test that maintained the biological and spatial structure of the dataset by shuffling clade labels within the same host species and region. This approach allowed us to assess two metrics: (1) whether clade B was present before 2018 and (2) changes in the relative proportions of clades over 6-month intervals. Statistical significance was determined by comparing observed values to null distributions generated from 1,000 permutations. Full methodological details are provided in the Suppl. Methods 4. Data availability and Visualization All novel sequences reported here have been submitted in GenBank (accession numbers are in (Suppl. Table 2). The tree was visualized in FigTree ( http://tree.bio.ed.ac.uk/software/figtree/ ). Maps were created in R version 4.3.0 57 (Suppl. Methods). Declarations Author Contributions: CKY, AJP, JW, KM, RKP and VJM conceived the research; CKY, AJP, JW, KM, RKP and VJM designed the research methodology; AJP, RKP and VJM acquired funding for the research; MKK, PE, DNJ-S, CAF, ASD, JW, KM, RKP, and AJP collected samples; CKY, AJP, JW, KM, RKP, KH and VJM led project administration; CKY, JW, KM, SLA, TB, KMW, KB and CM curated the data; CKY, JSE, BB, ETP, AV, MS, MP, WC, DJ, ECH and VJM analyzed and visualized the data; ETP, AV, MS, MP, DJ described clade mutations; AJP, CM, RKP and VJM provided supervision; CKY, SLA, TB, and KB conducted laboratory screening, testing and validation; RKP, AJP and VJM provided resources; CKY, JSE AJP, and VJM drafted the manuscript; and all authors participated in review and editing of the manuscript. Competing Interest Statement: The authors declare that they have no conflict of interest. Data availability and Visualization All novel sequences reported here have been submitted in GenBank (accession numbers are in (Suppl. Table 2). The tree was visualized in FigTree (http://tree.bio.ed.ac.uk/software/figtree/). Maps were created in R version 4.3.0 57 (Suppl. Methods). Acknowledgements The project was supported by the National Science Foundation (DEB1716698, EF-2133763/ EF-2231624), the DARPA PREEMPT program Cooperative Agreement # D18AC00031, and the Intramural Research Program of National Institute of Allergy and Infectious Diseases of the National Institutes of Health. The content of the information does not necessarily reflect the position or the policy of the U.S. government, and no official endorsement should be inferred. TJL was supported by an Endeavour Postgraduate Leadership Award and a Research Training Program scholarship sponsored by the Australian Government. AJP was supported by a Sydney Horizon Fellowship, an ARC DECRA fellowship (DE190100710), and a Queensland Government Accelerate Postdoctoral Research Fellowship. We acknowledge the Bundjalung, Butchulla, Danggan Balun, Gomeroi, Gumbainggir, Kabi Kabi, Taribelang Bunda, Turrbal, Widjabul Wia-bal, Yugambeh and Yuggera Ugarapul people, who are the Traditional Custodians of the land upon which this work was conducted. We also thank government and private landholders for granting permission for fieldwork and broader team members and volunteers for their contributions, including Peggy Eby, Maureen Kessler, Adrienne Dale, Manuel Ruiz Aravena, Liam Chirio, Mandy Allonby, Rachael Smethurst, Remy Brooks, Tim Pearson, Liam McGuire, Kirk Silas, Ticha Padgett-Stewart, Denise Karkkainen, Justine Scaccia, Ariana Ananda, Emma Glennon, Hannah Eiseman, Cinthia Pietromonaco, Sara LaTrielle, Isaac Knights, Dian Riseley, Emma Spence, Stella Maris Januario da Silva and many others. Hendra virus qRT-PCR primers and probe sequences were provided by the Public Health Agency of Canada. We thank Dan Sturdevant who designed the primers for amplicon-based sequencing, Julia R. Port, Jyothi Purushottam, Jonathan Schulz, and Zack Weishampel for helping to screen bat samples. We thank staff at Diagnostic Virology Team, Australian Centre for Disease Preparedness for technical supports, including virus isolation at BSL4 laboratory. We thank the Queensland and New South Wales Chief Veterinary Officers for allowing us to include the HeV horse sequences in this paper, and we thank the relevant veterinary and laboratory staff in each of these states for their contributions to the disease outbreak investigations. References Wang, J. et al. Individual bat virome analysis reveals co-infection and spillover among bats and virus zoonotic potential. Nature Communications 14 , 4079 (2023). https://doi.org/10.1038/s41467-023-39835-1 Wang, Y. et al. Unveiling bat-borne viruses: a comprehensive classification and analysis of virome evolution. Microbiome 12 , 235 (2024). https://doi.org/10.1186/s40168-024-01955-1 Olival, K. J. et al. Host and viral traits predict zoonotic spillover from mammals. Nature 546 , 646-650 (2017). https://doi.org/10.1038/nature22975 Irving, A. T., Ahn, M., Goh, G., Anderson, D. E. & Wang, L.-F. Lessons from the host defences of bats, a unique viral reservoir. Nature 589 , 363-370 (2021). https://doi.org/10.1038/s41586-020-03128-0 Plowright, R. K. et al. Transmission or Within-Host Dynamics Driving Pulses of Zoonotic Viruses in Reservoir–Host Populations. PLOS Neglected Tropical Diseases 10 , e0004796 (2016). https://doi.org/10.1371/journal.pntd.0004796 Field, H. et al. The natural history of Hendra and Nipah viruses. Microbes and infection 3 , 307-314 (2001). Field, H. et al. Hendra virus outbreak with novel clinical features, Australia. Emerging Infectious Diseases 16 , 338 (2010). Playford, E. G. et al. Human Hendra virus encephalitis associated with equine outbreak, Australia, 2008. Emerging Infectious Diseases 16 , 219-223 (2010). Halpin, K. et al. Pteropid bats are confirmed as the reservoir hosts of henipaviruses: a comprehensive experimental study of virus transmission. Am J Trop Med Hyg 85 , 946-951 (2011). https://doi.org/10.4269/ajtmh.2011.10-0567 Eaton, B. T., Broder, C. C., Middleton, D. & Wang, L.-F. Hendra and Nipah viruses: different and dangerous. Nature Reviews Microbiology 4 , 23-35 (2006). https://doi.org/10.1038/nrmicro1323 Kummer, S. & Kranz, D.-C. Henipaviruses—A constant threat to livestock and humans. PLOS Neglected Tropical Diseases 16 , e0010157 (2022). https://doi.org/10.1371/journal.pntd.0010157 Murray, K. et al. A morbillivirus that caused fatal disease in horses and humans. Science 268 , 94-97 (1995). https://doi.org/10.1126/science.7701348 Plowright, R. K. et al. Ecological dynamics of emerging bat virus spillover. Proceedings of the royal society B: biological sciences 282 , 20142124 (2015). Eby, P. et al. Pathogen spillover driven by rapid changes in bat ecology. Nature 613 , 340-344 (2023). https://doi.org/10.1038/s41586-022-05506-2 Taylor, J. et al. Novel variant Hendra virus genotype 2 infection in a horse in the greater Newcastle region, New South Wales, Australia. One Health 15 , 100423 (2022). https://doi.org/10.1016/j.onehlt.2022.100423 Peel, A. J. et al. Novel Hendra Virus Variant Circulating in Black Flying Foxes and Grey-Headed Flying Foxes, Australia. Emerg Infect Dis 28 , 1043-1047 (2022). https://doi.org/10.3201/eid2805.212338 Annand, E. J. et al. Novel Hendra Virus Variant Detected by Sentinel Surveillance of Horses in Australia. Emerg Infect Dis 28 , 693-704 (2022). https://doi.org/10.3201/eid2803.211245 Edson, D. et al. Routes of Hendra virus excretion in naturally-infected flying-foxes: implications for viral transmission and spillover risk. PloS one 10 , e0140670 (2015). Smith, I. et al. Identifying Hendra virus diversity in pteropid bats. PLoS one 6 , e25275 (2011). Halpin, K., Young, P. L., Field, H. & Mackenzie, J. Isolation of Hendra virus from pteropid bats: a natural reservoir of Hendra virus. Journal of General Virology 81 , 1927-1932 (2000). Plowright, R. K. et al. Urban habituation, ecological connectivity and epidemic dampening: the emergence of Hendra virus from flying foxes (Pteropus spp.). Proceedings of the Royal Society B: Biological Sciences 278 , 3703-3712 (2011). Eby, P. Seasonal movements of grey-headed flying-foxes, Pteropus poliocephalus (Chiroptera: Pteropodidae), from two maternity camps in northern New South Wales. Wildlife Research 18 , 547-559 (1991). Roberts, B. J., Catterall, C. P., Eby, P. & Kanowski, J. Long-distance and frequent movements of the flying-fox Pteropus poliocephalus: implications for management. (2012). Mo, M. et al. Congregations of a threatened species: mitigating impacts from Grey-headed Flying-fox Pteropus poliocephalus camps on the Batemans Bay community. Australian Zoologist 41 , 124-138 (2020). https://doi.org/10.7882/az.2020.021 Plowright, R. K. et al. Ecological dynamics of emerging bat virus spillover. Proc Biol Sci 282 , 20142124 (2015). https://doi.org/10.1098/rspb.2014.2124 Eby, P., Plowright, R. K., McCallum, H. & Peel, A. J. Conditions predict heightened Hendra virus spillover risk in horses this winter: actions now can change outcomes. Aust Vet J 98 , 270-271 (2020). https://doi.org/10.1111/avj.12964 Becker, D. J., Eby, P., Madden, W., Peel, A. J. & Plowright, R. K. Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts. Ecology Letters 26 , 23-36 (2023). https://doi.org/https://doi.org/10.1111/ele.14007 Lunn, T. J. et al. Periodic shifts in viral load increase risk of spillover from bats. bioRxiv , 2023.2009.2006.556454 (2023). https://doi.org/10.1101/2023.09.06.556454 Burroughs, A. L. et al. Hendra Virus Infection Dynamics in the Grey-Headed Flying Fox (Pteropus poliocephalus) at the Southern-Most Extent of Its Range: Further Evidence This Species Does Not Readily Transmit the Virus to Horses. PLoS One 11 , e0155252 (2016). https://doi.org/10.1371/journal.pone.0155252 Smith, I. et al. Identifying Hendra virus diversity in pteropid bats. PLoS One 6 , e25275 (2011). https://doi.org/10.1371/journal.pone.0025275 Grenfell, B. T. et al. Unifying the Epidemiological and Evolutionary Dynamics of Pathogens. Science 303 , 327-332 (2004). https://doi.org/doi:10.1126/science.1090727 Lo Presti, A. et al. Origin and evolution of Nipah virus. J Med Virol 88 , 380-388 (2016). https://doi.org/10.1002/jmv.24345 Cortes-Azuero, O. et al. The genetic diversity of Nipah virus across spatial scales. J Infect Dis (2024). https://doi.org/10.1093/infdis/jiae221 Streicker, D. G., Fallas González, S. L., Luconi, G., Barrientos, R. G. & Leon, B. Phylodynamics reveals extinction–recolonization dynamics underpin apparently endemic vampire bat rabies in Costa Rica. Proceedings of the Royal Society B: Biological Sciences 286 , 20191527 (2019). https://doi.org/doi:10.1098/rspb.2019.1527 Hughes, G. J., Orciari, L. A. & Rupprecht, C. E. Evolutionary timescale of rabies virus adaptation to North American bats inferred from the substitution rate of the nucleoprotein gene. Journal of General Virology 86 , 1467-1474 (2005). Smith, J. S., Orciari, L. A. & Yager, P. A. in Seminars in virology. 387-400 (Elsevier). Streicker, D. G. et al. Host Phylogeny Constrains Cross-Species Emergence and Establishment of Rabies Virus in Bats. Science 329 , 676-679 (2010). https://doi.org/doi:10.1126/science.1188836 Velasco-Villa, A. et al. Molecular diversity of rabies viruses associated with bats in Mexico and other countries of the Americas. J Clin Microbiol 44 , 1697-1710 (2006). https://doi.org/10.1128/jcm.44.5.1697-1710.2006 Holmes, E. C., Woelk, C. H., Kassis, R. & Bourhy, H. Genetic constraints and the adaptive evolution of rabies virus in nature. Virology 292 , 247-257 (2002). Briese, T. et al. Virome Capture Sequencing Enables Sensitive Viral Diagnosis and Comprehensive Virome Analysis. mBio 6 , e01491-01415 (2015). https://doi.org/10.1128/mBio.01491-15 Quick, J. et al. Multiplex PCR method for MinION and Illumina sequencing of Zika and other virus genomes directly from clinical samples. Nature protocols 12 , 1261-1276 (2017). https://doi.org/10.1038/nprot.2017.066 Avanzato, V. A. et al. Case Study: Prolonged Infectious SARS-CoV-2 Shedding from an Asymptomatic Immunocompromised Individual with Cancer. Cell 183 , 1901-1912 e1909 (2020). https://doi.org/10.1016/j.cell.2020.10.049 Gordon, A. FASTX-Toolkit (RRID:SCR_005534) , ( Yinda, C. K. et al. A Novel Field-Deployable Method for Sequencing and Analyses of Henipavirus Genomes From Complex Samples on the MinION Platform. J Infect Dis 221 , S383-s388 (2020). https://doi.org/10.1093/infdis/jiz576 Wang, J. et al. A new Hendra virus genotype found in Australian flying foxes. Virology Journal 18 , 197 (2021). https://doi.org/10.1186/s12985-021-01652-7 Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol 30 , 772-780 (2013). https://doi.org/10.1093/molbev/mst010 Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A. & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods 14 , 587-589 (2017). https://doi.org/10.1038/nmeth.4285 Minh, B. Q. et al. IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era. Molecular Biology and Evolution 37 , 1530-1534 (2020). https://doi.org/10.1093/molbev/msaa015 Hoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: Improving the Ultrafast Bootstrap Approximation. Mol Biol Evol 35 , 518-522 (2018). https://doi.org/10.1093/molbev/msx281 Waterhouse, A. M., Procter, J. B., Martin, D. M., Clamp, M. & Barton, G. J. Jalview Version 2--a multiple sequence alignment editor and analysis workbench. Bioinformatics 25 , 1189-1191 (2009). https://doi.org/10.1093/bioinformatics/btp033 Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596 , 583-589 (2021). https://doi.org/10.1038/s41586-021-03819-2 Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. J Mol Graph 14 , 33-38, 27-38 (1996). https://doi.org/10.1016/0263-7855(96)00018-5 Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol Biol 7 , 214 (2007). https://doi.org/10.1186/1471-2148-7-214 Shapiro, B., Rambaut, A. & Drummond, A. J. Choosing appropriate substitution models for the phylogenetic analysis of protein-coding sequences. Mol Biol Evol 23 , 7-9 (2006). https://doi.org/10.1093/molbev/msj021 Minin, V. N., Bloomquist, E. W. & Suchard, M. A. Smooth skyride through a rough skyline: Bayesian coalescent-based inference of population dynamics. Mol Biol Evol 25 , 1459-1471 (2008). https://doi.org/10.1093/molbev/msn090 Weaver, S. et al. Datamonkey 2.0: A Modern Web Application for Characterizing Selective and Other Evolutionary Processes. Molecular Biology and Evolution 35 , 773-777 (2018). https://doi.org/10.1093/molbev/msx335 R: A language and Environment for Statistical computing (R Foundation for Statistical Computing, 2023). Bowden, T. A. et al. Structural basis of Nipah and Hendra virus attachment to their cell-surface receptor ephrin-B2. Nat Struct Mol Biol 15 , 567-572 (2008). https://doi.org/10.1038/nsmb.1435 Bowden, T. A., Crispin, M., Harvey, D. J., Jones, E. Y. & Stuart, D. I. Dimeric architecture of the Hendra virus attachment glycoprotein: evidence for a conserved mode of assembly. J Virol 84 , 6208-6217 (2010). https://doi.org/10.1128/jvi.00317-10 Wang, Z. et al. Potent monoclonal antibody-mediated neutralization of a divergent Hendra virus variant. Proc Natl Acad Sci U S A 119 , e2122769119 (2022). https://doi.org/10.1073/pnas.2122769119 Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryinformationV8.docx Spatio-temporal dynamics of Hendra virus in Pteropus bats in Australia reveals high evolutionary diversity linked with spillover Cite Share Download PDF Status: Published Journal Publication published 07 Apr, 2026 Read the published version in Nature Microbiology → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6263655","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":440566546,"identity":"c0e49985-5825-46a7-840f-65446e3a83c8","order_by":0,"name":"Vincent Munster","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYDACZjS+HAMzD1FaDCCcAwwMxoS1MKBpSWxgIKDF4DjvwQ8fd/xh0J2Re/jzh4p76dvZeQ8wfqk4jFvLYb5kyZlnDBjMbuSlSRw4U5y7s5kvgVnmDG4tks08Zsy8bSAtOWYMB9sScjcc5jFglmxLw6/lL0SL8YeD/xLSDQhp4WcGamGEaDGQONiQkADSwvixzQafFmPJ3jZjHrMzb8wkzhxLMAQ57DDDGdxa2PjPGH742SYnZ3Yc6LCKmgR5g/NnDB/+qJDAqQUGUOPiMHGxiQwYf5CsZRSMglEwCoYxAACc6k4ILOJRtAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-2288-3196","institution":"National Institute of Allergy and Infectious Diseases","correspondingAuthor":true,"prefix":"","firstName":"Vincent","middleName":"","lastName":"Munster","suffix":""},{"id":440566547,"identity":"ab7a0f2b-ff0b-4e76-b806-fce8b99ac396","order_by":1,"name":"Claude Kwe Yinda","email":"","orcid":"","institution":"National Institute of Allergy and Infectious Diseases","correspondingAuthor":false,"prefix":"","firstName":"Claude","middleName":"Kwe","lastName":"Yinda","suffix":""},{"id":440566548,"identity":"c3a8ea13-a9c2-4e97-a4c8-d6064344d4c1","order_by":2,"name":"John-Sebastian Eden","email":"","orcid":"https://orcid.org/0000-0003-1374-3551","institution":"University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"John-Sebastian","middleName":"","lastName":"Eden","suffix":""},{"id":440566549,"identity":"68cc7215-12f6-4595-b793-8403ef264d5f","order_by":3,"name":"Erica Prates","email":"","orcid":"","institution":"Oak Ridge National Laboratory,","correspondingAuthor":false,"prefix":"","firstName":"Erica","middleName":"","lastName":"Prates","suffix":""},{"id":440566550,"identity":"6b8f292b-aaa1-452f-a53c-a0da78c9a5d1","order_by":4,"name":"Anna Vlot","email":"","orcid":"https://orcid.org/0000-0001-9538-987X","institution":"Oak Ridge National Laboratory,","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Vlot","suffix":""},{"id":440566551,"identity":"04d6a056-28aa-415e-aaff-20580020ca30","order_by":5,"name":"Sarah Anzick","email":"","orcid":"","institution":"National Institute of Allergy and Infectious Diseases","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Anzick","suffix":""},{"id":440566552,"identity":"1e492bca-ac09-49ad-885d-f23465a175b8","order_by":6,"name":"Jianning Wang","email":"","orcid":"","institution":"Australian Centre for Disease Preparedness, Commonwealth Scientific and Industrial Research Organisation (CSIRO)","correspondingAuthor":false,"prefix":"","firstName":"Jianning","middleName":"","lastName":"Wang","suffix":""},{"id":440566553,"identity":"5c1c37c3-cf12-4fc4-909e-4012d6e5a495","order_by":7,"name":"Kim Halpin","email":"","orcid":"","institution":"Australian Centre for Disease Preparedness, Commonwealth Scientific and Industrial Research Organisation (CSIRO)","correspondingAuthor":false,"prefix":"","firstName":"Kim","middleName":"","lastName":"Halpin","suffix":""},{"id":440566554,"identity":"d5c164f3-a8d7-4e19-a231-805b0d576c43","order_by":8,"name":"Benny Borremans","email":"","orcid":"","institution":"Wildlife Health Ecology Research","correspondingAuthor":false,"prefix":"","firstName":"Benny","middleName":"","lastName":"Borremans","suffix":""},{"id":440566555,"identity":"a198bbd4-5692-48a4-8062-8eacbf17ffb4","order_by":9,"name":"Tamika Lunn","email":"","orcid":"","institution":"Center for the Ecology of Infectious Diseases, University of Georgia","correspondingAuthor":false,"prefix":"","firstName":"Tamika","middleName":"","lastName":"Lunn","suffix":""},{"id":440566556,"identity":"d058d2cd-438f-49c2-9aa0-f9f5e03f1794","order_by":10,"name":"Kent Barbian","email":"","orcid":"https://orcid.org/0000-0001-9861-6150","institution":"National Institute of Allergy and Infectious Diseases","correspondingAuthor":false,"prefix":"","firstName":"Kent","middleName":"","lastName":"Barbian","suffix":""},{"id":440566557,"identity":"bde4c3aa-c85b-4b89-9bb6-f4b1bba80d1c","order_by":11,"name":"Ben Greene","email":"","orcid":"","institution":"National Institute of Allergy and Infectious Diseases","correspondingAuthor":false,"prefix":"","firstName":"Ben","middleName":"","lastName":"Greene","suffix":""},{"id":440566558,"identity":"c6dc93db-181c-42b7-b349-0e4ab00e5295","order_by":12,"name":"Kimberly Meade-White","email":"","orcid":"","institution":"NIAID/NIH","correspondingAuthor":false,"prefix":"","firstName":"Kimberly","middleName":"","lastName":"Meade-White","suffix":""},{"id":440566559,"identity":"4b9a6b1f-8548-46a7-8f02-96a68118e7dd","order_by":13,"name":"Trenton Bushmaker","email":"","orcid":"https://orcid.org/0000-0002-2161-4808","institution":"National Institute of Allergy and Infectious Diseases","correspondingAuthor":false,"prefix":"","firstName":"Trenton","middleName":"","lastName":"Bushmaker","suffix":""},{"id":440566560,"identity":"b61ef17b-dbfa-4dcd-9928-9afba29f0b46","order_by":14,"name":"Caylee Falvo","email":"","orcid":"","institution":"Cornell University","correspondingAuthor":false,"prefix":"","firstName":"Caylee","middleName":"","lastName":"Falvo","suffix":""},{"id":440566561,"identity":"79266060-6a1c-475b-aa56-c000e678540c","order_by":15,"name":"Daniel Crowley","email":"","orcid":"","institution":"Cornell University","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Crowley","suffix":""},{"id":440566562,"identity":"5f801de6-645c-4705-ab88-e2f6b3a91944","order_by":16,"name":"Devin Jones-Slobodian","email":"","orcid":"","institution":"Montana state University","correspondingAuthor":false,"prefix":"","firstName":"Devin","middleName":"","lastName":"Jones-Slobodian","suffix":""},{"id":440566563,"identity":"e2b2bc2b-e3cb-4186-8b17-4ada3a5e013f","order_by":17,"name":"Manesh Shah","email":"","orcid":"","institution":"Oak Ridge National Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Manesh","middleName":"","lastName":"Shah","suffix":""},{"id":440566564,"identity":"a17a12a2-41e6-4e25-b4db-47313845a64d","order_by":18,"name":"Mirko Pavicic","email":"","orcid":"","institution":"Oak Ridge National Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Mirko","middleName":"","lastName":"Pavicic","suffix":""},{"id":440566565,"identity":"0e2d9dcb-26aa-4e6c-8fcb-ab019392200b","order_by":19,"name":"William Carr","email":"","orcid":"","institution":"Oak Ridge National Laboratory","correspondingAuthor":false,"prefix":"","firstName":"William","middleName":"","lastName":"Carr","suffix":""},{"id":440566566,"identity":"ded3bcbc-0b4a-4f25-bc58-15178abd773d","order_by":20,"name":"Craig Martens","email":"","orcid":"","institution":"National Institutes of Health","correspondingAuthor":false,"prefix":"","firstName":"Craig","middleName":"","lastName":"Martens","suffix":""},{"id":440566567,"identity":"23a86cbb-4b85-42f7-8d77-aac77c8872a9","order_by":21,"name":"Daniel Jacobson","email":"","orcid":"https://orcid.org/0000-0002-9822-8251","institution":"Oak Ridge National Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Jacobson","suffix":""},{"id":440566568,"identity":"1ca5a307-84d9-49c9-bacf-eea2c081138e","order_by":22,"name":"Raina Plowright","email":"","orcid":"","institution":"Cornell University","correspondingAuthor":false,"prefix":"","firstName":"Raina","middleName":"","lastName":"Plowright","suffix":""},{"id":440566569,"identity":"b11e6b59-4395-4a15-b991-57f4f36e6ecb","order_by":23,"name":"Alison Peel","email":"","orcid":"https://orcid.org/0000-0003-3538-3550","institution":"University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"Alison","middleName":"","lastName":"Peel","suffix":""}],"badges":[],"createdAt":"2025-03-19 17:31:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6263655/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6263655/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41564-025-02254-7","type":"published","date":"2026-04-07T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80302596,"identity":"80ba720d-d11f-4dc9-b20b-038b40beb6d2","added_by":"auto","created_at":"2025-04-10 09:36:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":280330,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeV-g1 phylogeny and clades circulating in Australia between 1994 and 2020\u003c/strong\u003e. \u003cstrong\u003e(a)\u003c/strong\u003e Phylogenetic analysis of HeV full genome sequences from bats, horses, and humans. Of the 73 genomes used in the phylogeny, 57 were newly generated in this study. The phylogenetic tree was inferred using the maximum likelihood method implemented in IQ-TREE v2.2.0. The best-fit substitution model was selected automatically by ModelFinder, and branch support values were calculated using 1,000 ultrafast bootstrap replicates. The scale bar is proportional to the number of nucleotide substitutions per site. Tip colors indicate the host species from which each sequence was obtained, branch annotations show clade-defining sets of mutations (colored according to the affected viral protein), and background shading denotes the distinct circulating clades. \u003cstrong\u003e(b) \u003c/strong\u003eNumber of HeV genomes from each clade detected in Australia in each year from 1994 to 2023 noting uneven sampling effort over time.\u003cstrong\u003e (c) \u003c/strong\u003eTiming of HeV genome detections in flying foxes from 2017-2020, colored by clade (A: yellow circle, B: pink triangle, C: blue square, D: purple triangle. Sampling years, months and dates are presented at the top of the panel, with months of peak HeV shedding shaded (dark blue: winter months (June July, August); light blue: adjacent months (May and September)). Vertical alignment of symbols represents their position within the HeV phylogeny, and horizontal alignment represents timing of sampling. Genomes that are joined with a vertical grey line were detected within the same sampling session. \u003cstrong\u003e(d)\u003c/strong\u003e Boxes show the diversity and frequency of genomes detected within each shedding pulse (as per \u003csup\u003e5\u003c/sup\u003e), with the black line showing a stylized representation of HeV prevalence within the broader sample set across the four years, using the Ct threshold we used for sequencing, Ct ≤ 32.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6263655/v1/eef8e229b0aa2e0d3e58c596.png"},{"id":80302597,"identity":"803352b0-4505-4c78-addb-4f8ae4aefaf1","added_by":"auto","created_at":"2025-04-10 09:36:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73355,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial distribution of HeV-g1 clades in Subtropical Australia\u003c/strong\u003e. The cumulative total distribution of the identified clades at specific roost. The size of the pie is proportional to the amount of full genomes sequences recovered from each roost site. Yellow = clade A, purple = clade B, green = clade C, blue = clade D, grey = undefined.\u003cstrong\u003e \u003c/strong\u003eMaps were created in R version 4.3.0 \u003csup\u003e57\u003c/sup\u003e. QLD = Queensland, NSW = New South Wales.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6263655/v1/2104d2d0238fd0b575b41291.png"},{"id":80302598,"identity":"db224eff-c723-4951-8bcd-be24ad69c533","added_by":"auto","created_at":"2025-04-10 09:36:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":215418,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBayesian phylogenetic inference of HeV-g1\u003c/strong\u003e. Bayesian tree was constructed using a relaxed clock and skygrid analysis models. Tip colors represent sample location and horizontal node bar indicate 95% highest posterior density (HPD) intervals. Node shapes (diamond) represent the Bayesian posterior distribution. HeV-g1 clades circulating in Australia between 1994 and 2020 are indicated\u003cstrong\u003e. \u003c/strong\u003eMap shows sampling location (red = North and Central Queensland, green = South-East Queensland, blue = North New South Wales, darker colors indicate sampling locations close to the border between Queensland and New South Wales. Maps were created in R version 4.3.0 \u003csup\u003e57\u003c/sup\u003e. QLD = Queensland, NSW = New South Wales.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6263655/v1/04aee6480abba98c0db548e3.png"},{"id":80302603,"identity":"c3d42d49-f313-4f31-adf5-0f3c7abb122e","added_by":"auto","created_at":"2025-04-10 09:36:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":297670,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructure prediction and potential effect of clade defining mutations in the G glycoprotein (a) \u003c/strong\u003eMutations in the head domain of HeV G glycoprotein are depicted as van der Waals spheres (PDB ID: 2VSK) \u003csup\u003e58\u003c/sup\u003e. The mutation A336T that defines clades C, D, and part of undefined sequences is highlighted in bold. The human ephrin-B2 receptor is represented as blue ribbons. The position of the neighboring protomer is indicated as a mauve surface (PDB ID: 2X9M) \u003csup\u003e59\u003c/sup\u003e. Most substitutions in G do not seem to affect the interaction with the host receptor or the dimer interfacial contact. The attached glycans are depicted using PDB ID: 7SYY \u003csup\u003e60\u003c/sup\u003e. \u003cstrong\u003e(b)\u003c/strong\u003e Close view of the interactions formed with R248 in the core of the G head domain. Hydrogen bonds are formed with S232 and I279, and a salt bridge with D219. The substitution of the arginine by a glycine at site 248 may disrupt the local secondary structure as well as these strong dipole-dipole/electrostatic interactions. \u003cstrong\u003e(c) \u003c/strong\u003eClose view of the interactions with the host receptor that may be indirectly affected by the substitution at 248. The disulfide bridge C216-C240 may help to stabilize the local structure\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6263655/v1/6160a237799bfcdac8c77df4.png"},{"id":106389892,"identity":"30615b40-a76b-4047-a1c6-16b983b0d3e9","added_by":"auto","created_at":"2026-04-08 07:06:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1942336,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6263655/v1/9e430c5c-6fd2-4e82-9e49-3cc16614ab08.pdf"},{"id":80302608,"identity":"3096d736-0e6e-43db-a573-c77bfc39c7c6","added_by":"auto","created_at":"2025-04-10 09:36:33","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4661774,"visible":true,"origin":"","legend":"Spatio-temporal dynamics of Hendra virus in Pteropus bats in Australia reveals high evolutionary diversity linked with spillover","description":"","filename":"SupplementaryinformationV8.docx","url":"https://assets-eu.researchsquare.com/files/rs-6263655/v1/1efa7377dbfeb5c92b52779f.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Spatio-temporal dynamics of Hendra virus in Pteropus bats in Australia reveals high evolutionary diversity linked with spillover","fulltext":[{"header":"Significance Statement","content":"\u003cp\u003eLimited genomic data has constrained our understanding of the genetic diversity and transmission dynamics of Hendra virus (HeV). By expanding the number of available HeV genomes from 15 to 73, this study provides a more detailed and comprehensive view of the virus\u0026rsquo;s genetic variability. The discovery of four distinct and cryptic clades, alongside the absence of spatiotemporal structuring within bat populations, indicates that HeV persistence and spillover are not driven by localized outbreaks or immune-driven strain evolution. The results demonstrate the significant genetic diversity of HeV maintained in bats and lay the foundation for ecologically informed disease surveillance, with broader implications for reducing zoonotic disease emergence and managing public health risks associated with other bat-borne viruses.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eBat populations can host diverse communities of viruses, ranging from those of little or unknown significance to highly fatal zoonotic pathogens \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Together, targeted surveillance programs and unbiased viral discovery efforts have highlighted the breadth of viral species, genera and families hosted by bats, yet we have limited understanding of how diversity is structured within individual viral species across time and space \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Understanding these evolutionary patterns could provide key insights into how viruses are maintained in bat populations and which factors drive their emergence in new hosts \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. As a WHO priority pathogen and with decades of multidisciplinary research, Hendra virus (HeV, genus \u003cem\u003eHenipavirus\u003c/em\u003e) provides an ideal system for addressing this gap \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. HeV is a non-segmented, negative-strand RNA virus with an 18kb genome encoding six proteins: nucleocapsid, phosphoprotein, matrix protein, fusion glycoprotein, attachment glycoprotein, and large polymerase. The virus circulates endemically in Australian flying foxes (\u003cem\u003ePteropus\u003c/em\u003e spp), with high case fatality rates when it spills over into horses and humans (75% and 57%, respectively) \u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. In the 30 years since it emerged during a disease outbreak in the Brisbane suburb of Hendra, Australia \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, 67 spillover events have been documented \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. This includes two spillovers of a novel genotype (HeV-g2) that is sufficiently different from the original genotype (HeV-g1) to evade routine diagnostic assays until it was discovered by sentinel surveillance in 2021 (89% nucleotide and 92.5% amino acid identity) \u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAustralian flying foxes (\u003cem\u003ePteropus\u003c/em\u003e spp) are natural reservoir hosts of HeV, with serological assays, virus isolation and PCR indicating that HeV circulates among all four flying fox species in continental Australia: the black flying fox (\u003cem\u003eP. alecto\u003c/em\u003e), the spectacled flying fox (\u003cem\u003eP. conspicillatus\u003c/em\u003e), the little red flying fox (\u003cem\u003eP. scapulatus\u003c/em\u003e), and the grey-headed flying fox (\u003cem\u003eP. poliocephalus\u003c/em\u003e) \u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. These bats are nomadic and nocturnal foragers, moving large distances between roost sites \u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e that comprise hundreds to hundreds of thousands of individuals \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The factors contributing to HeV shedding in flying foxes and spillover dynamics into horses have been extensively studied \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, elucidating the mechanistic links between habitat loss, climatic variations, and the heightened risk of HeV spillover in Australia. However, the evolutionary dynamics of HeV remain under-explored.\u003c/p\u003e \u003cp\u003eThe passing of nearly three decades between the discovery of HeV and the identification of a second genotype underscore the gaps in understanding its genetic diversity \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. To date, HeV genomic data remain sparse, with the majority consisting of short, incomplete genomic sequences \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Most of these existing genomes were obtained from horses (n\u0026thinsp;=\u0026thinsp;9), with few from the natural reservoir (n\u0026thinsp;=\u0026thinsp;4). This limited genomic information has hindered the ability to describe HeV diversity and explore evolutionary patterns of the virus in the reservoir host. A deeper investigation into the genetic variability of HeV across different temporal and geographical scales is crucial to understanding the virus's evolution and dynamics within its natural reservoir, including how new viral strains with altered host tropism or transmission potential emerge. Such knowledge is essential for refining predictive models of spillover events and for the development of targeted strategies to mitigate the public health risks associated with HeV. Here we describe the results of a large, genome-scale phylodynamic analysis of HeV-g1 strains in Australia. By generating and analyzing a data set of 73 whole-genome sequences over a time span of 26 years, we define circulating clades of HeV in Australian fruit bat populations and infer their temporal and spatial evolutionary dynamics. This study provides crucial insights into the evolutionary and ecological dynamics of HeV in Australian fruit bat populations, with broader implications for predicting and mitigating zoonotic disease emergence.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHeV-g1 whole genome sequencing\u003c/h2\u003e \u003cp\u003eOf 9869 flying fox urine samples screened by PCR from 24 roosts (Supplementary (Suppl.) Table\u0026nbsp;1, Suppl. Figure\u0026nbsp;1), 703 tested positive for HeV-g1 with Ct values\u0026thinsp;\u0026le;\u0026thinsp;40, and 253 of these had Ct values\u0026thinsp;\u0026le;\u0026thinsp;32, making them suitable for sequencing. Overall, the success of HeV isolation from PCR-positive bat samples was low (3/177; 1.7%), with full genome sequences obtained from the three samples that generated isolates (Ct values of 24, 35, and 37). In total, we generated 48 HeV-g1 full genome sequences (6.8.0% success) with a mean coverage depth of 415\u0026times; (range, 8.0\u0026times; to 3,700 \u0026times;) from bat urine samples and the cycle threshold value (Ct) of these samples ranged between 21 to 32 (7.4 x 10\u003csup\u003e6\u003c/sup\u003e \u0026minus;\u0026thinsp;8.3 x 10\u003csup\u003e3\u003c/sup\u003e genome copies/mL) (Suppl. Table\u0026nbsp;2). This included 5 genomes from \u003cem\u003eP. alecto\u003c/em\u003e individuals and 43 genomes from under-roost sampling sheets, sampled from roosts spanning 595km along the coastline of eastern Australia (~\u0026thinsp;12,000 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e). Host genomic sequences detected within the sequencing libraries confirmed \u003cem\u003eP. alecto\u003c/em\u003e as the source of these samples. We also generated 10 novel HeV sequences from horses and obtained 15 published sequences from NCBI GenBank (Suppl. Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eWithin the combined dataset of 73 HeV-g1 sequences, genetic variation across the genome was relatively low with dips in identity mostly restricted to the intergenic regions: the mean nucleotide identity was 96.9% (range, 47 to 100%) with the lowest values observed at the 3\u0026rsquo;-end (Suppl. Figure\u0026nbsp;2). Base composition was mostly stable across the genome, with a mean GC content of 39.8%. The exception was a marked decrease in GC content at the intergenic regions and at the 5\u0026rsquo; and 3\u0026prime; end of the genome, corresponding to an AT-rich untranslated region.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePhylogenetics and circulating diversity of HeV-g1 in Australian\u003c/b\u003e \u003cb\u003ePteropus\u003c/b\u003e \u003cb\u003espp.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe phylogenetic analysis of all 73 HeV-g1 genome sequences from humans, horses and fruit bats revealed four well-sampled monophyletic clades of bat-derived sequences tentatively named clades A, B, C, and D (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Suppl. Table\u0026nbsp;3). All four clades also included HeV-g1 sequences detected in horses (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, Suppl. Figure\u0026nbsp;3\u0026ndash;4). A few cryptic sequences not belonging to clades A-D were detected in horses and humans between 1994 and 2008, during a period when limited bat sampling was conducted (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, Suppl. Figure\u0026nbsp;5). The mean inter- and intra-clade distance across the genome was \u0026lt;\u0026thinsp;99.5% and \u0026lt;\u0026thinsp;99.6%, respectively. We found that clade- and sub-clade defining mutations rarely occur in the G glycoprotein (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and Supp. Table\u0026nbsp;3), suggesting that HeV\u0026rsquo;s adaptation to its host entry receptors, ephrin B2 and ephrin B3, remains stable. There was limited evidence for adaptive evolution in the datasets, based on the absence of strong positive selection signals, across the viral genomes (Suppl. Table\u0026nbsp;4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition to generating complete genomes, we sequenced 13 additional G glycoprotein sequences and retrieved 6 more G glycoprotein sequences from GenBank (Suppl. Table\u0026nbsp;2). To verify the consistency of the phylogenetic topology, we used both the coding sequences and a more comprehensively sampled G glycoprotein gene dataset to construct the HeV-g1 phylogeny (Suppl. Figure\u0026nbsp;6). The trees constructed using the G glycoprotein sequences showed the same topology and clustering patterns as the complete genome tree.\u003c/p\u003e \u003cp\u003eSequences belonging to the four main clades co-circulated in time and space in the bat population. Each clade comprised sequences from flying fox roosts in both New South Wales and Queensland (Suppl. Figure\u0026nbsp;3\u0026ndash;4) and at least three of the four clades co-circulated in each year of our intensive field sampling (2017\u0026ndash;2020) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). The largest number of genomes were recovered in 2017 and 2018 between June and September, corresponding to the peak of HeV shedding within the natural reservoir during the Australian winter months (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed, Suppl. Figure\u0026nbsp;5) \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Clade A is made up of eight novel bat strains and two horse strains (from the 2009 and 2014 spillover events, both in Queensland). While the 2014 horse sequence (HORSE003/Horse/QLD/South_Kolan/2014-03-17) forms a monophyletic clade with the bat sequences, the 2009 strain is distantly related with a percentage nucleotide identity of 99.3\u0026ndash;99.8% to the rest of the clade. Clade B grouped into two subclades. One subclade contains just one strain, collected in 2018 from New South Wales, that is 99.5\u0026ndash;99.7% identical to the rest of the clade. The rest of the clade shares a nucleotide identity of 99.8\u0026ndash;100%, comprising ten bat strains and one horse strain arising from a 2023 HeV spillover in New South Wales. Similarly, clade C is made up of 10 strains, including nine bat strains and one spillover horse strain (HORSE009/Horse/NSW/South_Arm/2013-06-06), all identified in this study, and sharing a nucleotide identity of 99.6\u0026ndash;99.7%. Finally, clade D is the largest and contains a monophyletic group of four previously published bat strains from bats sampled in 2009 with three sharing a nucleotide percentage identity of 100%, from bats sampled in 2009 \u003csup\u003e30\u003c/sup\u003e. This clade also comprises four strains from spillover events in horses, including two in Queensland (HORSE002/Horse/QLD/Tamborine_Mt/2017-05-25 from May 2017 and HORSE004/Horse/QLD/Currumbin_Valley/2011-08-22 from August 2011) and two in New South Wales (HORSE001/Horse/NSW/Clunes/2015-09-03 from September 2015 and HORSE007/Horse/NSW/Pimlico/2011-08-15 from August 2011). Importantly, we did not identify any examples where we could link a spillover strain directly to the sampled bat diversity (both temporally and by sequence identity); however, only 20 of 89 known horse spillovers had sequences available for inclusion within this phylogeny.\u003c/p\u003e \u003cp\u003eOverall, the HeV-g1 phylogeny showed a spatially diffuse structure with a high diversity of strains maintained in the bat population. Co-circulation of multiple strains within and across clades was detected within the same roosts at the same time and over multiple consecutive years (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Within each clade, detections spanned 500km, 650km, 172km and 1535km for clades A to D, respectively (or 380km and 172km for clades A and B, respectively, if only bat sequences are considered). Amongst roosts from which new genomes were recovered, we detected an average of 2.2 clades per roost. The highest number of genomes were obtained from samples collected in Toowoomba (17 genomes: as 2:4:2:9 for clades A:B:C:D, respectively), Clunes (12 genomes: 4:3:0:5), and Redcliffe roosts (5 genomes: 1:1:2:1) (Suppl. Table\u0026nbsp;2). Three genomes were recovered from Sunnybank (clades A, D and D) and Nambucca Heads (A, C and C) roosts, two from Lismore (C, D) and Simpsons Creek (C, C) roosts, and one from each of Mount Ommaney (D), Canungra (A), Currumbin (B), and Hervey Bay (D) roosts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTemporal analysis of HeV-g1\u003c/h3\u003e\n\u003cp\u003eHeV-g1 sequences demonstrated strong temporal signal, indicative of clock-like evolution (Suppl. Figure\u0026nbsp;7). Linear regression revealed a strong correlation between root-to-tip genetic distance against time of sampling for complete genome and coding sequences (correlation coefficients: 0.78 and 0.74, respectively). While G glycoprotein showed a weaker temporal signal (correlation coefficient: 0.53), this may be due to reduced phylogenetic signal in this region.\u003c/p\u003e \u003cp\u003eWe next used a Bayesian Markov chain Monte Carlo (MCMC) approach to more rigorously assess the evolutionary dynamics of HeV. Based on the best fit model (Suppl. Table\u0026nbsp;5), the coding sequences showed a refined estimate for the mean rate of evolution of 1.43 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e (95% HPD, 1.07 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e to 1.80 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) subs/site/year. The time to the most recent common ancestor (TMRCA) estimate was 1975 (95% HPD, 1954.80 to 1988.40) across the whole tree. It was estimated that clades A and B have been circulating for 24\u0026ndash;25 years (since 1995\u0026ndash;1996) while clade C and D have been circulating for 15 years (since 2005) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Noticeably, the evolutionary history of clades A and B is under-sampled (reflected by the ~\u0026thinsp;15 years gap between the timing of the divergence of these clades and the sampling of representative sequences), whereas clades C and D have been more continuously sampled since their divergence (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Some spillover sequences emerged before 1994 (with branch lengths representing\u0026thinsp;\u0026gt;\u0026thinsp;20years of evolution before detection) or around the same time as clade C and D (~\u0026thinsp;2002\u0026ndash;2005); however, we did not detect these in the reservoir population.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDue to under-sampling in flying foxes prior to the mid 2000\u0026rsquo;s, a large proportion of horse- and human-derived strains remain unclassified and most more recently detected strains in bats erroneously appear to have evolved from horse- and human-derived strains. A notable exception here can be found in clade D, where we find a more recent (August 27, 2018) bat strain that clusters with two horse-derived strains identified on 15 and 22 August 2011 (Suppl. Figure\u0026nbsp;2). However, permutation analyses supported the recent emergence of clade B, indicating that the likelihood of clade B being present but not observed before 2018 was 0.02 (details in Suppl. Results and Suppl. Figure\u0026nbsp;8).\u003c/p\u003e\n\u003ch3\u003eStructure prediction and potential effect of clade defining mutations\u003c/h3\u003e\n\u003cp\u003eWe used experimentally determined or predicted structures of these proteins for a preliminary discussion of their potential functional effects, particularly focusing on the mutations that define the most recently emerged clade, clade B (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, details in Suppl. Discussion). The predicted model indicates that the two identified LD mutations in the G glycoprotein, S175T and A336T, are distant from the host protein binding surface and likely neutral (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Consequently, despite these substitutions, the key interaction sites with the host receptor ephrin-B2 and the dimer interfacial contact regions remain largely unaffected. In contrast, LD mutations across the P/V/W and L proteins may have functional implications, particularly when they co-occur (Suppl. Figure\u0026nbsp;9\u0026ndash;11, Suppl. Discussion).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eUnderstanding how viral populations are structured and maintained within bat hosts over time and space has been hindered by a lack of comprehensive genomic data, despite extensive sampling efforts in bat populations. This knowledge gap, in turn, limited our ability to predict and mitigate spillover risk of zoonotic viruses like HeV, which remains a threat to veterinary and public health Australia \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Our study addresses this gap by employing extensive spatiotemporal sampling and viral enrichment sequencing. We screened 9,853 flying fox urine samples collected over nearly four years, identifying over 600 HeV-positives samples. Of these, 253 samples with Ct\u0026thinsp;\u0026lt;\u0026thinsp;32 were selected for sequencing, and through customized viral enrichment methods, we generated 48 new HeV genomes from bats. Together with an additional ten genomes from HeV-infected horses, this study increased the number of HeV genomes available by almost fivefold (from 15 to 73). Our analyses revealed the presence of four distinct HeV clades co-circulating in bats, with no distinct spatiotemporal structuring or evidence of immune-driven strain evolution, providing new insights into viral maintenance in bat populations.\u003c/p\u003e \u003cp\u003eTheoretical expectations about viral dynamics in populations suggest that, due to selective pressures like immune evasion and transmissibility, a single or set of viral strains should become dominant at any given time, leading to clear temporal and spatial clustering \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Over time, this process should reduce viral diversity through adaptive evolution, driven by factors such as immune pressure or environmental changes. In contrast to these expectations, our results show that HeV variants co-exist simultaneously across wide geographic areas, with no distinct temporal or spatial clustering. While long-distance movements between roosts likely facilitates the sharing of HeV strains within our study area (~\u0026thinsp;12,000km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e), immune pressure, if present, should shape the strains/variants present at any given time via adaptive evolution. The co-existence of four divergent clades in both time and space suggest that selective pressures are minimal or absent, and that adaptive evolution plays a limited role in shaping HeV diversity within these natural reservoirs.\u003c/p\u003e \u003cp\u003eWe previously hypothesized that phylogenetic signatures could distinguish between competing hypotheses of viral infection dynamics in bats \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Acute infections, cleared quickly, should exhibit low diversity within outbreaks (pulses) but high diversity between them, reflecting distinct viral lineages from single introductions. By contrast, persistent infections should show higher viral diversity within each pulse due to within-host evolution, but low diversity between pulses. At the time, the large-scale genomic surveillance data required to validate these hypotheses did not exist. The HeV genomic data presented here align with patterns expected under persistent infections, supporting the theory that bats can maintain HeV infections for extended periods. This suggests that intermittent shedding may be triggered by host or environmental factors.\u003c/p\u003e \u003cp\u003eThis stable co-existence of divergent HeV lineages within flying fox populations is somewhat consistent with observations in the closely related Nipah virus (NiV) but contrasts sharply with expectations from other systems where selective pressures lead to rapid turnover and spatial clustering of viral strains. For NiV, two distinct genogroups have been described (I and II), comprising four minor genotypes (IA, IB, IIA, and IIB), and a total of 15 genetic \u0026lsquo;clusters\u0026rsquo; \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Each genotype shows broad spatial structure across the Indomalayan region, with countries on the eastern (Indonesia and Malaysia) and western (India and Bangladesh) edges of the region having only one circulating genogroup currently identified, while central countries (Thailand and Cambodia) have sequences from both genogroups \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. At a finer genetic scale, NiV genetic clusters exhibit overlapping spatial structuring within the same country, and an average of 2.41 genetic clusters per roost (analogous to estimates of 2.2 clades per roost here) \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Some genetic clusters spanned\u0026thinsp;\u0026gt;\u0026thinsp;2100km, comparable to our observations. \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. By contrast, for bat-borne rabies virus, the phylogeny is structured by host species, with obvious temporal signal reflecting an evolutionary history of host shifts followed by predominantly within-species transmission \u003csup\u003e\u003cspan additionalcitationids=\"CR35 CR36\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Longitudinal sampling of vampire bats (\u003cem\u003eDesmodus rotundus\u003c/em\u003e) over 14 years identified limited spatial and temporal overlap of rabies virus clades, likely due to extinction-recolonization dynamics \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Bat rabies viruses in monophyletic clades also show a high level of amino acid conservation, suggesting that rabies virus diversity within reservoir species is highly constrained and evolving under a model of purifying selection \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLineage-defining (LD) mutations in P/V/W, L, or in the G glycoprotein, and the rare occurrence of these mutations in the G glycoprotein (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and Suppl. Table\u0026nbsp;3), amongst our dataset suggests that HeV adaptation to its host entry receptors, ephrin B2 and ephrin B3, remains stable and their highly conserved binding interface across mammalian species benefits the broad host tropism of HeV. The two identified LD mutations in the G glycoprotein, S175T and A336T, are distant from the host protein binding surface and likely neutral (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Conversely, the presence of several LD mutations across P/V/W and L proteins, and their combined occurrence, may indicate potential for epistatic interactions, warranting future investigation. Furthermore, we conducted a preliminary examination of the mutations defining the newly identified clade B and discussed the potential functional effects of these mutations in Suppl. material (Suppl. Figure\u0026nbsp;10, and Suppl. Figure\u0026nbsp;11, Suppl. discussion). For example, we speculated that the mutation P352S in P/V/W may not be functionally silent. Even though it is located in the long intrinsically disordered region, which is highly variable among paramyxoviruses, it is part of a short motif that is highly conserved between HeV and NiV (Suppl. Figure\u0026nbsp;9). Both viruses employ the V protein as an immune evasion strategy to evade STAT1/STAT4-dependent interferon responses. Our observations encourage a follow-up study to evaluate the impact of this substitution on the interaction with host proteins and phenotypic differences in clade B relative to the other clades.\u003c/p\u003e \u003cp\u003eAlongside the discovery of HeV-g2 \u003csup\u003e16,17\u003c/sup\u003e, our results suggests that the true diversity of HeV and related viruses in flying fox populations are incomplete, and intensified surveillance endeavors would likely uncover more divergent strains and variants. Most HeV-positive samples from this study had Ct values greater than 32 (\u0026lt;\u0026thinsp;3 x 10\u003csup\u003e4\u003c/sup\u003e copies/mL) indicative of a low viral load and making the recovery of full genomes challenging. This further emphasizes the need for large, longitudinal studies and viral enrichment sequencing to obtain sufficient genomes for analyses such as we present here. In addition, there exist historical gaps in the sequence available hindering a full understanding of the HeV evolution.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe characterized the genetic diversity of HeV-g1 and identified the circulating clades, observing that individual flying fox roosts host multiple strains. The absence of genotypic structuring within the bat population, combined with minimal evidence of immune-driven selection, implies that immune pressure is insufficient to drive the evolution of these strains. The stable maintenance of diverse viral clades over four consecutive years supports a model of long-term infections in individual bats, with episodic shedding, rather than epidemic waves with strain replacement. Finally, our detection of novel circulating HeV clades in bats indicates that we have yet to fully characterize circulating viral diversity and potentially distinct phenotypic traits, highlighting the importance of enhanced surveillance and in-depth research to fully understand the implications for spillover dynamics and public health.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Online Methods","content":"\u003ch2\u003eSample collection\u003c/h2\u003e\u003cp\u003eSample collection from flying foxes was covered under Griffith University Animal Ethics Committee Approval ENV/10/16/AEC and ENV/07/20/AEC, and is described in full in Peel, Yinda et al., 2022 \u003csup\u003e16\u003c/sup\u003e. Briefly, sampling was conducted at flying fox roosts in southeast Queensland and mid- to north-coast New South Wales between December 2016 and September 2020 (Suppl. Table. 1, Suppl. Figure\u0026nbsp;1). Urine samples were collected from both individual bats captured in mist nests at their roost site and from plastic sheets placed underneath roosting trees pre-dawn \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. For captured bats, urine samples were collected directly from the bat if it urinated while anaesthetized, or from a urine collection bag attached to its holding bag (the bottom third was lined with plastic). Information on species, sex, and age class (adult, subadult, juvenile) was also recorded. For samples collected from sheets, a single pooled urine sample was collected from each sheet, and the number and species of bats immediately above the sheet were recorded. Sheet placement was prioritized to target black flying-fox roosting locations, but other species were also present. For both sample types, urine was pipetted into a tube with AVL lysis buffer (Qiagen, target 140ul of urine into 560ul buffer), viral transport medium (VTM, between 200–1000µl of urine into 1000ul of VTM) or a plain cryovial. Samples were held on ice during collection, then transferred to a CryoShipper (\u0026lt; -80°C) for transport and stored at -80°C in the laboratory.\u003c/p\u003e\n\u003ch3\u003e\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cb\u003eRNA extraction and HeV screening of bat samples\u003c/b\u003e\u003c/div\u003e \u003cp\u003eRNA was extracted using the QIAamp Viral RNA Kit using a QIAcube HT automated system (Qiagen, USA) according to the manufacturer's instructions. RNA was eluted in 150\u0026micro;l of TE buffer and used for HeV screening using a qRT-PCR assay targeting the viral P gene (5'-CCCAACCAAGAAAGCAAGAG (forward), 5'-TTCATTCCTCGTGACAGCAC (reverse) and 5'-TTACTGCGGAGAATGTCCAACTGAGTG (probe)). Ten \u0026micro;l RNA was tested with a TaqMan Fast Virus 1-Step master mix on QuantStudio 6 Flex Real-Time PCR instrument (Applied Biosystems, USA) according to instructions of the manufacturer. Samples were deemed positive with genome detection at cycle threshold (Ct) value of \u0026le;\u0026thinsp;40. Spatiotemporal analyses of PCR results generated as part of the same study are described in Lunn et al \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Positive samples with Ct values\u0026thinsp;\u0026le;\u0026thinsp;32 were selected for further whole genome recovery.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eVirus isolation\u003c/h2\u003e \u003cp\u003eWhere positive samples had available sample aliquots in VTM (n\u0026thinsp;=\u0026thinsp;177), virus isolation was performed to enhance genome sequencing success. Vero E6 cells in a 24-well plate were inoculated with 250 \u0026micro;l original undiluted sample and a 1:10 dilution thereof in different wells. Diluted and undiluted samples were plated in duplicates. Plates were centrifuged for 30 minutes at 1000 rpm and incubated for 30 minutes at 37\u0026deg;C and 5% CO2. The inoculum was removed and replaced with 500 \u0026micro;l DMEM containing 2% FBS, 50 U/ml penicillin and 50 \u0026micro;g/ml streptomycin. Two and three days after inoculation, cytopathic effect (CPE) was scored. Blind passage was performed on samples without CPE as above; other than that, plates were not spun. Supernatants from plates with CPE present were analyzed via qRT-PCR for Hendra virus RNA to rule out other causes of CPE. RNA of positive samples was subjected to full genome recovery.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePartial and full genome recovery of bat HeV-g1\u003c/h2\u003e \u003cp\u003eSequencing libraries were prepared using either the Kapa HyperPrep for DNA library preparation kit or the Kapa RNA HyperPrep library preparation kit according to manufacturer\u0026rsquo;s protocols (Roche Sequencing Solutions, USA, details in Suppl. materials). Then target enrichment was performed as follows: Kapa libraries were quantified using the area under the curve on Bioanalyzer traces as well as UV spectrophotometry on the Nanodrop instrument (ThermoFisher Scientific, USA). Pre-capture sequencing libraries were normalized and then combined in 4- to 12-plex reactions for solution capture hybridization using version of VirCapSeq-VERT probe set, described by Briese \u003cem\u003eet al.\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e or custom myBaits probe library. For VirCapSeq-VERT probe set, Kapa libraires were enriched for virus following the SeqCap EZ HyperCap Workflow User\u0026rsquo;s Guide, version 2.3, while for custom myBaits probe library, the myBaits Hybridization Capture for Targeted NGS protocol, Version 4.01 was used.\u003c/p\u003e \u003cp\u003eAdditionally, PCR based amplicon-enrichment was performed on the samples. First, tiled primers were designed using primal scheme multiplex\u0026thinsp;\u0026lt;\u0026thinsp;FASTA\u0026thinsp;\u0026gt;\u0026thinsp;command line interface following existing methods \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Then, single-stranded cDNA was generated as described above and 2.5 \u0026micro;L was used as template \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e in a PCR assay using HeV-specific tiled primers that generated over-lapping 400 bp amplicons spanning the HeV genome. Even and odd multiplex PCR conditions/amplicon generation using Q5 HotStart Polymerase PCR (ThermoFishcer Scientific, USA) was optimized following the ARTIC nCoV-2019 protocol above with the following modifications: 2X primer concentration in each even/odd primer pools with 35 cycles at a 52\u003csup\u003e0\u003c/sup\u003eC/1-min annealing temperature and 72\u003csup\u003e0\u003c/sup\u003eC /30-sec extension. Products from each primer pool were combined, AmPure XP purified, and quantified with Qubit (ThermoFishcer Scientific, USA) fluorometric quantitation as per instructions, and library generation and sequencing were performed as outlined in the Suppl. Methods 2. After sequencing, adapters were removed from raw fastq files using cutadapt v 1.12 and reads were quality trimmed and filtered using the FAST-X toolkit \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Trimmed and filtered paired-end (PE) reads were processed through a custom viral and bacterial database screening and \u003cem\u003ede novo\u003c/em\u003e assembly pipeline to identify and assemble reads mapping Hendra virus. Details of the bioinformatic pipeline can be found in the Suppl. Methods 3.\u003c/p\u003e \u003cp\u003eLastly, we subjected some of the samples to long range PCR amplification of the attachment (G) glycoprotein. Single-stranded cDNA was generated as described above \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e and long range semi-nested PCR was performed on samples for which full genomes could not be recovered according to previously established protocol (Suppl. Table\u0026nbsp;6) \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Sequencing was performed as outlined in the Suppl. Methods 2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFull genome recovery of HeV-g1 from horses\u003c/h2\u003e \u003cp\u003eClinical specimens, blood, serum, and nasal swabs were also collected from horses with symptoms and submitted to the Australian Centre for Disease Preparedness (ACDP) for the diagnosis of HeV. Virus isolation was conducted at the Biosafety Level 4 laboratory at ACDP. The samples were passed on Vero cells (ATCC CCL-81) for three consecutive times of 7 days of each passage. The samples were then tested by real-time quantitative reverse transcription PCR (qRT-PCR) to confirm the presence of replicating HeV genome. Then full genome sequencing was performed as reported in Wang et al., 2021 \u003csup\u003e45\u003c/sup\u003e. Briefly, viral RNA was extracted by using MagMax 96 Viral RNA Kit (ThermoFisher Scientific, USA) in a MagMAX Express Magnetic Particle Processor (ThermoFisher Scientific, USA) following manufacturer\u0026rsquo;s instructions. TruSeq RNA library Prep Kit V2 (Illumina, USA) was utilized for DNA library preparation, according to manufacturer\u0026rsquo;s instructions. The purified libraries were quantified using a Qubit Fluorometer and an Agilent 2100 Bioanalyzer (Agilent Technologies, USA). The concentration of the final libraries was normalized and pooled in equimolar ratios. The library pool was then loaded into a MiSeq Reagent Kit V2 (2 x 150 cycles; Illumina, USA) and sequenced in a MiSeq platform (Illumina, USA) according to the manufacturer\u0026rsquo;s instructions. The NGS sequence data was analyzed using CLC Genomic Workbench 20 (Qiagen, USA) using standard parameters. The raw reads were quality-trimmed before assembly by both reference mapping (using HeV genomes available) and de novo assembly. The resultant sequences were confirmed as HeV-g1 by blasting against the NCBI nonredundant nucleotide database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://blast.ncbi.nlm.nih.gov/Blast\u003c/span\u003e\u003cspan address=\"https://blast.ncbi.nlm.nih.gov/Blast\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePhylogenetic and evolutionary analysis\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003ePhylogenetic analysis\u003c/h2\u003e \u003cp\u003eTo examine the genome-wide evolution of HeV-g1 in Australia, an alignment was prepared using 72 complete genome sequences with MAFFT \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e including 58 from this study and 15 reference strains from NCBI GenBank. A second alignment was also prepared with non-coding regions removed. Finally, to improve sampling from incomplete genomes available in our own dataset and in public dataset, we prepared a third alignment of G glycoprotein sequences (coding region) that contained additional 19 sequences. The best model for distance estimates were identified with the ModelFinder function \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e in IG-TREE2 \u003csup\u003e48\u003c/sup\u003e as the one with the lowest Bayesian information criterion (BIC). Maximum likelihood phylogenetic tree was also constructed using IG-TREE2 and branch support was assessed using both ultrafast bootstrap approximation (ufBoot, 1000 replicates) \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e and SH-like approximate likelihood ratio test (SH-aLRT). The tree was visualized in FigTree (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tree.bio.ed.ac.uk/software/figtree/\u003c/span\u003e\u003cspan address=\"http://tree.bio.ed.ac.uk/software/figtree/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and midpoint rooted for purposes of clarity.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of clade-defining mutations\u003c/h2\u003e \u003cp\u003eJalView \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e was used for genome translation and for the identification of persistent mutations in the viral proteins. The oldest genome of HeV sampled from a human in our library (ID: KY425627, Supplementary Table\u0026nbsp;2) was used as reference to annotate the mutations. Experimentally determined or AlphaFold \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e structures predicted with a local installation were used to locate mutations and for a preliminary inference of potential functional impacts. Visual Molecular Dynamics was used for visual inspection \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePhylodynamic analysis\u003c/h2\u003e \u003cp\u003eFirst, we assessed the temporal signal in HeV-g1 data by linear regression of root-to-tip distances on the Maximum Likelihood phylogeny against time of sampling using the program TempEst (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tree.bio.ed.ac.uk/software\u003c/span\u003e\u003cspan address=\"http://tree.bio.ed.ac.uk/software\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Exact dates were available for all sequences generated in this study. For two samples for which the exact day of sample collection was not known mid-month dates were assigned. Given the apparent temporal structure in the genome-scale alignments data, we then made estimates of the rates of evolutionary change (i.e., nucleotide substitutions per site per year) and the time to most recent common ancestor (TMRCA) using the Bayesian Markov chain Monte Carlo (MCMC) method available in BEAST (version 1.10.4) \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Here, we chose to use the complete coding regions dataset to make use of the SRD06 codon substitution model \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e and tested four different temporal \u0026amp; demographic scenarios: relaxed and strict molecular clock each with constant population and SkyGrid coalescent model \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e with path sampling used to rank the final models.\u003c/p\u003e \u003cp\u003eFor each analysis, three independent chains of 50\u0026nbsp;million generations (sampled every 10,000 states) were run to ensure convergence and then combined with appropriate burn-in. Statistical uncertainty was reflected in values of the 95% highest probability density (HPD). The maximum clade credibility (MCC) tree was estimated from the posterior distribution of trees with node heights scaled to mean values and posterior probabilities showing the statistical support for individual nodes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of the nature of selection pressures\u003c/h2\u003e \u003cp\u003eNext, we sought to determine the nature of selection pressures acting on HeV-g1 using the Datamonkey web server of the HyPhy package \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.datamonkey.org/\u003c/span\u003e\u003cspan address=\"http://www.datamonkey.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Accordingly, codon-based ML methods were used to estimate the ratio of nonsynonymous to synonymous substitutions per site (\u003cem\u003edN\u003c/em\u003e/\u003cem\u003edS\u003c/em\u003e ratio; also denoted ω); fixed-effects likelihood (FEL); fast, unconstrained Bayesian approximation (FUBAR); single likelihood ancestor counting (SLAC); and random effects likelihood (REL). Those sites with \u003cem\u003eP\u003c/em\u003e values of \u0026lt;\u0026thinsp;0.05 or with posterior probability values of \u0026gt;\u0026thinsp;0.95 were considered to provide significant evidence of positive selection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSpatiotemporal analyses\u003c/h2\u003e \u003cp\u003eWe tested for temporal structure in the presence of different clades using a permutation test that maintained the biological and spatial structure of the dataset by shuffling clade labels within the same host species and region. This approach allowed us to assess two metrics: (1) whether clade B was present before 2018 and (2) changes in the relative proportions of clades over 6-month intervals. Statistical significance was determined by comparing observed values to null distributions generated from 1,000 permutations. Full methodological details are provided in the Suppl. Methods 4.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eData availability and Visualization\u003c/h2\u003e \u003cp\u003eAll novel sequences reported here have been submitted in GenBank (accession numbers are in (Suppl. Table\u0026nbsp;2). The tree was visualized in FigTree (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tree.bio.ed.ac.uk/software/figtree/\u003c/span\u003e\u003cspan address=\"http://tree.bio.ed.ac.uk/software/figtree/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Maps were created in R version 4.3.0 \u003csup\u003e57\u003c/sup\u003e (Suppl. Methods).\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eCKY, AJP, JW, KM, RKP and VJM conceived the research; CKY, AJP, JW, KM, RKP and VJM designed the research methodology; AJP, RKP and VJM acquired funding for the research; MKK, PE, DNJ-S, CAF, ASD, JW, KM, RKP, and AJP collected samples; CKY, AJP, JW, KM, RKP, KH and VJM led project administration; CKY, JW, KM, SLA, TB, KMW, KB and CM curated the data; CKY, JSE, BB, ETP, AV, MS, MP, WC, DJ, ECH and VJM analyzed and visualized the data; ETP, AV, MS, MP, DJ described clade mutations; AJP, CM, RKP and VJM provided supervision; CKY, SLA, TB, and KB conducted laboratory screening, testing and validation; RKP, AJP and VJM provided resources; CKY, JSE AJP, and VJM drafted the manuscript; and all authors participated in review and editing of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest Statement:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no conflict of interest. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability and Visualization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll novel sequences reported here have been submitted in GenBank (accession numbers are in (Suppl. Table 2). The tree was visualized in FigTree (http://tree.bio.ed.ac.uk/software/figtree/). Maps were created in R version 4.3.0 \u003csup\u003e57\u003c/sup\u003e (Suppl. Methods).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe project was supported by the National Science Foundation (DEB1716698, EF-2133763/ EF-2231624), the DARPA PREEMPT program Cooperative Agreement # D18AC00031, and the Intramural Research Program of National Institute of Allergy and Infectious Diseases of the National Institutes of Health. The content of the information does not necessarily reflect the position or the policy of the U.S. government, and no official endorsement should be inferred. TJL was supported by an Endeavour Postgraduate Leadership Award and a Research Training Program scholarship sponsored by the Australian Government. AJP was supported by a Sydney Horizon Fellowship, an ARC DECRA fellowship (DE190100710), and a Queensland Government Accelerate Postdoctoral Research Fellowship. We acknowledge the Bundjalung, Butchulla, Danggan Balun, Gomeroi, Gumbainggir, Kabi Kabi, Taribelang Bunda, Turrbal, Widjabul Wia-bal, Yugambeh and Yuggera Ugarapul people, who are the Traditional Custodians of the land upon which this work was conducted. We also thank government and private landholders for granting permission for fieldwork and broader team members and volunteers for their contributions, including Peggy Eby, Maureen Kessler, Adrienne Dale, Manuel Ruiz Aravena, Liam Chirio, Mandy Allonby, Rachael Smethurst, Remy Brooks, Tim Pearson, Liam McGuire, Kirk Silas, Ticha Padgett-Stewart, Denise Karkkainen, Justine Scaccia, Ariana Ananda, Emma Glennon, Hannah Eiseman, Cinthia Pietromonaco, Sara LaTrielle, Isaac Knights, Dian Riseley, Emma Spence, Stella Maris Januario da Silva and many others. Hendra virus qRT-PCR primers and probe sequences were provided by the Public Health Agency of Canada. We thank Dan Sturdevant who designed the primers for amplicon-based sequencing, Julia R. Port, Jyothi Purushottam, Jonathan Schulz, and Zack Weishampel for helping to screen bat samples. We thank staff at Diagnostic Virology Team, Australian Centre for Disease Preparedness for technical supports, including virus isolation at BSL4 laboratory. We thank the Queensland and New South Wales Chief Veterinary Officers for allowing us to include the HeV horse sequences in this paper, and we thank the relevant veterinary and laboratory staff in each of these states for their contributions to the disease outbreak investigations.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWang, J.\u003cem\u003e et al.\u003c/em\u003e Individual bat virome analysis reveals co-infection and spillover among bats and virus zoonotic potential. \u003cem\u003eNature Communications\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 4079 (2023). https://doi.org/10.1038/s41467-023-39835-1\u003c/li\u003e\n\u003cli\u003eWang, Y.\u003cem\u003e et al.\u003c/em\u003e Unveiling bat-borne viruses: a comprehensive classification and analysis of virome evolution. \u003cem\u003eMicrobiome\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 235 (2024). https://doi.org/10.1186/s40168-024-01955-1\u003c/li\u003e\n\u003cli\u003eOlival, K. J.\u003cem\u003e et al.\u003c/em\u003e Host and viral traits predict zoonotic spillover from mammals. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e546\u003c/strong\u003e, 646-650 (2017). https://doi.org/10.1038/nature22975\u003c/li\u003e\n\u003cli\u003eIrving, A. T., Ahn, M., Goh, G., Anderson, D. E. \u0026amp; Wang, L.-F. Lessons from the host defences of bats, a unique viral reservoir. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e589\u003c/strong\u003e, 363-370 (2021). https://doi.org/10.1038/s41586-020-03128-0\u003c/li\u003e\n\u003cli\u003ePlowright, R. K.\u003cem\u003e et al.\u003c/em\u003e Transmission or Within-Host Dynamics Driving Pulses of Zoonotic Viruses in Reservoir\u0026ndash;Host Populations. \u003cem\u003ePLOS Neglected Tropical Diseases\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, e0004796 (2016). https://doi.org/10.1371/journal.pntd.0004796\u003c/li\u003e\n\u003cli\u003eField, H.\u003cem\u003e et al.\u003c/em\u003e The natural history of Hendra and Nipah viruses. \u003cem\u003eMicrobes and infection\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 307-314 (2001). \u003c/li\u003e\n\u003cli\u003eField, H.\u003cem\u003e et al.\u003c/em\u003e Hendra virus outbreak with novel clinical features, Australia. \u003cem\u003eEmerging Infectious Diseases\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 338 (2010). \u003c/li\u003e\n\u003cli\u003ePlayford, E. G.\u003cem\u003e et al.\u003c/em\u003e Human Hendra virus encephalitis associated with equine outbreak, Australia, 2008. \u003cem\u003eEmerging Infectious Diseases\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 219-223 (2010). \u003c/li\u003e\n\u003cli\u003eHalpin, K.\u003cem\u003e et al.\u003c/em\u003e Pteropid bats are confirmed as the reservoir hosts of henipaviruses: a comprehensive experimental study of virus transmission. \u003cem\u003eAm J Trop Med Hyg\u003c/em\u003e \u003cstrong\u003e85\u003c/strong\u003e, 946-951 (2011). https://doi.org/10.4269/ajtmh.2011.10-0567\u003c/li\u003e\n\u003cli\u003eEaton, B. T., Broder, C. C., Middleton, D. \u0026amp; Wang, L.-F. Hendra and Nipah viruses: different and dangerous. \u003cem\u003eNature Reviews Microbiology\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 23-35 (2006). https://doi.org/10.1038/nrmicro1323\u003c/li\u003e\n\u003cli\u003eKummer, S. \u0026amp; Kranz, D.-C. Henipaviruses\u0026mdash;A constant threat to livestock and humans. \u003cem\u003ePLOS Neglected Tropical Diseases\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, e0010157 (2022). https://doi.org/10.1371/journal.pntd.0010157\u003c/li\u003e\n\u003cli\u003eMurray, K.\u003cem\u003e et al.\u003c/em\u003e A morbillivirus that caused fatal disease in horses and humans. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e268\u003c/strong\u003e, 94-97 (1995). https://doi.org/10.1126/science.7701348\u003c/li\u003e\n\u003cli\u003ePlowright, R. K.\u003cem\u003e et al.\u003c/em\u003e Ecological dynamics of emerging bat virus spillover. \u003cem\u003eProceedings of the royal society B: biological sciences\u003c/em\u003e \u003cstrong\u003e282\u003c/strong\u003e, 20142124 (2015). \u003c/li\u003e\n\u003cli\u003eEby, P.\u003cem\u003e et al.\u003c/em\u003e Pathogen spillover driven by rapid changes in bat ecology. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e613\u003c/strong\u003e, 340-344 (2023). https://doi.org/10.1038/s41586-022-05506-2\u003c/li\u003e\n\u003cli\u003eTaylor, J.\u003cem\u003e et al.\u003c/em\u003e Novel variant Hendra virus genotype 2 infection in a horse in the greater Newcastle region, New South Wales, Australia. \u003cem\u003eOne Health\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 100423 (2022). https://doi.org/10.1016/j.onehlt.2022.100423\u003c/li\u003e\n\u003cli\u003ePeel, A. J.\u003cem\u003e et al.\u003c/em\u003e Novel Hendra Virus Variant Circulating in Black Flying Foxes and Grey-Headed Flying Foxes, Australia. \u003cem\u003eEmerg Infect Dis\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 1043-1047 (2022). https://doi.org/10.3201/eid2805.212338\u003c/li\u003e\n\u003cli\u003eAnnand, E. J.\u003cem\u003e et al.\u003c/em\u003e Novel Hendra Virus Variant Detected by Sentinel Surveillance of Horses in Australia. \u003cem\u003eEmerg Infect Dis\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 693-704 (2022). https://doi.org/10.3201/eid2803.211245\u003c/li\u003e\n\u003cli\u003eEdson, D.\u003cem\u003e et al.\u003c/em\u003e Routes of Hendra virus excretion in naturally-infected flying-foxes: implications for viral transmission and spillover risk. \u003cem\u003ePloS one\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, e0140670 (2015). \u003c/li\u003e\n\u003cli\u003eSmith, I.\u003cem\u003e et al.\u003c/em\u003e Identifying Hendra virus diversity in pteropid bats. \u003cem\u003ePLoS one\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, e25275 (2011). \u003c/li\u003e\n\u003cli\u003eHalpin, K., Young, P. L., Field, H. \u0026amp; Mackenzie, J. Isolation of Hendra virus from pteropid bats: a natural reservoir of Hendra virus. \u003cem\u003eJournal of General Virology\u003c/em\u003e \u003cstrong\u003e81\u003c/strong\u003e, 1927-1932 (2000). \u003c/li\u003e\n\u003cli\u003ePlowright, R. K.\u003cem\u003e et al.\u003c/em\u003e Urban habituation, ecological connectivity and epidemic dampening: the emergence of Hendra virus from flying foxes (Pteropus spp.). \u003cem\u003eProceedings of the Royal Society B: Biological Sciences\u003c/em\u003e \u003cstrong\u003e278\u003c/strong\u003e, 3703-3712 (2011). \u003c/li\u003e\n\u003cli\u003eEby, P. Seasonal movements of grey-headed flying-foxes, Pteropus poliocephalus (Chiroptera: Pteropodidae), from two maternity camps in northern New South Wales. \u003cem\u003eWildlife Research\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 547-559 (1991). \u003c/li\u003e\n\u003cli\u003eRoberts, B. J., Catterall, C. P., Eby, P. \u0026amp; Kanowski, J. Long-distance and frequent movements of the flying-fox Pteropus poliocephalus: implications for management. (2012). \u003c/li\u003e\n\u003cli\u003eMo, M.\u003cem\u003e et al.\u003c/em\u003e Congregations of a threatened species: mitigating impacts from Grey-headed Flying-fox Pteropus poliocephalus camps on the Batemans Bay community. \u003cem\u003eAustralian Zoologist\u003c/em\u003e \u003cstrong\u003e41\u003c/strong\u003e, 124-138 (2020). https://doi.org/10.7882/az.2020.021\u003c/li\u003e\n\u003cli\u003ePlowright, R. K.\u003cem\u003e et al.\u003c/em\u003e Ecological dynamics of emerging bat virus spillover. \u003cem\u003eProc Biol Sci\u003c/em\u003e \u003cstrong\u003e282\u003c/strong\u003e, 20142124 (2015). https://doi.org/10.1098/rspb.2014.2124\u003c/li\u003e\n\u003cli\u003eEby, P., Plowright, R. K., McCallum, H. \u0026amp; Peel, A. J. Conditions predict heightened Hendra virus spillover risk in horses this winter: actions now can change outcomes. \u003cem\u003eAust Vet J\u003c/em\u003e \u003cstrong\u003e98\u003c/strong\u003e, 270-271 (2020). https://doi.org/10.1111/avj.12964\u003c/li\u003e\n\u003cli\u003eBecker, D. J., Eby, P., Madden, W., Peel, A. J. \u0026amp; Plowright, R. K. Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts. \u003cem\u003eEcology Letters\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 23-36 (2023). https://doi.org/https://doi.org/10.1111/ele.14007\u003c/li\u003e\n\u003cli\u003eLunn, T. J.\u003cem\u003e et al.\u003c/em\u003e Periodic shifts in viral load increase risk of spillover from bats. \u003cem\u003ebioRxiv\u003c/em\u003e, 2023.2009.2006.556454 (2023). https://doi.org/10.1101/2023.09.06.556454\u003c/li\u003e\n\u003cli\u003eBurroughs, A. L.\u003cem\u003e et al.\u003c/em\u003e Hendra Virus Infection Dynamics in the Grey-Headed Flying Fox (Pteropus poliocephalus) at the Southern-Most Extent of Its Range: Further Evidence This Species Does Not Readily Transmit the Virus to Horses. \u003cem\u003ePLoS One\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, e0155252 (2016). https://doi.org/10.1371/journal.pone.0155252\u003c/li\u003e\n\u003cli\u003eSmith, I.\u003cem\u003e et al.\u003c/em\u003e Identifying Hendra virus diversity in pteropid bats. \u003cem\u003ePLoS One\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, e25275 (2011). https://doi.org/10.1371/journal.pone.0025275\u003c/li\u003e\n\u003cli\u003eGrenfell, B. T.\u003cem\u003e et al.\u003c/em\u003e Unifying the Epidemiological and Evolutionary Dynamics of Pathogens. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e303\u003c/strong\u003e, 327-332 (2004). https://doi.org/doi:10.1126/science.1090727\u003c/li\u003e\n\u003cli\u003eLo Presti, A.\u003cem\u003e et al.\u003c/em\u003e Origin and evolution of Nipah virus. \u003cem\u003eJ Med Virol\u003c/em\u003e \u003cstrong\u003e88\u003c/strong\u003e, 380-388 (2016). https://doi.org/10.1002/jmv.24345\u003c/li\u003e\n\u003cli\u003eCortes-Azuero, O.\u003cem\u003e et al.\u003c/em\u003e The genetic diversity of Nipah virus across spatial scales. \u003cem\u003eJ Infect Dis\u003c/em\u003e (2024). https://doi.org/10.1093/infdis/jiae221\u003c/li\u003e\n\u003cli\u003eStreicker, D. G., Fallas Gonz\u0026aacute;lez, S. L., Luconi, G., Barrientos, R. G. \u0026amp; Leon, B. Phylodynamics reveals extinction\u0026ndash;recolonization dynamics underpin apparently endemic vampire bat rabies in Costa Rica. \u003cem\u003eProceedings of the Royal Society B: Biological Sciences\u003c/em\u003e \u003cstrong\u003e286\u003c/strong\u003e, 20191527 (2019). https://doi.org/doi:10.1098/rspb.2019.1527\u003c/li\u003e\n\u003cli\u003eHughes, G. J., Orciari, L. A. \u0026amp; Rupprecht, C. E. Evolutionary timescale of rabies virus adaptation to North American bats inferred from the substitution rate of the nucleoprotein gene. \u003cem\u003eJournal of General Virology\u003c/em\u003e \u003cstrong\u003e86\u003c/strong\u003e, 1467-1474 (2005). \u003c/li\u003e\n\u003cli\u003eSmith, J. S., Orciari, L. A. \u0026amp; Yager, P. A. in \u003cem\u003eSeminars in virology.\u003c/em\u003e 387-400 (Elsevier).\u003c/li\u003e\n\u003cli\u003eStreicker, D. G.\u003cem\u003e et al.\u003c/em\u003e Host Phylogeny Constrains Cross-Species Emergence and Establishment of Rabies Virus in Bats. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e329\u003c/strong\u003e, 676-679 (2010). https://doi.org/doi:10.1126/science.1188836\u003c/li\u003e\n\u003cli\u003eVelasco-Villa, A.\u003cem\u003e et al.\u003c/em\u003e Molecular diversity of rabies viruses associated with bats in Mexico and other countries of the Americas. \u003cem\u003eJ Clin Microbiol\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e, 1697-1710 (2006). https://doi.org/10.1128/jcm.44.5.1697-1710.2006\u003c/li\u003e\n\u003cli\u003eHolmes, E. C., Woelk, C. H., Kassis, R. \u0026amp; Bourhy, H. Genetic constraints and the adaptive evolution of rabies virus in nature. \u003cem\u003eVirology\u003c/em\u003e \u003cstrong\u003e292\u003c/strong\u003e, 247-257 (2002). \u003c/li\u003e\n\u003cli\u003eBriese, T.\u003cem\u003e et al.\u003c/em\u003e Virome Capture Sequencing Enables Sensitive Viral Diagnosis and Comprehensive Virome Analysis. \u003cem\u003emBio\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, e01491-01415 (2015). https://doi.org/10.1128/mBio.01491-15\u003c/li\u003e\n\u003cli\u003eQuick, J.\u003cem\u003e et al.\u003c/em\u003e Multiplex PCR method for MinION and Illumina sequencing of Zika and other virus genomes directly from clinical samples. \u003cem\u003eNature protocols\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 1261-1276 (2017). https://doi.org/10.1038/nprot.2017.066\u003c/li\u003e\n\u003cli\u003eAvanzato, V. A.\u003cem\u003e et al.\u003c/em\u003e Case Study: Prolonged Infectious SARS-CoV-2 Shedding from an Asymptomatic Immunocompromised Individual with Cancer. \u003cem\u003eCell\u003c/em\u003e \u003cstrong\u003e183\u003c/strong\u003e, 1901-1912 e1909 (2020). https://doi.org/10.1016/j.cell.2020.10.049\u003c/li\u003e\n\u003cli\u003eGordon, A. \u003cem\u003eFASTX-Toolkit (RRID:SCR_005534)\u003c/em\u003e, \u0026lt;http://hannonlab.cshl.edu/fastx_toolkit/\u0026gt; (\u003c/li\u003e\n\u003cli\u003eYinda, C. K.\u003cem\u003e et al.\u003c/em\u003e A Novel Field-Deployable Method for Sequencing and Analyses of Henipavirus Genomes From Complex Samples on the MinION Platform. \u003cem\u003eJ Infect Dis\u003c/em\u003e \u003cstrong\u003e221\u003c/strong\u003e, S383-s388 (2020). https://doi.org/10.1093/infdis/jiz576\u003c/li\u003e\n\u003cli\u003eWang, J.\u003cem\u003e et al.\u003c/em\u003e A new Hendra virus genotype found in Australian flying foxes. \u003cem\u003eVirology Journal\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 197 (2021). https://doi.org/10.1186/s12985-021-01652-7\u003c/li\u003e\n\u003cli\u003eKatoh, K. \u0026amp; Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. \u003cem\u003eMol Biol Evol\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 772-780 (2013). https://doi.org/10.1093/molbev/mst010\u003c/li\u003e\n\u003cli\u003eKalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A. \u0026amp; Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. \u003cem\u003eNat Methods\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 587-589 (2017). https://doi.org/10.1038/nmeth.4285\u003c/li\u003e\n\u003cli\u003eMinh, B. Q.\u003cem\u003e et al.\u003c/em\u003e IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era. \u003cem\u003eMolecular Biology and Evolution\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 1530-1534 (2020). https://doi.org/10.1093/molbev/msaa015\u003c/li\u003e\n\u003cli\u003eHoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q. \u0026amp; Vinh, L. S. UFBoot2: Improving the Ultrafast Bootstrap Approximation. \u003cem\u003eMol Biol Evol\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 518-522 (2018). https://doi.org/10.1093/molbev/msx281\u003c/li\u003e\n\u003cli\u003eWaterhouse, A. M., Procter, J. B., Martin, D. M., Clamp, M. \u0026amp; Barton, G. J. Jalview Version 2--a multiple sequence alignment editor and analysis workbench. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 1189-1191 (2009). https://doi.org/10.1093/bioinformatics/btp033\u003c/li\u003e\n\u003cli\u003eJumper, J.\u003cem\u003e et al.\u003c/em\u003e Highly accurate protein structure prediction with AlphaFold. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e596\u003c/strong\u003e, 583-589 (2021). https://doi.org/10.1038/s41586-021-03819-2\u003c/li\u003e\n\u003cli\u003eHumphrey, W., Dalke, A. \u0026amp; Schulten, K. VMD: visual molecular dynamics. \u003cem\u003eJ Mol Graph\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 33-38, 27-38 (1996). https://doi.org/10.1016/0263-7855(96)00018-5\u003c/li\u003e\n\u003cli\u003eDrummond, A. J. \u0026amp; Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. \u003cem\u003eBMC Evol Biol\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 214 (2007). https://doi.org/10.1186/1471-2148-7-214\u003c/li\u003e\n\u003cli\u003eShapiro, B., Rambaut, A. \u0026amp; Drummond, A. J. Choosing appropriate substitution models for the phylogenetic analysis of protein-coding sequences. \u003cem\u003eMol Biol Evol\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 7-9 (2006). https://doi.org/10.1093/molbev/msj021\u003c/li\u003e\n\u003cli\u003eMinin, V. N., Bloomquist, E. W. \u0026amp; Suchard, M. A. Smooth skyride through a rough skyline: Bayesian coalescent-based inference of population dynamics. \u003cem\u003eMol Biol Evol\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 1459-1471 (2008). https://doi.org/10.1093/molbev/msn090\u003c/li\u003e\n\u003cli\u003eWeaver, S.\u003cem\u003e et al.\u003c/em\u003e Datamonkey 2.0: A Modern Web Application for Characterizing Selective and Other Evolutionary Processes. \u003cem\u003eMolecular Biology and Evolution\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 773-777 (2018). https://doi.org/10.1093/molbev/msx335\u003c/li\u003e\n\u003cli\u003eR: A language and Environment for Statistical computing (R Foundation for Statistical Computing, 2023).\u003c/li\u003e\n\u003cli\u003eBowden, T. A.\u003cem\u003e et al.\u003c/em\u003e Structural basis of Nipah and Hendra virus attachment to their cell-surface receptor ephrin-B2. \u003cem\u003eNat Struct Mol Biol\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 567-572 (2008). https://doi.org/10.1038/nsmb.1435\u003c/li\u003e\n\u003cli\u003eBowden, T. A., Crispin, M., Harvey, D. J., Jones, E. Y. \u0026amp; Stuart, D. I. Dimeric architecture of the Hendra virus attachment glycoprotein: evidence for a conserved mode of assembly. \u003cem\u003eJ Virol\u003c/em\u003e \u003cstrong\u003e84\u003c/strong\u003e, 6208-6217 (2010). https://doi.org/10.1128/jvi.00317-10\u003c/li\u003e\n\u003cli\u003eWang, Z.\u003cem\u003e et al.\u003c/em\u003e Potent monoclonal antibody-mediated neutralization of a divergent Hendra virus variant. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e \u003cstrong\u003e119\u003c/strong\u003e, e2122769119 (2022). https://doi.org/10.1073/pnas.2122769119\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"flying fox, Henipavirus, Hendra virus, genetic diversity, phylodynamics, phylogeography","lastPublishedDoi":"10.21203/rs.3.rs-6263655/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6263655/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHendra virus (HeV) was first discovered in 1994 in Australia, following an outbreak of respiratory and neurological disease in horses and humans. Limited genomic data on HeV has hindered a comprehensive understanding of HeV\u0026rsquo;s evolutionary dynamics. We conducted extensive spatiotemporal sampling and whole-genome sequencing of HeV-positive samples from bats and horses and genomic analyses revealed four distinct clades and additional cryptic clades. Each clade extended over a large spatial area, with strains from different clades co-occurring within a single roost on the same day and over multiple consecutive years. This absence of spatiotemporal genotypic structuring suggests that viral shedding events are not driven by the introduction of a single lineage into a susceptible population and then strain evolution through population level immune pressure. These findings provide crucial insights into how bats generate and maintain their extraordinary viral diversity, with direct implications for zoonotic disease emergence and pandemic threats.\u003c/p\u003e","manuscriptTitle":"Spatio-temporal dynamics of Hendra virus in Pteropus bats in Australia reveals high evolutionary diversity linked with spillover","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-10 09:36:28","doi":"10.21203/rs.3.rs-6263655/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-microbiology","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"nmicrobiol","sideBox":"Learn more about [Nature Microbiology](http://www.nature.com/nmicrobiol/)","snPcode":"","submissionUrl":"","title":"Nature Microbiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"16d53016-484f-443f-b4a6-ea83b359393d","owner":[],"postedDate":"April 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46905286,"name":"Biological sciences/Evolution/Population genetics/Genetic variation"},{"id":46905287,"name":"Biological sciences/Microbiology/Virology/Viral evolution"},{"id":46905288,"name":"Biological sciences/Microbiology/Virology/Viral reservoirs"},{"id":46905289,"name":"Biological sciences/Microbiology/Virology/Viral transmission"}],"tags":[],"updatedAt":"2026-04-08T07:05:59+00:00","versionOfRecord":{"articleIdentity":"rs-6263655","link":"https://doi.org/10.1038/s41564-025-02254-7","journal":{"identity":"nature-microbiology","isVorOnly":false,"title":"Nature Microbiology"},"publishedOn":"2026-04-07 04:00:00","publishedOnDateReadable":"April 7th, 2026"},"versionCreatedAt":"2025-04-10 09:36:28","video":"","vorDoi":"10.1038/s41564-025-02254-7","vorDoiUrl":"https://doi.org/10.1038/s41564-025-02254-7","workflowStages":[]},"version":"v1","identity":"rs-6263655","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6263655","identity":"rs-6263655","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}