Traits, phylogeny and host cell receptors predict Ebolavirus host status among African mammals

Sundaram, Mekala; Schmidt, John P.; Han, Barbara A.; Drake, John M.; Stephens, Patrick R.

doi:10.1371/journal.pntd.0010993

Cited by 5 publications

(3 citation statements)

References 68 publications

(115 reference statements)

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“…Further developments in the elds of virology and immunology will bring crucial contributions to our framework, generating essential knowledge for model improvement and validation. As these data become more readily available, they will provide essential information on drivers of host-pathogen interaction at the molecular level, including receptor structure and molecular binding a nity, which will increase e cacy of modelling approaches (Sundaram et al, 2022). In conclusion, overcoming current data gaps and explicitly addressing the limitations of predictive approaches will allow modelling pipelines to be better integrated into strategies for zoonosis risk assessment and mitigation in the future.…”

Section: Discussionmentioning

confidence: 99%

A framework to predict zoonotic reservoirs under data uncertainty: a case study on betacoronaviruses

Tonelli,

Blagrove,

Wardeh

et al. 2024

Preprint

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1. Modelling approaches aimed at identifying currently unknown hosts of zoonotic diseases have the potential to make high-impact contributions to global strategies for zoonotic risk surveillance. However, geographical and taxonomic biases in host-pathogen associations might influence reliability of models and their predictions. 2. Here we propose a methodological framework to mitigate the effect of biases in host–pathogen data and account for uncertainty in models’ predictions. Our approach involves identifying “pseudo-negative” species and integrating sampling biases into the modelling pipeline. We present an application on the Betacoronavirus genus and provide estimates of mammal-borne betacoronavirus hazard at the global scale. 3. We show that the inclusion of pseudo-negatives in the analysis improves the overall performance of our model significantly (AUC = 0.82 and PR-AUC = 0.48, on average) compared to a model that does not use pseudo-negatives (AUC = 0.75 and PR-AUC = 0.39, on average), reducing the rate of false positives. Results of our application unveil currently unrecognised hotspots of betacoronavirus hazard in subequatorial Africa, and South America. 4. Our approach addresses crucial limitations in host–virus association modelling, with important downstream implications for zoonotic risk assessments. The proposed framework is adaptable to different multi-host disease systems and may be used to identify surveillance priorities as well as knowledge gaps in zoonotic pathogens’ host-range.

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Section: Discussionmentioning

confidence: 99%

A framework to predict zoonotic reservoirs under data uncertainty: a case study on betacoronaviruses

Tonelli,

Blagrove,

Wardeh

et al. 2024

Preprint

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“…This body of evidence, along with modelling studies (electronic supplementary material, text S6), point to forest-dwelling bats as likely Orthoebolavirus reservoir hosts. However, more recent studies indicate a more complex scenario with potential bridging hosts and environmental influences, highlighting the need for multidisciplinary approaches [153,[180][181][182][183][184][185][186][187][188][189]. Direct transmission of EBOV from bats to humans has been posited in two theoretical scenarios [112,162], without evidence.…”

Section: (A) Contrasting Examples: Information On Bats As Reservoir H...mentioning

confidence: 99%

Robust evidence for bats as reservoir hosts is lacking in most African virus studies: a review and call to optimize sampling and conserve bats

Weber,

Nagy,

Markotter

et al. 2023

Biol. Lett.

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Africa experiences frequent emerging disease outbreaks among humans, with bats often proposed as zoonotic pathogen hosts. We comprehensively reviewed virus–bat findings from papers published between 1978 and 2020 to evaluate the evidence that African bats are reservoir and/or bridging hosts for viruses that cause human disease. We present data from 162 papers (of 1322) with original findings on (1) numbers and species of bats sampled across bat families and the continent, (2) how bats were selected for study inclusion, (3) if bats were terminally sampled, (4) what types of ecological data, if any, were recorded and (5) which viruses were detected and with what methodology. We propose a scheme for evaluating presumed virus–host relationships by evidence type and quality, using the contrasting available evidence for Orthoebolavirus versus Orthomarburgvirus as an example. We review the wording in abstracts and discussions of all 162 papers, identifying key framing terms, how these refer to findings, and how they might contribute to people's beliefs about bats. We discuss the impact of scientific research communication on public perception and emphasize the need for strategies that minimize human–bat conflict and support bat conservation. Finally, we make recommendations for best practices that will improve virological study metadata.

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“…For the next 20 years, human cases were rare and geographically localized within the Middle Africa region ( 10 ). It is hypothesized to circulate in pteropodid bats and possibly other non-human primates that act as its natural reservoirs ( 11 ). However, outbreaks of EBOD have become more frequent, with almost 70% of the total outbreaks recorded after the year 2000 ( 10 ).…”

Section: Introductionmentioning

confidence: 99%

Molecular characterization of the 2022 Sudan virus disease outbreak in Uganda

Balinandi,

Whitmer,

Mulei

et al. 2023

J Virol

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Uganda experienced five Ebola disease outbreaks caused by Bundibugyo virus ( n = 1) and Sudan virus (SUDV) ( n = 4) from 2000 to 2021. On 20 September 2022, Uganda declared a fifth Sudan virus disease outbreak in the Mubende district, resulting in 142 confirmed and 22 probable cases by the end of the outbreak declaration on 11 January 2023. The earliest identified cases, through retrospective case investigations, had onset in early August 2022. From the 142 confirmed cases, we performed unbiased (Illumina) and SUDV-amplicon-specific (Minion) high-throughput sequencing to obtain 120 SUDV genome-and coding-complete sequences, representing 95.4% (104/109) of SVD-confirmed individuals within a sequence-able range (Ct ≤30) and 10 genome sequences outside of this range and 6 duplicate genome sequences. A comparison of the nucleotide genetic relatedness for the newly emerged Mubende variant indicated that it was most closely related to the Nakisamata SUDV sequence from 2011, represented a likely new zoonotic spillover event, and exhibited an inter- and intra-outbreak substitution rate consistent with previous outbreaks. The most recent common ancestor for the Mubende variant was estimated to have occurred in October and November 2021. The Mubende variant glycoprotein amino acid sequences exhibited 99.7% similarity altogether and a maximum of 96.1% glycoprotein similarity compared to historical SUDV strains from 1976. Integrating the genetic sequence and epidemiological data into the response activities generated a broad overview of the outbreak, allowing for quick fact-checking of epidemiological connections between the identified patients. IMPORTANCE Ebola disease (EBOD) is a public health threat with a high case fatality rate. Most EBOD outbreaks have occurred in remote locations, but the 2013–2016 Western Africa outbreak demonstrated how devastating EBOD can be when it reaches an urban population. Here, the 2022 Sudan virus disease (SVD) outbreak in Mubende District, Uganda, is summarized, and the genetic relatedness of the new variant is evaluated. The Mubende variant exhibited 96% amino acid similarity with historic SUDV sequences from the 1970s and a high degree of conservation throughout the outbreak, which was important for ongoing diagnostics and highly promising for future therapy development. Genetic differences between viruses identified during the Mubende SVD outbreak were linked with epidemiological data to better interpret viral spread and contact tracing chains. This methodology should be used to better integrate discrete epidemiological and sequence data for future viral outbreaks.

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Traits, phylogeny and host cell receptors predict Ebolavirus host status among African mammals

Cited by 5 publications

References 68 publications

A framework to predict zoonotic reservoirs under data uncertainty: a case study on betacoronaviruses

A framework to predict zoonotic reservoirs under data uncertainty: a case study on betacoronaviruses

Robust evidence for bats as reservoir hosts is lacking in most African virus studies: a review and call to optimize sampling and conserve bats

Molecular characterization of the 2022 Sudan virus disease outbreak in Uganda

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