Viruses: How avian influenza viruses spill over to mammals
The H3N2 canine influenza virus – which originally came from birds – is evolving to become more transmissible between dogs.
- University of Colorado Boulder, United States
- University of Georgia, United States
Picture your local lake covered with migrating geese, ducks or other waterfowl. Even though you don’t hear any coughing, you might well be witnessing ‘flu season’ for birds. Influenza viruses cause gastrointestinal infections in birds, and are spread when birds defecate in water that other birds then drink (Caliendo et al., 2020). Sometimes, however, avian influenza viruses make their way into mammals, including humans, and cause respiratory infections: how does this happen?
Waterfowl are the main natural reservoir for influenza viruses, and influenza viruses that infect humans and other mammals originally came from birds. This spillover can happen in two ways. The first way involves special mammalian hosts (like pigs) that can be infected by both avian and mammalian influenza viruses (Figure 1A). Occasionally, an individual from one of these species becomes simultaneously infected with both types of virus, and the two viruses exchange gene segments to form a novel virus that retains the ability to infect mammals. This process – which is known as gene reassortment – is what happened to start human influenza pandemics in 1957 and 1968 (Harrington et al., 2021).
Figure 1
How avian influenza viruses adapt to mammals.
(A) One way that avian viruses can adapt to mammals is through virus reassortment, as shown here. The avian virus (red) reassorts with another influenza virus that is already adapted to mammals (green), yielding a new mammalian-adapted virus. For this to happen, a single individual must be infected by both viruses simultaneously. (B) The other way that avian viruses can adapt to mammals is by direct infection and subsequent adaptation, as is illustrated here. As an avian virus (red) starts to spread between individuals in a mammalian population, it can acquire mutations that make it better at infecting mammals (an evolutionary intermediate is shown in blue). Eventually it may become well-adapted to infecting mammals (green). (C) Chen at al. isolated H3N2 canine influenza viruses from samples collected between 2012 and 2019 (middle columns), and tested them for a range of phenotypic properties that are associated with influenza viruses being able to infect mammals (rows). As controls, they included avian H3N2 (left column) and human H3N2 (right column) viruses. They found that, over time, the canine H3N2 virus gained multiple properties that make it compatible with mammals (green squares), with several of the key adaptations occurring around or after 2016/2017. Yellow squares indicate intermediate phenotypes. A blank square indicates that the test was not performed.
The second way that spillover can happen involves a mammal getting directly infected with a bird virus (Figure 1B). This individual then transmits this bird virus to others in its same species. If infection of the new species is sustained over time and within many individuals, the avian virus will experience natural selection for genetic mutations that make it more and more compatible with the mammalian species (Lipsitch et al., 2016).
Now, in eLife, Yipeng Sun (Chinese Agricultural University) and co-workers – including Mingyue Chen and Yanli Lyu as joint first authors – report the results of experiments which shed light on how a virus that normally infects birds is spreading and evolving in dogs around the world via this second form of spillover (Chen et al., 2023). It is important to understand how this happens, because there are avian viruses currently adapting to several different mammalian species via this direct bird-to-mammal pathway (Meng et al., 2022). Each of these ongoing evolution experiments could, at least in theory, yield a human-adapted virus.
There are only a small number of influenza viruses that humans live with, and these constitute our seasonal influenza virus repertoire. The same is true for dogs, and there are just two influenza viruses that dogs transmit consistently between each other – H3N2 and H3N8. The H3N2 canine influenza virus was first found in 2006 in Guangdong Province in China, and research revealed that its genome was closely related to that of the H3N2 influenza viruses found in birds (Li et al., 2010). The virus has since spread out of Asia and was first identified in dogs in the United States in 2015.
To study the evolution of H3N2 in dogs, Chen et al. collected 4174 tracheal swab samples from sick dogs in veterinary hospitals and kennels. The oldest sample dated back to 2012 and the most recent was from 2019. In addition to sequencing the viruses in these samples and analyzing them phylogenetically, the researchers also tested the viruses from a subset of the samples for a range of phenotypic properties that are associated with bird influenza viruses being able to infect mammals (Lipsitch et al., 2016). They found that the H3N2 canine influenza viruses have gained a number of these phenotypic properties since 2012, making them more and more adapted to mammals as time goes by (Figure 1C). To draw these conclusions, Chen et al. performed infections in dogs and ferrets, and also examined the ability of these viruses to infect human cells in tissue culture.
The researchers were also able to map some of the adaptions to specific sets of genetic changes in the virus genome. Some of these adaptations of avian H3N2 to dogs had been observed before, but prior studies focused on strains from 2017 or earlier (Martinez-Sobrido et al., 2020; Tangwangvivat et al., 2022). The remarkable experimental virology demonstrated in these studies is laborious, but critical for assessing the danger that animal viruses pose to humans (Warren and Sawyer, 2023).
Just because the H3N2 canine influenza virus is perfecting itself for dogs does not mean that it will infect humans. Once an avian influenza virus has perfected itself for a particular mammalian species, such as dogs, we don’t know the details of what then dictates its transmission to a different mammalian species, such as humans (Warren and Sawyer, 2019). Thankfully, there have been no reports to date of humans being infected with canine influenza virus.
References
-
The evolution and future of influenza pandemic preparednessExperimental & Molecular Medicine 53:737–749.https://doi.org/10.1038/s12276-021-00603-0
-
Avian-origin H3N2 canine influenza A viruses in Southern ChinaInfection, Genetics and Evolution 10:1286–1288.https://doi.org/10.1016/j.meegid.2010.08.010
-
Characterizing emerging canine H3 influenza virusesPLOS Pathogens 16:e1008409.https://doi.org/10.1371/journal.ppat.1008409
-
How host genetics dictates successful viral zoonosisPLOS Biology 17:e3000217.https://doi.org/10.1371/journal.pbio.3000217
Article and author information
Author details
Arturo Barbachano-Guerrero
Publication history
- Version of Record published: April 11, 2023 (version 1)
Copyright
© 2023, Barbachano-Guerrero et al.
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,329
- Page views
-
- 158
- Downloads
-
- 0
- Citations
Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Viruses: How avian influenza viruses spill over to mammals
eLife 12:e86051.
https://doi.org/10.7554/eLife.86051
Further reading
-
- Computational and Systems Biology
- Epidemiology and Global Health
Background: While biological age in adults is often understood as representing general health and resilience, the conceptual interpretation of accelerated biological age in children and its relationship to development remains unclear. We aimed to clarify the relationship of accelerated biological age, assessed through two established biological age indicators, telomere length and DNA methylation age, and two novel candidate biological age indicators , to child developmental outcomes, including growth and adiposity, cognition, behaviour, lung function and onset of puberty, among European school-age children participating in the HELIX exposome cohort.
Methods: The study population included up to 1,173 children, aged between 5 and 12 years, from study centres in the UK, France, Spain, Norway, Lithuania, and Greece. Telomere length was measured through qPCR, blood DNA methylation and gene expression was measured using microarray, and proteins and metabolites were measured by a range of targeted assays. DNA methylation age was assessed using Horvath's skin and blood clock, while novel blood transcriptome and 'immunometabolic' (based on plasma protein and urinary and serum metabolite data) clocks were derived and tested in a subset of children assessed six months after the main follow-up visit. Associations between biological age indicators with child developmental measures as well as health risk factors were estimated using linear regression, adjusted for chronological age, sex, ethnicity and study centre. The clock derived markers were expressed as Δ age (i.e., predicted minus chronological age).
Results: Transcriptome and immunometabolic clocks predicted chronological age well in the test set (r= 0.93 and r= 0.84 respectively). Generally, weak correlations were observed, after adjustment for chronological age, between the biological age indicators. Among associations with health risk factors, higher birthweight was associated with greater immunometabolic Δ age, smoke exposure with greater DNA methylation Δ age and high family affluence with longer telomere length. Among associations with child developmental measures, all biological age markers were associated with greater BMI and fat mass, and all markers except telomere length were associated with greater height, at least at nominal significance (p<0.05). Immunometabolic Δ age was associated with better working memory (p = 4e -3) and reduced inattentiveness (p= 4e -4), while DNA methylation Δ age was associated with greater inattentiveness (p=0.03) and poorer externalizing behaviours (p= 0.01). Shorter telomere length was also associated with poorer externalizing behaviours (p=0.03).
Conclusions: In children, as in adults, biological ageing appears to be a multi-faceted process and adiposity is an important correlate of accelerated biological ageing. Patterns of associations suggested that accelerated immunometabolic age may be beneficial for some aspects of child development while accelerated DNA methylation age and telomere attrition may reflect early detrimental aspects of biological ageing, apparent even in children.
Funding: UK Research and Innovation (MR/S03532X/1); European Commission (grant agreement numbers: 308333; 874583).
-
- Epidemiology and Global Health
Background:
Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022.
Methods:
We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance.
Results:
Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models.
Conclusions:
Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks.
Funding:
AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 (https://www.nfdi4health.de/task-force-covid-19-2) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).
