While urban expansion increasingly encroaches on natural habitats, many wildlife species capitalize on anthropogenic food resources, which have the potential to both positively and negatively ...influence their responses to infection. Here we examine how food availability and key nutrients have been reported to shape innate and adaptive immunity in wildlife by drawing from field-based studies, as well as captive and food restriction studies with wildlife species. Examples of food provisioning and key nutrients enhancing immune function were seen across the three study type distinctions, as were cases of trace metals and pharmaceuticals impairing the immunity of wildlife species. More generally, food provisioning in field studies tended to increase innate and adaptive responses to certain immune challenges, whereas patterns were less clear in captive studies. Mild food restriction often enhanced, whereas severe food restriction frequently impaired immunity. However, to enable stronger conclusions we stress a need for further research, especially field studies, and highlight the importance of integrating nutritional manipulation, immune challenge, and functional outcomes. Despite current gaps in research on this topic, modern high throughput molecular approaches are increasingly feasible for wildlife studies and offer great opportunities to better understand human influences on wildlife health.
This article is part of the theme issue ‘Anthropogenic resource subsidies and host–parasite dynamics in wildlife’.
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We report a novel orthohantavirus, putatively named Ozark orthohantavirus, in hispid cotton rats captured within the Ozark Plateau in Arkansas, USA. This virus phylogenetically clusters with other ...orthohantaviruses that cause severe human disease. Continued orthohantavirus surveillance and virus sequencing are needed to address the potential public health threat of this virus.
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DOBA, IZUM, KILJ, NUK, ODKLJ, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
3.
Bombali Virus in Mops condylurus Bat, Kenya Forbes, Kristian M; Webala, Paul W; Jääskeläinen, Anne J ...
Emerging infectious diseases,
05/2019, Volume:
25, Issue:
5
Journal Article
Peer reviewed
Open access
Bombali virus (genus Ebolavirus) was identified in organs and excreta of an Angolan free-tailed bat (Mops condylurus) in Kenya. Complete genome analysis revealed 98% nucleotide sequence similarity to ...the prototype virus from Sierra Leone. No Ebola virus-specific RNA or antibodies were detected from febrile humans in the area who reported contact with bats.
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We present a framework to better understand and predict data-deficient and novel orthohantavirus traits based on three distinct orthohantavirus–rodent host groups: murid-borne, arvicoline-borne, and ...non-arvicoline cricetid-borne orthohantaviruses.Relative to other orthohantaviruses, cricetid orthohantaviruses often cause severe human disease, arvicoline orthohantaviruses cause mild disease, and murid orthohantavirus cause moderate disease.Murid orthohantaviruses are often transmitted through aerosolized excreta, arvicoline orthohantaviruses through aerosolized excreta and saliva, and cricetid orthohantaviruses through saliva.Murid and arvicoline orthohantaviruses generally have high host fidelity, but cricetid orthohantaviruses frequently spillover with possible multi-host systems.Our framework provides generalizable insight that can help inform public health and biosafety policy.
Orthohantaviruses present a global public health threat; there are 58 distinct viruses currently recognized and case fatality of pathogenic orthohantaviruses ranges from <0.1% to 50%. An Old World versus New World dichotomy is frequently applied to distinguish human diseases caused by orthohantaviruses. However, this geographic grouping masks the importance of phylogeny and virus–host ecology in shaping orthohantavirus traits, especially since related arvicoline rodents and their orthohantaviruses are found in both regions. We argue that orthohantaviruses can be separated into three phylogenetically based rodent host groups with differences in key functional traits, including human disease, transmission route, and virus–host fidelity. This framework can help understand and predict traits of under-studied and newly discovered orthohantaviruses and guide public health and biosafety policy.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Persistent Puumala virus shedding occurs in wild voles.•Host population density thresholds estimated for hantavirus invasion.•Little is known about hantavirus transmission in non-rodent ...hosts.•Occupational, recreational and environmental risk factors identified for human infection.
Hantaviruses are primarily hosted by mammalian species of the orders Rodentia, Eulipotyphla and Chiroptera. Spillover to humans is common, and understanding hantavirus maintenance and transmission in reservoir host populations is important for efforts to curtail human disease. Recent field research challenges traditional phases of virus shedding kinetics derived from laboratory rodent infection experiments. Organ infection sites in non-rodent hosts suggest similar transmission routes to rodents, but require direct assessment. Further advances have also been made in understanding virus persistence (and fadeouts) in fluctuating host populations, as well as occupational, recreational and environmental risk factors associated with spillover to humans. However, despite relevance for both intra-species and inter-species transmission, our understanding of the longevity of hantaviruses in natural environments remains limited.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
We found similar mild perivascular inflammation in lungs of Bombali virus-positive and -negative Mops condylurus bats in Kenya, indicating the virus is well-tolerated. Our findings indicate M. ...condylurus bats may be a reservoir host for Bombali virus. Increased surveillance of these bats will be important to reduce potential virus spread.
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Ecoimmunology at spatial scales Forbes, Kristian M.; Gill, Jennifer
The Journal of animal ecology,
October 2020, 2020-10-00, 20201001, Volume:
89, Issue:
10
Journal Article
Peer reviewed
In Focus
Becker, D. J., Albery, G. F., Kessler, M. K., Lunn, T. J., Falvo, C. A., Czirják, G. Á., Martin, L. B., & Plowright, R. K. (2020). Macroimmunology: The drivers and consequences of spatial ...patterns in wildlife immune defence. Journal of Animal Ecology, 89, 972–995. Ecoimmunology seeks to identify and explain natural variation in immune function. Most research so far has focused on differences among individuals within populations, which are often driven by trade‐offs in resource allocation between energetically costly immunity and competing processes such as reproduction. In their review article, Becker et al. (2020) have proposed a framework to explicitly address habitat‐ and population‐level differences in wildlife immune phenotypes. Termed macroimmunology, this concept integrates principles from ecoimmunology and macroecology. Becker et al. (2020) have highlighted three non‐mutually exclusive habitat features that are likely to vary at spatial scales and influence immune function: (a) parasite pressure, (b) abiotic and biotic factors and (c) anthropogenic changes. However, a large and robust body of literature suitable for synthesis to detect macroimmunology patterns and effect sizes is not yet available. Through their systematic review and critical assessment, Becker et al. (2020) identified common problems in existing research that hinders spatial inferences, such as a need for spatial replication in study design and statistical analyses that account for spatial dependence. Overall, macroimmunology has the potential to identify and even predict spatial patterns in immune phenotypes that form the mechanistic underpinnings of important wildlife disease processes, and this review represents an important step to realizing these goals.
This paper contextualizes the 2020 Sidnie Manton winning article by Daniel Becker and colleagues. Comparison of ecoimmunology and macroimmunology sampling designs.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Identifying reservoir host species is crucial for understanding the ecology of multi‐host pathogens and predicting risks of pathogen spillover from wildlife to people.
Predictive models are ...increasingly used for identifying ecological traits and prioritizing surveillance of likely zoonotic reservoirs, but these often employ different types of evidence for establishing host associations. Comparisons between models with different infection evidence are necessary to guide inferences about the trait profiles of likely hosts and identify which hosts and geographical regions are likely sources of spillover.
Here, we use New World rodent–orthohantavirus associations to explore differences in the performance and predictions of models trained on two types of evidence for infection and onward transmission: RT‐PCR and live virus isolation data, representing active infections versus host competence, respectively. Orthohantaviruses are primarily carried by muroid rodents and cause the diseases haemorrhagic fever with renal syndrome (HFRS) and hantavirus cardiopulmonary syndrome (HCPS) in humans.
We show that although boosted regression tree (BRT) models trained on RT‐PCR and live virus isolation data both performed well and capture generally similar trait profiles, rodent phylogeny influenced previously collected RT‐PCR data, and BRTs using virus isolation data displayed a narrower list of predicted reservoirs than those using RT‐PCR data. BRT models trained on RT‐PCR data identified 138 undiscovered hosts and virus isolation models identified 92 undiscovered hosts, with 27 undiscovered hosts identified by both models. Distributions of predicted hosts were concentrated in several different regions for each model, with large discrepancies between evidence types. As a form of validation, virus isolation models independently predicted several orthohantavirus–rodent host associations that had been previously identified through empirical research using RT‐PCR.
Our model predictions provide a priority list of species and locations for future orthohantavirus sampling. More broadly, these results demonstrate the value of multiple data types for predicting zoonotic pathogen hosts. These methods can be applied across a range of systems to improve our understanding of pathogen maintenance and increase efficiency of pathogen surveillance.
The authors analysed two different types of evidence for host–virus relationships to predict undocumented novel hosts of New World orthohantaviruses. The methods used will be beneficial in focusing future surveillance for hosts of orthohantaviruses and other virus systems important to human and wildlife health.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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