Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2

Detection of avian, murine, bovine, shrew, and bat coronaviruses in wild mammals from Mexico

Coronaviruses infect a wide range of animal and human hosts. Some human coronaviruses, such as SARS-CoV, MERS-CoV, and SARS-CoV-2, originated in animals, with bats often serving as ancestral hosts. This study analyzed samples from wild animals in three Mexican states, using an RT-PCR assay targeting the RdRp gene to detect and genotype coronaviruses, assessing their potential role as reservoirs. Phylogenetic analysis was conducted to determine the genetic relationships of the identified coronaviruses. Gammacoronavirus RNA was identified in fallow deer, llamas, spider monkeys, and mouflons; Betacoronavirus RNA in mouflons and dwarf goats; and Alphacoronavirus RNA in dwarf goats and ponies. The detected viral sequences exhibited high nucleotide identity with known coronaviruses, including Avian coronavirus (Gammacoronavirus), Murine coronavirus (Betacoronavirus), Betacoronavirus 1 (Betacoronavirus), Wénchéng shrew coronavirus (unclassified Alphacoronavirus), and Bat coronavirus HKU10 (Alphacoronavirus). These findings represent the first report of Avian coronavirus, Murine coronavirus, Wénchéng shrew coronavirus, and Bat coronavirus HKU10 in these species, as well as the first detection of Avian coronavirus in llamas, spider monkeys, and mouflons. This study provides valuable insights into the potential role of wildlife as coronavirus reservoirs, highlighting the importance of monitoring these viruses to mitigate future zoonotic transmission risks.

Viral genomic features predict Orthopoxvirus reservoir hosts

Orthopoxviruses (OPVs), including the causative agents of smallpox and mpox have led to devastating outbreaks in human populations worldwide. However, the discontinuation of smallpox vaccination, which also provides cross-protection against related OPVs, has diminished global immunity to OPVs more broadly. We apply machine learning models incorporating both host ecological and viral genomic features to predict likely reservoirs of OPVs. We demonstrate that incorporating viral genomic features in addition to host ecological traits enhanced the accuracy of potential OPV host predictions, highlighting the importance of host-virus molecular interactions in predicting potential host species. We identify hotspots for geographic regions rich with potential OPV hosts in parts of southeast Asia, equatorial Africa, and the Amazon, revealing high overlap between regions predicted to have a high number of potential OPV host species and those with the lowest smallpox vaccination coverage, indicating a heightened risk for the emergence or establishment of zoonotic OPVs. Our findings can be used to target wildlife surveillance, particularly related to concerns about mpox establishment beyond its historical range.

Viral genomic features predict Orthopoxvirus reservoir hosts

Orthopoxviruses (OPVs), including the causative agents of smallpox and mpox have led to devastating outbreaks in human populations worldwide. However, the discontinuation of smallpox vaccination, which also provides cross-protection against related OPVs, has diminished global immunity to OPVs more broadly. We apply machine learning models incorporating both host ecological and viral genomic features to predict likely reservoirs of OPVs. We demonstrate that incorporating viral genomic features in addition to host ecological traits enhanced the accuracy of potential OPV host predictions, highlighting the importance of host-virus molecular interactions in predicting potential host species. We identify hotspots for geographic regions rich with potential OPV hosts in parts of southeast Asia, equatorial Africa, and the Amazon, revealing high overlap between regions predicted to have a high number of potential OPV host species and those with the lowest smallpox vaccination coverage, indicating a heightened risk for the emergence or establishment of zoonotic OPVs. Our findings can be used to target wildlife surveillance, particularly related to concerns about mpox establishment beyond its historical range.

Using serosurveys to optimize surveillance for zoonotic pathogens

Zoonotic pathogens pose a significant risk to human health, with spillover into human populations contributing to chronic disease, sporadic epidemics, and occasional pandemics. Despite the widely recognized burden of zoonotic spillover, our ability to identify which animal populations serve as primary reservoirs for these pathogens remains incomplete. This challenge is compounded when prevalence reaches detectable levels only at specific times of year. In these cases, statistical models designed to predict the timing of peak prevalence could guide field sampling for active infections. Thus, we develop a general model that leverages routinely collected serosurveillance data to optimize sampling for elusive pathogens by predicting time windows of peak prevalence. Using simulated data sets, we show that our methodology reliably identifies times when pathogen prevalence is expected to peak. Then, we demonstrate an implementation of our method using publicly available data from two putative Ebolavirus reservoirs, straw-colored fruit bats (Eidolon helvum) and hammer-headed bats (Hypsignathus monstrosus). We envision our method being used to guide the planning of field sampling to maximize the probability of detecting active infections, and in cases when longitudinal data is available, our method can also yield predictions for the times of year that are most likely to produce future spillover events. The generality and simplicity of our methodology make it broadly applicable to a wide range of putative reservoir species where seasonal patterns of birth lead to predictable, but potentially short-lived, pulses of pathogen prevalence.

Subsequent Waves of Convergent Evolution in SARS-CoV-2 Genes and Proteins

Beginning in 2022, following widespread infection and vaccination among the global population, the SARS-CoV-2 virus mainly evolved to evade immunity derived from vaccines and past infections. This review covers the convergent evolution of structural, nonstructural, and accessory proteins in SARS-CoV-2, with a specific look at common mutations found in long-lasting infections that hint at the virus potentially reverting to an enteric sarbecovirus type.

Widespread exposure to SARS-CoV-2 in wildlife communities

Pervasive SARS-CoV-2 infections in humans have led to multiple transmission events to animals. While SARS-CoV-2 has a potential broad wildlife host range, most documented infections have been in captive animals and a single wildlife species, the white-tailed deer. The full extent of SARS-CoV-2 exposure among wildlife communities and the factors that influence wildlife transmission risk remain unknown. We sampled 23 species of wildlife for SARS-CoV-2 and examined the effects of urbanization and human use on seropositivity. Here, we document positive detections of SARS-CoV-2 RNA in six species, including the deer mouse, Virginia opossum, raccoon, groundhog, Eastern cottontail, and Eastern red bat between May 2022-September 2023 across Virginia and Washington, D.C., USA. In addition, we found that sites with high human activity had three times higher seroprevalence than low human-use areas. We obtained SARS-CoV-2 genomic sequences from nine individuals of six species which were assigned to seven Pango lineages of the Omicron variant. The close match to variants circulating in humans at the time suggests at least seven recent human-to-animal transmission events. Our data support that exposure to SARS-CoV-2 has been widespread in wildlife communities and suggests that areas with high human activity may serve as points of contact for cross-species transmission.

Significance of Artificial Intelligence in the Study of Virus-Host Cell Interactions

A highly critical event in a virus's life cycle is successfully entering a given host. This process begins when a viral glycoprotein interacts with a target cell receptor, which provides the molecular basis for target virus-host cell interactions for novel drug discovery. Over the years, extensive research has been carried out in the field of virus-host cell interaction, generating a massive number of genetic and molecular data sources. These datasets are an asset for predicting virus-host interactions at the molecular level using machine learning (ML), a subset of artificial intelligence (AI). In this direction, ML tools are now being applied to recognize patterns in these massive datasets to predict critical interactions between virus and host cells at the protein-protein and protein-sugar levels, as well as to perform transcriptional and translational analysis. On the other end, deep learning (DL) algorithms-a subfield of ML-can extract high-level features from very large datasets to recognize the hidden patterns within genomic sequences and images to develop models for rapid drug discovery predictions that address pathogenic viruses displaying heightened affinity for receptor docking and enhanced cell entry. ML and DL are pivotal forces, driving innovation with their ability to perform analysis of enormous datasets in a highly efficient, cost-effective, accurate, and high-throughput manner. This review focuses on the complexity of virus-host cell interactions at the molecular level in light of the current advances of ML and AI in viral pathogenesis to improve new treatments and prevention strategies.

Perspectives on SARS-CoV-2 Cases in Zoological Institutions

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in a zoological institution were initially reported in March 2020. Since then, at least 94 peer-reviewed cases have been reported in zoos worldwide. Among the affected animals, nonhuman primates, carnivores, and artiodactyls appear to be most susceptible to infection, with the Felidae family accounting for the largest number of reported cases. Clinical symptoms tend to be mild across taxa; although, certain species exhibit increased susceptibility to disease. A variety of diagnostic tools are available, allowing for initial diagnostics and for the monitoring of infectious risk. Whilst supportive therapy proves sufficient in most cases, monoclonal antibody therapy has emerged as a promising additional treatment option. Effective transmission of SARS-CoV-2 in some species raises concerns over potential spillover and the formation of reservoirs. The occurrence of SARS-CoV-2 in a variety of animal species may contribute to the emergence of variants of concern due to altered viral evolutionary constraints. Consequently, this review emphasizes the need for effective biosecurity measures and surveillance strategies to prevent and control SARS-CoV-2 infections in zoological institutions.

Experimental SARS-CoV-2 Infection of Elk and Mule Deer

To assess the susceptibility of elk (Cervus canadensis) and mule deer (Odocoileus hemionus) to SARS-CoV-2, we performed experimental infections in both species. Elk did not shed infectious virus but mounted low-level serologic responses. Mule deer shed and transmitted virus and mounted pronounced serologic responses and thus could play a role in SARS-CoV-2 epidemiology.

Evidence of SARS-CoV-2 infection in companion animals from owners who tested positive for COVID-19 in the Valley of Mexico

BACKGROUND

Little is known about the companion animals which tested positive in Mexico for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. Due to this, it is that we have documented the infection of companion animals, via an exploratory approach in two localities of the Valley of Mexico, in which the companion animal owners tested positive for COVID-19.

METHODS

Oropharyngeal and nasopharyngeal swabs were collected from 21 companion animals. Also, a Reverse-Transcription Quantitative Polymerase Chain Reaction was used to test five probes in three SARS-CoV-2 genes. More than one-third (5/14) of these samples were positive for SARS CoV-2 corresponding to dogs.

RESULTS

This research translates into the first available report on companion animals with SARS-CoV-2 infection in the most populated area of Mexico. Samples were added chronologically to previous reports prepared in other areas of the country, from February through November 2022.

CONCLUSION

Although SARS-CoV-2 infection in dogs is not as common as in other animals, our results suggest that it can be transmitted to dogs by their owners to a greater extent than previously reported.

Survey of white-footed mice (Peromyscus leucopus) in Connecticut, USA reveals low SARS-CoV-2 seroprevalence and infection with divergent betacoronaviruses

Diverse mammalian species display susceptibility to SARS-CoV-2. Potential SARS-CoV-2 spillback into rodents is understudied despite their host role for numerous zoonoses and human proximity. We assessed exposure and infection among white-footed mice (Peromyscus leucopus) in Connecticut, USA. We observed 1% (6/540) wild-type neutralizing antibody seroprevalence among 2020-2022 residential mice with no cross-neutralization of variants. We detected no SARS-CoV-2 infections via RT-qPCR, but identified non-SARS-CoV-2 betacoronavirus infections via pan-coronavirus PCR among 1% (5/468) of residential mice. Sequencing revealed two divergent betacoronaviruses, preliminarily named Peromyscus coronavirus-1 and -2. Both belong to the Betacoronavirus 1 species and are ~90% identical to the closest known relative, Porcine hemagglutinating encephalomyelitis virus. In addition, to provide a comparison, we also screened a species with significant SARS-CoV-2 infection and exposure across North America: the white-tailed deer (Odocoileus virginianus). We detected no active coronavirus infections and 7% (4/55) wild-type SARS-CoV-2 neutralizing antibody seroprevalence. Low SARS-CoV-2 seroprevalence suggests white-footed mice may not be sufficiently susceptible or exposed to SARS-CoV-2 to present a long-term human health risk. However, the discovery of divergent, non-SARS-CoV-2 betacoronaviruses expands the diversity of known rodent coronaviruses and further investigation is required to understand their transmission extent.

Molecular surveillance of potential SARS-CoV-2 reservoir hosts in wildlife rehabilitation centers

BACKGROUND

The COVID-19 pandemic, caused by SARS-CoV-2 infection, has become the most devastating zoonotic event in recent times, with negative impacts on both human and animal welfare as well as on the global economy. Although SARS-CoV-2 is considered a human virus, it likely emerged from animals, and it can infect both domestic and wild animals. This constitutes a risk for human and animal health including wildlife with evidence of SARS-CoV-2 horizontal transmission back and forth between humans and wild animals.

AIM

Molecular surveillance in different wildlife rehabilitation centers and wildlife associated institutions in Chile, which are critical points of animal-human interaction and wildlife conservation, especially since the aim of wildlife rehabilitation centers is to reintroduce animals to their original habitat.

MATERIALS AND METHODS

The survey was conducted in six WRCs and three wildlife associated institutions. A total of 185 samples were obtained from 83 individuals belonging to 15 different species, including vulnerable and endangered species. Each specimen was sampled with two different swabs: one oropharyngeal or nasopharyngeal according to the nostril diameter, and/or a second rectal sample. RNA was extracted from the samples and two different molecular assays were performed: first, a conventional RT-PCR with pan-coronavirus primers and a second SARS-CoV-2 qPCR targeting the N and S genes.

RESULTS

All 185 samples were negative for SARS-CoV-2.

CLINICAL RELEVANCE

This study constitutes the first report on the surveillance of SARS-CoV-2 from wildlife treated in rehabilitation centers in Chile, and supports the biosafety procedures adopted in those centers.

Modeling Tick Populations: An Ecological Test Case for Gradient Boosted Trees

General linear models have been the foundational statistical framework used to discover the ecological processes that explain the distribution and abundance of natural populations. Analyses of the rapidly expanding cache of environmental and ecological data, however, require advanced statistical methods to contend with complexities inherent to extremely large natural data sets. Modern machine learning frameworks such as gradient boosted trees efficiently identify complex ecological relationships in massive data sets, which are expected to result in accurate predictions of the distribution and abundance of organisms in nature. However, rigorous assessments of the theoretical advantages of these methodologies on natural data sets are rare. Here we compare the abilities of gradient boosted and linear models to identify environmental features that explain observed variations in the distribution and abundance of blacklegged tick (Ixodes scapularis) populations in a data set collected across New York State over a ten-year period. The gradient boosted and linear models use similar environmental features to explain tick demography, although the gradient boosted models found non-linear relationships and interactions that are difficult to anticipate and often impractical to identify with a linear modeling framework. Further, the gradient boosted models predicted the distribution and abundance of ticks in years and areas beyond the training data with much greater accuracy than their linear model counterparts. The flexible gradient boosting framework also permitted additional model types that provide practical advantages for tick surveillance and public health. The results highlight the potential of gradient boosted models to discover novel ecological phenomena affecting pathogen demography and as a powerful public health tool to mitigate disease risks.

The panzootic potential of SARS-CoV-2

Each year, SARS-CoV-2 is infecting an increasingly unprecedented number of species. In the present article, we combine mammalian phylogeny with the genetic characteristics of isolates found in mammals to elaborate on the host-range potential of SARS-CoV-2. Infections in nonhuman mammals mirror those of contemporary viral strains circulating in humans, although, in certain species, extensive viral circulation has led to unique genetic signatures. As in other recent studies, we found that the conservation of the ACE2 receptor cannot be considered the sole major determinant of susceptibility. However, we are able to identify major clades and families as candidates for increased surveillance. On the basis of our findings, we argue that the use of the term panzootic could be a more appropriate term than pandemic to describe the ongoing scenario. This term better captures the magnitude of the SARS-CoV-2 host range and would hopefully inspire inclusive policy actions, including systematic screenings, that could better support the management of this worldwide event.

Zoonoses in a changing world

Animals are continuously exposed to pathogens but rarely get infected, because pathogens must overcome barriers to establish successful infections. Ongoing planetary changes affect factors relevant for such infections, such as pathogen pressure and pathogen exposure. The replacement of wildlife with domestic animals shrinks the original host reservoirs, whereas expanding agricultural frontiers lead to increased contact between natural and altered ecosystems, increasing pathogen exposure and reducing the area where the original hosts can live. Climate change alters species' distributions and phenology, pathogens included, resulting in exposure to pathogens that have colonized or recolonized new areas. Globalization leads to unwilling movement of and exposure to pathogens. Because people and domestic animals are overdominant planetwide, there is increased selective pressure for pathogens to infect them. Nature conservation measures can slow down but not fully prevent spillovers. Additional and enhanced surveillance methods in potential spillover hotspots should improve early detection and allow swifter responses to emerging outbreaks.

Survey of white-footed mice in Connecticut, USA reveals low SARS-CoV-2 seroprevalence and infection with divergent betacoronaviruses

Diverse mammalian species display susceptibility to and infection with SARS-CoV-2. Potential SARS-CoV-2 spillback into rodents is understudied despite their host role for numerous zoonoses and human proximity. We assessed exposure and infection among white-footed mice (Peromyscus leucopus) in Connecticut, USA. We observed 1% (6/540) wild-type neutralizing antibody seroprevalence among 2020-2022 residential mice with no cross-neutralization of variants. We detected no SARS-CoV-2 infections via RT-qPCR, but identified non-SARS-CoV-2 betacoronavirus infections via pan-coronavirus PCR among 1% (5/468) of residential mice. Sequencing revealed two divergent betacoronaviruses, preliminarily named Peromyscus coronavirus-1 and -2. Both belong to the Betacoronavirus 1 species and are ~90% identical to the closest known relative, Porcine hemagglutinating encephalomyelitis virus. Low SARS-CoV-2 seroprevalence suggests white-footed mice may not be sufficiently susceptible or exposed to SARS-CoV-2 to present a long-term human health risk. However, the discovery of divergent, non-SARS-CoV-2 betacoronaviruses expands the diversity of known rodent coronaviruses and further investigation is required to understand their transmission extent.

A synergistic future for AI and ecology

Research in both ecology and AI strives for predictive understanding of complex systems, where nonlinearities arise from multidimensional interactions and feedbacks across multiple scales. After a century of independent, asynchronous advances in computational and ecological research, we foresee a critical need for intentional synergy to meet current societal challenges against the backdrop of global change. These challenges include understanding the unpredictability of systems-level phenomena and resilience dynamics on a rapidly changing planet. Here, we spotlight both the promise and the urgency of a convergence research paradigm between ecology and AI. Ecological systems are a challenge to fully and holistically model, even using the most prominent AI technique today: deep neural networks. Moreover, ecological systems have emergent and resilient behaviors that may inspire new, robust AI architectures and methodologies. We share examples of how challenges in ecological systems modeling would benefit from advances in AI techniques that are themselves inspired by the systems they seek to model. Both fields have inspired each other, albeit indirectly, in an evolution toward this convergence. We emphasize the need for more purposeful synergy to accelerate the understanding of ecological resilience whilst building the resilience currently lacking in modern AI systems, which have been shown to fail at times because of poor generalization in different contexts. Persistent epistemic barriers would benefit from attention in both disciplines. The implications of a successful convergence go beyond advancing ecological disciplines or achieving an artificial general intelligence-they are critical for both persisting and thriving in an uncertain future.

Surfing Corona waves - instead of breaking them: Rethinking the role of natural immunity in COVID-19 policy

In the first two years of the pandemic, COVID-19 response policies have aimed to break Corona waves through non-pharmaceutical interventions and mass vaccination. However, for long-term strategies to be effective and efficient, and to avoid massive disruption and social harms, it is crucial to introduce the role of natural immunity in our thinking about COVID-19  (or future "Disease-X") control and prevention. We argue that any Corona or similar virus control policy must appropriately balance five key elements simultaneously: balancing the various fundamental interests of the nation, as well as the various interventions within the health sector; tailoring the prevention measures and treatments to individual needs; limiting social interaction restrictions; and balancing the role of vaccinations against the role of naturally induced immunity. Given the high infectivity of SARS-CoV-2 and its differential impact on population segments, we examine this last element in more detail and argue that an important aspect of 'living with the virus' will be to better understand the role of naturally induced immunity in our overall COVID-19 policy response. In our eyes, a policy approach that factors natural immunity should be considered for persons without major comorbidities and those having 'encountered' the antigen in the past.

COVID-19 Spotlights Connections between Disease and Multiple Lifestyle Factors

The SARS-CoV-2 virus (severe acute respiratory syndrome coronavirus 2), and the disease it causes (COVID-19), have had a profound impact on global human society and threaten to continue to have such an impact with newly emerging variants. Because of the widespread effects of SARS-CoV-2, understanding how lifestyle choices impact the severity of disease is imperative. This review summarizes evidence for an involvement of chronic, non-resolving inflammation, gut microbiome disruption (dysbiosis with loss of beneficial microorganisms), and impaired viral defenses, all of which are associated with an imbalanced lifestyle, in severe disease manifestations and post-acute sequelae of SARS-CoV-2 (PASC). Humans' physiological propensity for uncontrolled inflammation and severe COVID-19 are briefly contrasted with bats' low propensity for inflammation and their resistance to viral disease. This insight is used to identify positive lifestyle factors with the potential to act in synergy for restoring balance to the immune response and gut microbiome, and thereby protect individuals against severe COVID-19 and PASC. It is proposed that clinicians should consider recommending lifestyle factors, such as stress management, balanced nutrition and physical activity, as preventative measures against severe viral disease and PASC.

SARS-CoV-2 in animals: susceptibility of animal species, risk for animal and public health, monitoring, prevention and control

The epidemiological situation of SARS-CoV-2 in humans and animals is continually evolving. To date, animal species known to transmit SARS-CoV-2 are American mink, raccoon dog, cat, ferret, hamster, house mouse, Egyptian fruit bat, deer mouse and white-tailed deer. Among farmed animals, American mink have the highest likelihood to become infected from humans or animals and further transmit SARS-CoV-2. In the EU, 44 outbreaks were reported in 2021 in mink farms in seven MSs, while only six in 2022 in two MSs, thus representing a decreasing trend. The introduction of SARS-CoV-2 into mink farms is usually via infected humans; this can be controlled by systematically testing people entering farms and adequate biosecurity. The current most appropriate monitoring approach for mink is the outbreak confirmation based on suspicion, testing dead or clinically sick animals in case of increased mortality or positive farm personnel and the genomic surveillance of virus variants. The genomic analysis of SARS-CoV-2 showed mink-specific clusters with a potential to spill back into the human population. Among companion animals, cats, ferrets and hamsters are those at highest risk of SARS-CoV-2 infection, which most likely originates from an infected human, and which has no or very low impact on virus circulation in the human population. Among wild animals (including zoo animals), mostly carnivores, great apes and white-tailed deer have been reported to be naturally infected by SARS-CoV-2. In the EU, no cases of infected wildlife have been reported so far. Proper disposal of human waste is advised to reduce the risks of spill-over of SARS-CoV-2 to wildlife. Furthermore, contact with wildlife, especially if sick or dead, should be minimised. No specific monitoring for wildlife is recommended apart from testing hunter-harvested animals with clinical signs or found-dead. Bats should be monitored as a natural host of many coronaviruses.

SARS-CoV-2 Infection in Captive Hippos (Hippopotamus amphibius), Belgium

Two adult female hippos in Zoo Antwerp who were naturally infected with SARS-CoV-2 showed nasal discharge for a few days. Virus was detected by immunocytochemistry and PCR in nasal swab samples and by PCR in faeces and pool water. Serology was also positive. No treatment was necessary.

Genomic representation predicts an asymptotic host adaptation of bat coronaviruses using deep learning

Introduction

Coronaviruses (CoVs) are naturally found in bats and can occasionally cause infection and transmission in humans and other mammals. Our study aimed to build a deep learning (DL) method to predict the adaptation of bat CoVs to other mammals.

Methods

The CoV genome was represented with a method of dinucleotide composition representation (DCR) for the two main viral genes, ORF1ab and Spike. DCR features were first analyzed for their distribution among adaptive hosts and then trained with a DL classifier of convolutional neural networks (CNN) to predict the adaptation of bat CoVs.

Results and discussion

The results demonstrated inter-host separation and intra-host clustering of DCR-represented CoVs for six host types: Artiodactyla, Carnivora, Chiroptera, Primates, Rodentia/Lagomorpha, and Suiformes. The DCR-based CNN with five host labels (without Chiroptera) predicted a dominant adaptation of bat CoVs to Artiodactyla hosts, then to Carnivora and Rodentia/Lagomorpha mammals, and later to primates. Moreover, a linear asymptotic adaptation of all CoVs (except Suiformes) from Artiodactyla to Carnivora and Rodentia/Lagomorpha and then to Primates indicates an asymptotic bats-other mammals-human adaptation.

Conclusion

Genomic dinucleotides represented as DCR indicate a host-specific separation, and clustering predicts a linear asymptotic adaptation shift of bat CoVs from other mammals to humans via deep learning.

Glycosylation and the global virome

The sugars that coat the outsides of viruses and host cells are key to successful disease transmission, but they remain understudied compared to other molecular features. Understanding the comparative zoology of glycosylation - and harnessing it for predictive science - could help close the molecular gap in zoonotic risk assessment.

SARS-CoV-2 Omicron (B.1.1.529) Infection of Wild White-Tailed Deer in New York City

There is mounting evidence of SARS-CoV-2 spillover from humans into many domestic, companion, and wild animal species. Research indicates that humans have infected white-tailed deer, and that deer-to-deer transmission has occurred, indicating that deer could be a wildlife reservoir and a source of novel SARS-CoV-2 variants. We examined the hypothesis that the Omicron variant is actively and asymptomatically infecting the free-ranging deer of New York City. Between December 2021 and February 2022, 155 deer on Staten Island, New York, were anesthetized and examined for gross abnormalities and illnesses. Paired nasopharyngeal swabs and blood samples were collected and analyzed for the presence of SARS-CoV-2 RNA and antibodies. Of 135 serum samples, 19 (14.1%) indicated SARS-CoV-2 exposure, and 11 reacted most strongly to the wild-type B.1 lineage. Of the 71 swabs, 8 were positive for SARS-CoV-2 RNA (4 Omicron and 4 Delta). Two of the animals had active infections and robust neutralizing antibodies, revealing evidence of reinfection or early seroconversion in deer. Variants of concern continue to circulate among and may reinfect US deer populations, and establish enzootic transmission cycles in the wild: this warrants a coordinated One Health response, to proactively surveil, identify, and curtail variants of concern before they can spill back into humans.

Variation in the ACE2 receptor has limited utility for SARS-CoV-2 host prediction

Transmission of SARS-CoV-2 from humans to other species threatens wildlife conservation and may create novel sources of viral diversity for future zoonotic transmission. A variety of computational heuristics have been developed to pre-emptively identify susceptible host species based on variation in the angiotensin-converting enzyme 2 (ACE2) receptor used for viral entry. However, the predictive performance of these heuristics remains unknown. Using a newly compiled database of 96 species, we show that, while variation in ACE2 can be used by machine learning models to accurately predict animal susceptibility to sarbecoviruses (accuracy = 80.2%, binomial confidence interval [CI]: 70.8-87.6%), the sites informing predictions have no known involvement in virus binding and instead recapitulate host phylogeny. Models trained on host phylogeny alone performed equally well (accuracy = 84.4%, CI: 75.5-91.0%) and at a level equivalent to retrospective assessments of accuracy for previously published models. These results suggest that the predictive power of ACE2-based models derives from strong correlations with host phylogeny rather than processes which can be mechanistically linked to infection biology. Further, biased availability of ACE2 sequences misleads projections of the number and geographic distribution of at-risk species. Models based on host phylogeny reduce this bias, but identify a very large number of susceptible species, implying that model predictions must be combined with local knowledge of exposure risk to practically guide surveillance. Identifying barriers to viral infection or onward transmission beyond receptor binding and incorporating data which are independent of host phylogeny will be necessary to manage the ongoing risk of establishment of novel animal reservoirs of SARS-CoV-2.

Ecological Predictors of Zoonotic Vector Status Among Dermacentor Ticks (Acari: Ixodidae): A Trait-Based Approach

Increasing incidence of tick-borne human diseases and geographic range expansion of tick vectors elevates the importance of research on characteristics of tick species that transmit pathogens. Despite their global distribution and role as vectors of pathogens such as Rickettsia spp., ticks in the genus Dermacentor Koch, 1844 (Acari: Ixodidae) have recently received less attention than ticks in the genus Ixodes Latreille, 1795 (Acari: Ixodidae). To address this knowledge gap, we compiled an extensive database of Dermacentor tick traits, including morphological characteristics, host range, and geographic distribution. Zoonotic vector status was determined by compiling information about zoonotic pathogens found in Dermacentor species derived from primary literature and data repositories. We trained a machine learning algorithm on this data set to assess which traits were the most important predictors of zoonotic vector status. Our model successfully classified vector species with ~84% accuracy (mean AUC) and identified two additional Dermacentor species as potential zoonotic vectors. Our results suggest that Dermacentor species that are most likely to be zoonotic vectors are broad ranging, both in terms of the range of hosts they infest and the range of ecoregions across which they are found, and also tend to have large hypostomes and be small-bodied as immature ticks. Beyond the patterns we observed, high spatial and species-level resolution of this new, synthetic dataset has the potential to support future analyses of public health relevance, including species distribution modeling and predictive analytics, to draw attention to emerging or newly identified Dermacentor species that warrant closer monitoring for zoonotic pathogens.

In silico method for predicting infectious strains of influenza A virus from its genome and protein sequences

Influenza A is a contagious viral disease responsible for four pandemics in the past and a major public health concern. Being zoonotic in nature, the virus can cross the species barrier and transmit from wild aquatic bird reservoirs to humans via intermediate hosts. In this study, we have developed a computational method for the prediction of human-associated and non-human-associated influenza A virus sequences. The models were trained and validated on proteins and genome sequences of influenza A virus. Firstly, we have developed prediction models for 15 types of influenza A proteins using composition-based and one-hot-encoding features. We have achieved a highest AUC of 0.98 for HA protein on a validation dataset using dipeptide composition-based features. Of note, we obtained a maximum AUC of 0.99 using one-hot-encoding features for protein-based models on a validation dataset. Secondly, we built models using whole genome sequences which achieved an AUC of 0.98 on a validation dataset. In addition, we showed that our method outperforms a similarity-based approach (i.e., blast) on the same validation dataset. Finally, we integrated our best models into a user-friendly web server 'FluSPred' (https://webs.iiitd.edu.in/raghava/fluspred/index.html) and a standalone version (https://github.com/raghavagps/FluSPred) for the prediction of human-associated/non-human-associated influenza A virus strains.

COVID-19 in animals: A need for One Health approach

BACKGROUND

SARS-CoV-2 has been identified as the cause of the COVID-19, which caused a global pandemic. It is a pathogen that causes respiratory disease and can easily navigate the interspecies barrier. A significant number of COVID-19 cases in animals have been reported worldwide, including but not limited to animals in farms, captivity, and household pets. Thus, assessing the affected population and anticipating 'at risk' population becomes essential.

OBJECTIVES

This article aims to emphasize the zoonotic potential of SARS- CoV-2 and discuss the One Health aspects of the disease.

CONTENT

This is a narrative review of recently published studies on animals infected with SARS-CoV-2, both experimental and natural. The elucidation of the mechanism of infection by binding SARS-CoV-2 spike protein to the ACE-2 receptor cells in humans has led to bioinformatic analysis that has identified a few other susceptible species in silico. While infections in animals have been extensively reported, no intermediary host has yet been identified for this disease. The articles collected in this review have been grouped into four categories; experimental inoculations, infection in wild animals, infection in farm animals and infection in pet animals, along with a review of literature in each category. The risk of infection transmission between humans and animals and vice versa and the importance of the One Health approach has been discussed at length in this article.

Artificial Intelligence Models for Zoonotic Pathogens: A Survey

Zoonotic diseases or zoonoses are infections due to the natural transmission of pathogens between species (animals and humans). More than 70% of emerging infectious diseases are attributed to animal origin. Artificial Intelligence (AI) models have been used for studying zoonotic pathogens and the factors that contribute to their spread. The aim of this literature survey is to synthesize and analyze machine learning, and deep learning approaches applied to study zoonotic diseases to understand predictive models to help researchers identify the risk factors, and develop mitigation strategies. Based on our survey findings, machine learning and deep learning are commonly used for the prediction of both foodborne and zoonotic pathogens as well as the factors associated with the presence of the pathogens.

The Huanan Seafood Wholesale Market in Wuhan was the early epicenter of the COVID-19 pandemic

Understanding how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in 2019 is critical to preventing future zoonotic outbreaks before they become the next pandemic. The Huanan Seafood Wholesale Market in Wuhan, China, was identified as a likely source of cases in early reports, but later this conclusion became controversial. We show here that the earliest known COVID-19 cases from December 2019, including those without reported direct links, were geographically centered on this market. We report that live SARS-CoV-2-susceptible mammals were sold at the market in late 2019 and that within the market, SARS-CoV-2-positive environmental samples were spatially associated with vendors selling live mammals. Although there is insufficient evidence to define upstream events, and exact circumstances remain obscure, our analyses indicate that the emergence of SARS-CoV-2 occurred through the live wildlife trade in China and show that the Huanan market was the epicenter of the COVID-19 pandemic.

Molecular adaptations during viral epidemics

In 1977, the world witnessed both the eradication of smallpox and the beginning of the modern age of genomics. Over the following half-century, 7 epidemic viruses of international concern galvanized virologists across the globe and led to increasingly extensive virus genome sequencing. These sequencing efforts exerted over periods of rapid adaptation of viruses to new hosts, in particular, humans provide insight into the molecular mechanisms underpinning virus evolution. Investment in virus genome sequencing was dramatically increased by the unprecedented support for phylogenomic analyses during the COVID-19 pandemic. In this review, we attempt to piece together comprehensive molecular histories of the adaptation of variola virus, HIV-1 M, SARS, H1N1-SIV, MERS, Ebola, Zika, and SARS-CoV-2 to the human host. Disruption of genes involved in virus-host interaction in animal hosts, recombination including genome segment reassortment, and adaptive mutations leading to amino acid replacements in virus proteins involved in host receptor binding and membrane fusion are identified as the key factors in the evolution of epidemic viruses.

Optimising predictive models to prioritise viral discovery in zoonotic reservoirs

Despite the global investment in One Health disease surveillance, it remains difficult and costly to identify and monitor the wildlife reservoirs of novel zoonotic viruses. Statistical models can guide sampling target prioritisation, but the predictions from any given model might be highly uncertain; moreover, systematic model validation is rare, and the drivers of model performance are consequently under-documented. Here, we use the bat hosts of betacoronaviruses as a case study for the data-driven process of comparing and validating predictive models of probable reservoir hosts. In early 2020, we generated an ensemble of eight statistical models that predicted host-virus associations and developed priority sampling recommendations for potential bat reservoirs of betacoronaviruses and bridge hosts for SARS-CoV-2. During a time frame of more than a year, we tracked the discovery of 47 new bat hosts of betacoronaviruses, validated the initial predictions, and dynamically updated our analytical pipeline. We found that ecological trait-based models performed well at predicting these novel hosts, whereas network methods consistently performed approximately as well or worse than expected at random. These findings illustrate the importance of ensemble modelling as a buffer against mixed-model quality and highlight the value of including host ecology in predictive models. Our revised models showed an improved performance compared with the initial ensemble, and predicted more than 400 bat species globally that could be undetected betacoronavirus hosts. We show, through systematic validation, that machine learning models can help to optimise wildlife sampling for undiscovered viruses and illustrates how such approaches are best implemented through a dynamic process of prediction, data collection, validation, and updating.

Exceptional diversity and selection pressure on coronavirus host receptors in bats compared to other mammals

Pandemics originating from non-human animals highlight the need to understand how natural hosts have evolved in response to emerging human pathogens and which groups may be susceptible to infection and/or potential reservoirs to mitigate public health and conservation concerns. Multiple zoonotic coronaviruses, such as severe acute respiratory syndrome-associated coronavirus (SARS-CoV), SARS-CoV-2 and Middle Eastern respiratory syndrome-associated coronavirus (MERS-CoV), are hypothesized to have evolved in bats. We investigate angiotensin-converting enzyme 2 (ACE2), the host protein bound by SARS-CoV and SARS-CoV-2, and dipeptidyl-peptidase 4 (DPP4 or CD26), the host protein bound by MERS-CoV, in the largest bat datasets to date. Both the ACE2 and DPP4 genes are under strong selection pressure in bats, more so than in other mammals, and in residues that contact viruses. Additionally, mammalian groups vary in their similarity to humans in residues that contact SARS-CoV, SARS-CoV-2 and MERS-CoV, and increased similarity to humans in binding residues is broadly predictive of susceptibility to SARS-CoV-2. This work augments our understanding of the relationship between coronaviruses and mammals, particularly bats, provides taxonomically diverse data for studies of how host proteins are bound by coronaviruses and can inform surveillance, conservation and public health efforts.

Susceptibility of Wild Canids to SARS-CoV-2

We assessed 2 wild canid species, red foxes (Vulpes vulpes) and coyotes (Canis latrans), for susceptibility to SARS-CoV-2. After experimental inoculation, red foxes became infected and shed infectious virus. Conversely, experimentally challenged coyotes did not become infected; therefore, coyotes are unlikely to be competent hosts for SARS-CoV-2.

Assessing the risk of human-to-wildlife pathogen transmission for conservation and public health

The SARS-CoV-2 pandemic has led to increased concern over transmission of pathogens from humans to animals, and its potential to threaten conservation and public health. To assess this threat, we reviewed published evidence of human-to-wildlife transmission events, with a focus on how such events could threaten animal and human health. We identified 97 verified examples, involving a wide range of pathogens; however, reported hosts were mostly non-human primates or large, long-lived captive animals. Relatively few documented examples resulted in morbidity and mortality, and very few led to maintenance of a human pathogen in a new reservoir or subsequent "secondary spillover" back into humans. We discuss limitations in the literature surrounding these phenomena, including strong evidence of sampling bias towards non-human primates and human-proximate mammals and the possibility of systematic bias against reporting human parasites in wildlife, both of which limit our ability to assess the risk of human-to-wildlife pathogen transmission. We outline how researchers can collect experimental and observational evidence that will expand our capacity for risk assessment for human-to-wildlife pathogen transmission.

The science of the host-virus network

Better methods to predict and prevent the emergence of zoonotic viruses could support future efforts to reduce the risk of epidemics. We propose a network science framework for understanding and predicting human and animal susceptibility to viral infections. Related approaches have so far helped to identify basic biological rules that govern cross-species transmission and structure the global virome. We highlight ways to make modelling both accurate and actionable, and discuss the barriers that prevent researchers from translating viral ecology into public health policies that could prevent future pandemics.