DOI: https://doi.org/10.3390/cancers13225721
PDF: https://www.mdpi.com/2072-6694/13/22/5721/pdf
DOI: https://doi.org/10.3390/cancers13225721
PDF: https://www.mdpi.com/2072-6694/13/22/5721/pdf
,
,
,
and
Back and forth transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between humans and animals will establish wild reservoirs of virus that endanger long-term efforts to control COVID-19 in people and to protect vulnerable animal populations. Better targeting surveillance and laboratory experiments to validate zoonotic potential requires predicting high-risk host species. A major bottleneck to this effort is the few species with available sequences for angiotensin-converting enzyme 2 receptor, a key receptor required for viral cell entry. We overcome this bottleneck by combining species' ecological and biological traits with three-dimensional modelling of host-virus protein–protein interactions using machine learning. This approach enables predictions about the zoonotic capacity of SARS-CoV-2 for greater than 5000 mammals—an order of magnitude more species than previously possible. Our predictions are strongly corroborated by in vivo studies. The predicted zoonotic capacity and proximity to humans suggest enhanced transmission risk from several common mammals, and priority areas of geographic overlap between these species and global COVID-19 hotspots. With molecular data available for only a small fraction of potential animal hosts, linking data across biological scales offers a conceptual advance that may expand our predictive modelling capacity for zoonotic viruses with similarly unknown host ranges.
Given that contact with humans fundamentally underlies transmission risk, it is notable that our model predicted high zoonotic capacity for multiple captive species that have also been confirmed as susceptible to SARS-CoV-2. These include numerous carnivores, such as large cats from multiple zoos and pet dogs and cats. Our model also predicted high SARS-CoV-2 zoonotic capacity for many farmed and domesticated species. The water buffalo (Bubalus bubalis), widely kept for dairy and plowing, had the highest probability of zoonotic capacity among livestock (0.91). Model predictions in the 90th percentile also included American mink (Neovison vison), red fox (Vulpes vulpes), sika deer (Cervus nippon), white-lipped peccary (Tayassu pecari), nilgai (Boselaphus tragocamelus) and raccoon dogs (Nyctereutes procyonoides), all of which are farmed. The escape of farmed individuals into wild populations has implications for the enzootic establishment of SARS-CoV-2 [33]. These findings also have implications for vaccination strategies, for instance, prioritizing people in contact with potential bridge species (e.g. slaughterhouse workers, farmers, veterinarians).
The Macaca genus comprised the majority of live-traded primates. Our model predicted high zoonotic capacity for all Macaca species (20/21 species, with all species within the top 10% of predictions except M. assamensis). Several live-traded carnivores and pangolins were also assigned high zoonotic capacity, including the Asiatic black bear (Ursus thibetanus), grey wolf (Canis lupus) and jaguar (Panthera onca), and the Philippine pangolin (Manis culionensis) and Sunda pangolin (M. javanica). One of the betacoronaviruses with the highest sequence similarity to SARS-CoV-2 was isolated from Sunda pangolins [70]. Interestingly, pangolin burrows are known to be occupied by other animal species, including numerous bats [71].
Commonly hunted species in the top 10% of predictions include duiker (Cephalophus zebra, West Africa), warty pig (Sus celebes, Southeast Asia) and two deer (Odocoileus hemionus and O. virginianus, Americas). The white-tailed deer (O. virginianus) was recently confirmed to transmit SARS-CoV-2 to conspecifics via aerosolized virus particles [72].
Our model identified 35 bat species within the 90th percentile of zoonotic capacity. Within the genus Rhinolophus, our model identified the large rufous horseshoe bat (Rhinolophus rufus) as having the highest probability of zoonotic capacity (0.89). Rhinolophus rufus is a known natural host for bat betacoronaviruses [73] and a congener to three other horseshoe bats harbouring betacoronaviruses with high nucleotide sequence similarity to SARS-CoV-2 (approx. 92–96%) [6,74,75]. For these other three species, our model assigned a range of probabilities for SARS-CoV-2 zoonotic capacity (Rhinolophus affinis (0.58), R. malayanus (0.70) and R. shameli (0.71)) and also predicted relatively high probabilities for two congeners, Rhinolophus acuminatus (0.84) and R. macrotis (0.70). These predictions agree with recent experiments demonstrating efficient viral binding of SARS-CoV-2 RBD for R. macrotis [76] and confirmation of SARS-CoV-2-neutralizing antibodies in field-caught R. acuminatus harbouring a closely related betacoronavirus [77].
Our model also identified 17 species in the genus Pteropus (flying foxes) with high probabilities of zoonotic capacity for SARS-CoV-2. Some of these species are confirmed reservoirs of other zoonotic viruses (e.g. henipaviruses in P. lylei, P. vampyrus, P. conspicillatus and P. alecto), with Southeast Asia also having the most mammal species with a high predicted zoonotic capacity (figure 4). Annual outbreaks attributed to spillover transmission from bats illustrate a persistent epizootic risk to humans [78–80] and confirm that gaps in systematic surveillance of zoonotic viruses, including betacoronaviruses, remain an urgent priority (e.g. [81]).
Our model identified 76 rodent species with high zoonotic capacity. Among these are the deer mouse (Peromyscus maniculatus) and white-footed mouse (P. leucopus), which are reservoirs for multiple zoonotic pathogens and parasites in North America [82–84]. Experimental infection, viral shedding and sustained intraspecific transmission of SARS-CoV-2 were recently confirmed for P. maniculatus [65,66]. Also in the top 10% were two rodents considered to be human commensals whose geographic ranges are expanding due to human activities: Rattus argentiventer (0.84) and R. tiomanicus (0.79) (electronic supplementary material, file S1) [85–87]. It is notable that many of these rodent species are preyed upon by carnivores, such as the red fox (Vulpes vulpes) or domestic cats (Felis catus) who themselves were predicted to have high zoonotic capacity by our model.
https://royalsocietypublishing.org/doi/10.1098/rspb.2021.1651
Maxence Meyer, Florentin Constancias, Claudia Worth, Anita Meyer, Marion Muller, Alexandre Boussuge, Georges Kaltenbach, Elise Schmitt, Said Chayer, Aurelie Velay, Thomas Vogel, Samira Fafi-Kremer, Patrick Karcher
INTRODUCTION The objectives of this study were to assess the dynamics of the SARS-CoV-2 anti-RBD IgG response over time among older people after COVID-19 infection or vaccination and its comparison with speculative levels of protection assumed by current data.
METHODS From November 2020 to October 2021, we included geriatric patients with serological test results for COVID-19. We considered antibody titre thresholds thought to be high enough to protect against SARS-CoV-2 infection: 141 BAU/ml for protection/vaccine efficacy > 89.3%. Three cohorts are presented. A vaccine group (n=34) that received two BNT162b2/Comirnaty injections 21 days apart, a group of natural COVID-19 infection (n=32) and a third group who contracted COVID-19 less than 15 days after the first BNT162b2/Comirnaty injection (n=17).
RESULTS 83 patients were included, the median age was 87 (81-91) years. In the vaccine group at 1 month since the first vaccination, the median BAU/ml with IQR was 620 (217-1874) with 87% of patients above the threshold of 141 BAU/ml. Seven months after the first vaccination the BAU/ml was 30 (19-58) with 9.5% of patients above the threshold of 141 BAU/ml. In the natural COVID-19 infection group, at 1 month since the date of first symptom onset, the median BAU/ml was 798 (325-1320) with 86.7% of patients above the threshold of 141 BAU/ml and fell to 88 (37-385) with 42.9% of patients above the threshold of 141 BAU/ml at 2 months. The natural infection group was vaccinated three months after the infection. Five months after the end of the vaccination cycle the BAU/ml was 2048 (471-4386) with 83.3% of patients above the threshold of 141 BAU/ml.
DISCUSSION On the humoral level, this supports the clinical results describing the decrease in vaccine protection over time.
The authors have declared no competing interest.
This study did not receive any funding
https://www.medrxiv.org/content/10.1101/2021.11.19.21266252v1
Dixit Tandel, Haripriya Parthasarathy,
People with diabetes are reported to have a higher risk of experiencing severe COVID-19 complications. Metformin, a first-line medication for type 2 diabetes, has antiviral properties. Some studies have indicated its prognostic potential in COVID-19. Here, we report that metformin significantly inhibits SARS-CoV-2 growth in cell culture models. SARS-CoV-2 infection of gut epithelial cell line, Caco2, resulted in higher phosphorylation of AMPK. Metformin reduced viral titers in the infected cells by nearly 99%, and by about 90% when cells were treated prior to infection. Metformin pre-treatment resulted in further phosphorylation of AMPK and caused a ten-fold reduction of viral titers indicating its potential in preventing naive infections. Confirming the positive impact of AMPK activation, another AMPK activator AICAR substantially inhibited of viral titers and, AMPK inhibitor Compound C, augmented it considerably. Metformin treatment post-SARS-CoV-2 infection resulted in nearly hundred-fold reduction of viral titers, indicating that the antiviral potency of the drug is far higher in infected cells, while still being able to reduce fresh infection. Metformin displayed SARS-CoV-2 TCID50 and TCID90 at 3.5 and 8.9 mM, respectively. In conclusion, our study demonstrates that metformin is very effective in limiting the replication of SARS-CoV-2 in cell culture and thus possibly could offer double benefits to diabetic COVID-19 patients by lowering both blood glucose levels and viral load.
The authors have declared no competing interest.