Middle East respiratory syndrome coronavirus (MERS-CoV) uses the S1B domain of its spike protein to bind to dipeptidyl peptidase 4 (DPP4), its functional receptor, and its S1A domain to bind to sialic acids. The tissue localization of DPP4 in humans, bats, camelids, pigs, and rabbits generally correlates with MERS-CoV tropism, highlighting the role of DPP4 in virus pathogenesis and transmission. However, MERS-CoV S1A does not indiscriminately bind to all α2,3-sialic acids, and the species-specific binding and tissue distribution of these sialic acids in different MERS-CoV-susceptible species have not been investigated. We established a novel method to detect these sialic acids on tissue sections of various organs of different susceptible species by using nanoparticles displaying multivalent MERS-CoV S1A. We found that the nanoparticles specifically bound to the nasal epithelial cells of dromedary camels, type II pneumocytes in human lungs, and the intestinal epithelial cells of common pipistrelle bats. Desialylation by neuraminidase abolished nanoparticle binding and significantly reduced MERS-CoV infection in primary susceptible cells. In contrast, S1A nanoparticles did not bind to the intestinal epithelium of serotine bats and frugivorous bat species, nor did they bind to the nasal epithelium of pigs and rabbits. Both pigs and rabbits have been shown to shed less infectious virus than dromedary camels and do not transmit the virus via either contact or airborne routes. Our results depict species-specific colocalization of MERS-CoV entry and attachment receptors, which may be relevant in the transmission and pathogenesis of MERS-CoV.
IMPORTANCE MERS-CoV uses the S1B domain of its spike protein to attach to its host receptor, dipeptidyl peptidase 4 (DPP4). The tissue localization of DPP4 has been mapped in different susceptible species. On the other hand, the S1A domain, the N-terminal domain of this spike protein, preferentially binds to several glycotopes of α2,3-sialic acids, the attachment factor of MERS-CoV. Here we show, using a novel method, that the S1A domain specifically binds to the nasal epithelium of dromedary camels, alveolar epithelium of humans, and intestinal epithelium of common pipistrelle bats. In contrast, it does not bind to the nasal epithelium of pigs or rabbits, nor does it bind to the intestinal epithelium of serotine bats and frugivorous bat species. This finding supports the importance of the S1A domain in MERS-CoV infection and tropism, suggests its role in transmission, and highlights its potential use as a component of novel vaccine candidates.
Copyright © 2019 American Society for Microbiology.
Antimicrobial resistance (AMR) is currently the most alarming issue for human health. AMR already causes 700,000 deaths/year. It is estimated that 10 million deaths due to AMR will occur every year after 2050. This equals the number of people dying of cancer every year in present times. International institutions such as G20, World Bank, World Health Organization (WHO), UN General Assembly, European Union, and the UK and USA governments are calling for new antibiotics. To underline this emergency, a list of antibiotic-resistant “priority pathogens” has been published by WHO. It contains 12 families of bacteria that represent the greatest danger for human health. Resistance to multiple antibiotics is particularly relevant for the Gram-negative bacteria present in the list. The ability of these bacteria to develop mechanisms to resist treatment could be transmitted with genetic material, allowing other bacteria to become drug resistant. Although the search for new antimicrobial drugs remains a top priority, the pipeline for new antibiotics is not promising, and alternative solutions are needed. A possible answer to AMR is vaccination. In fact, while antibiotic resistance emerges rapidly, vaccines can lead to a much longer lasting control of infections. New technologies, such as the high-throughput cloning of human B cells from convalescent or vaccinated people, allow for finding new protective antigens (Ags) that could not be identified with conventional technologies. Antibodies produced by convalescent B cell clones can be screened for their ability to bind, block, and kill bacteria, using novel high-throughput microscopy platforms that rapidly capture digital images, or by conventional technologies such as bactericidal, opsono-phagocytosis and FACS assays. Selected antibodies expressed by recombinant DNA techniques can be used for passive immunization in animal models and tested for protection. Antibodies providing the best protection can be employed to identify new Ags and then used for generating highly specific recombinant Fab fragments. Co-crystallization of Ags bound to Fab fragments will allow us to determine the structure and characteristics of new Ags. This structure-based Ag design will bring to a new generation of vaccines able to target previously elusive infections, thereby offering an effective solution to the problem of AMR.
Infectious disease outbreaks recapitulate biology: they emerge from the multi-level interaction of hosts, pathogens, and environment. Therefore, outbreak forecasting requires an integrative approach to modeling. While specific components of outbreaks are predictable, it remains unclear whether fundamental limits to outbreak prediction exist. Here, adopting permutation entropy as a model independent measure of predictability, we study the predictability of a diverse collection of outbreaks and identify a fundamental entropy barrier for disease time series forecasting. However, this barrier is often beyond the time scale of single outbreaks, implying prediction is likely to succeed. We show that forecast horizons vary by disease and that both shifting model structures and social network heterogeneity are likely mechanisms for differences in predictability. Our results highlight the importance of embracing dynamic modeling approaches, suggest challenges for performing model selection across long time series, and may relate more broadly to the predictability of complex adaptive systems.