Sebastiaan Theuns

The 2019 coronavirus disease pandemic (COVID-19) showed how genetic mutations can alter coronavirus characteristics. However, the evolution of livestock coronaviruses remains understudied. We analyzed 15 bovine coronavirus (BCoV), three porcine hemagglutinating encephalomyelitis virus (PHEV) and 18 porcine respiratory coronavirus (PRCV) isolates, mainly from Belgian livestock collected between 2020 and 2023. Spike gene phylogenetic analysis showed nucleotide substitution rates comparable between BCoV and PRCV, while PHEV appeared slower. Unlike severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), synonymous substitutions were preferred, limiting amino acid variation across decades in the animal coronaviruses. Virus neutralization assays with swine antisera indicated minimal antigenic change in PHEV and PRCV. Recent BCoV isolates showed antigenic divergence from the classical Mebus vaccine strain. The impact of this divergence on vaccine efficacy may warrant further research. Our findings underscore the need for periodic surveillance, as changes in surface proteins may affect pathogenicity, tissue tropism, host range and vaccine efficacy.

Mycoplasmas are known respiratory pathogens in tortoises, but few studies exist in snakes. To better understand the correlation with clinical signs and co-infections, samples from mycoplasma-positive snakes with and without clinical respiratory disease were analyzed. Oral swabs from 15 snakes (pythons n = 12, boas n = 3) were examined using polymerase chain reaction (PCR) and third-generation sequencing (TGS). Additionally, mycoplasma isolation assays were performed. Pathogens detected by PCR included Mycoplasmas (15/15, 100%), serpentoviruses (9/15, 60%), and Chlamydia sp. (2/15, 13%); those detected by TGS included Mycoplasmas (14/15, 93%), serpentoviruses (10/15, 67%), Chlamydia sp. (1/15, 7%), and 15 different bacterial species. Sequencing of the mycoplasma PCR products revealed a close genetic relationship to Mycoplasmopsis agassizii. TGS identified genetically distinct mycoplasmas and three different serpentoviruses. While mycoplasmas could not be successfully propagated, Brucella intermedia comb. nov. was identified in eight cultures. Respiratory disease in snakes is often multifactorial, involving various pathogens and environmental influences. This study demonstrates that comprehensive diagnostics are essential for understanding disease processes in snakes and improving the detection of diverse pathogens. Further research is needed to improve laboratory diagnostics for infectious diseases in reptiles and to better understand the roles of various pathogens in respiratory diseases in snakes.

Honey bees are dying due to a disease complex consisting of viruses, parasites, chemicals, nutritional deficiencies, and management problems. In the present study, pathogens and hemocytes were analyzed in honey bee hemolymph samples using third‐generation sequencing and flow cytometry in three apiaries over a honey bee season. Using nanopore sequencing, several viruses and bacteria were identified, including the first reported presence in European honey bees of La Jolla virus and Apis rhabdovirus 5. Seasonal pathogen peaks were found, with spring and summer showing the highest loads which coincide with the main flowering periods. Notably, viral infections often did not persist after initial detection despite the continuous appearance of new bees, hinting at colony‐wide transgenerational immunity. However, Hubei partiti‐like virus 34 remained endemic in one apiary and was found even in young bees, raising concerns about its ability to evade the transgenerational immunity. Additionally, dynamics in total hemocyte counts (THCs) were identified. Understanding pathogen and immune factor dynamics throughout the bee season will help identify weak colonies and provide valuable insights into colony collapse and its prevention.

Honey bees are rapidly declining, which poses a significant threat to our environment and agriculture industry. These vital insects face a disease complex believed to be caused by a combination of parasites, viruses, pesticides, and nutritional deficiencies. However, the real aetiology is still enigmatic. Due to the conventional analysis methods, we still lack complete insights into the honey bee virome and the presence of pathogenic bacteria. To fill this knowledge gap, we employed third-generation nanopore metagenomic sequencing on honey bee haemolymph to monitor the presence of pathogens over almost a year. This study provides valuable insights into the changes in bacterial and viral loads within honey bee colonies. We identified different pathogens in the honey bee haemolymph, which are not included in honey bee screenings. These pathogens comprise the Apis mellifera filamentous virus, Apis rhabdoviruses, and various bacteria such as Frischella sp. and Arsenophonus sp. Furthermore, a sharp contrast was observed between young and old bees. Our research proposes that transgenerational immune priming may play a role in shaping infection patterns in honey bees. We observed a significant increase in pathogen loads in the spring, followed by a notable decrease in pathogen presence during the summer and autumn months. However, certain pathogens seem to be able to evade this priming effect, making them particularly intriguing as potential factors contributing to mortality. In the future, we aim to expand our research on honey bee transgenerational immune priming and investigate its potential in natural settings. This knowledge will ultimately enhance honey bee health and decrease colony mortality.

Effective porcine health management relies majorly on diagnostic tests, vaccination, treatment strategies, and a proper biosecurity management plan. However, understanding the link between circulating microbes and biosecurity measures on a pig farm is not evident. Substantial progress has been made in recent years with the availability of new diagnostic tools (e.g., sequencing‐based diagnostics) and extensive biosecurity management questionnaires. However, the interpretation and correlation of these results are hampered by the abundance of gained (meta)data. Therefore, we aimed to cross‐correlate viral and bacterial pathogens with respiratory tropism detected by third‐generation nanopore metagenomic sequencing with biosecurity measures assessed by Biocheck.UGent™. The study was conducted on 25 sow farms with attached nurseries in Germany with known respiratory distress. The biosecurity level of the study farms complied with the European averages. Interestingly, the farms with the highest biosecurity score showed the lowest overall prevalence of porcine reproductive and respiratory syndrome virus (PRRSV) and Actinobacillus sp.; the circulation of well‐studied pathogenic viruses, such as PRRSV, was correlated with overall lower biosecurity scores, a higher number of stillborn piglets, and cocirculation of porcine parvovirus type 7. Moreover, potential risk factors for lesser‐known agents (e.g., porcine hemagglutinating and encephalomyelitis virus, porcine respiratory coronavirus, and porcine polyomavirus) could also be addressed. For the bacterial pathogen Glaesserella sp., a correlation with increased clinical signs was observed, whereas Lactobacillus sp. and Moraxella sp. are putative biomarkers for pig farms with better biosecurity scores. In conclusion, in‐depth cross‐correlation of (meta)data from new diagnostic platforms with biosecurity measures on pig farms may contribute to a better understanding of new actions in adapting biosecurity measures. This will not only contribute to improved animal welfare and economic productivity but also helping to address (new) zoonotic disease threats and potential treatments.

To date, no comprehensive diagnostics for the study of polymicrobial infections that are associated with porcine respiratory disease have been offered. This precludes the proper understanding of the entire disease landscape, thereby hampering effective preventive and therapeutic actions.

Dromedary camels are an important source of food and income in many countries. However, it has been largely overlooked that they can also transmit antibiotic-resistant bacteria. The aim of this study was to identify the Staphylococcaceae bacteria composition of the nasal flora in dromedary camels and evaluate the presence of methicillin-resistant Mammaliicoccus (MRM) and methicillin-resistant Staphylococcus (MRS) in dromedary camels in Algeria. Nasal swabs were collected from 46 camels from seven farms located in two different regions of Algeria (M’sila and Ouargla). We used non-selective media to determine the nasal flora, and antibiotic-supplemented media to isolate MRS and MRM. The staphylococcal isolates were identified using an Autoflex Biotyper Mass Spectrometer (MALDI-TOF MS). The mecA and mecC genes were detected by PCR. Methicillin-resistant strains were further analysed by long-read whole genome sequencing (WGS). Thirteen known Staphylococcus and Mammaliicoccus species were identified in the nasal flora, of which half (49.2%) were coagulase-positive staphylococci. The results showed that four out of seven farms were positive for MRS and/or MRM, with a total of 16 isolates from 13 dromedary camels. The predominant species were M. lentus, S. epidermidis, and S. aureus. Three methicillin-resistant S. aureus (MRSA) were found to be ST6 and spa type t304. Among methicillin-resistant S. epidermidis (MRSE), ST61 was the predominant ST identified. Phylogenetic analysis showed clonal relatedness among M. lentus strains, while S. epidermidis strains were not closely related. Resistance genes were detected, including mecA, mecC, ermB, tet(K), and blaZ. An SCCmec type VIII element was found in a methicillin-resistant S. hominis (MRSH) belonging to the ST1 strain. An SCCmec-mecC hybrid element was detected in M. lentus, similar to what was previously detected in M. sciuri. This study highlights that dromedary camels may be a reservoir for MRS and MRM, and that they contain a specific set of SCCmec elements. This emphasizes the need for further research in this ecological niche from a One Health perspective.

Influenza A virus (IAV) is a single-stranded, negative-sense RNA virus and a common cause of seasonal flu in humans. Its genome comprises eight RNA segments that facilitate reassortment, resulting in a great variety of IAV strains. To study these processes, the genetic code of each segment should be unraveled. Fortunately, new third-generation sequencing approaches allow for cost-efficient sequencing of IAV segments. Sequencing success depends on various factors, including proper sample storage and processing. Hence, this work focused on the effect of storage of oral fluids and swIAV sequencing. Oral fluids (n = 13) from 2017 were stored at −22 °C and later transferred to −80 °C. Other samples (n = 21) were immediately stored at −80 °C. A reverse transcription quantitative PCR (RT-qPCR) pre- and post-storage was conducted to assess IAV viral loads. Next, samples were subjected to two IAV long-read nanopore sequencing methods to evaluate success in this complex matrix. A significant storage-associated loss of swIAV loads was observed. Still, a total of 17 complete and 6 near-complete Polish swIAV genomes were obtained. Genotype T, (H1avN2, seven herds), P (H1N1pdm09, two herds), U (H1avN1, three herds), and A (H1avN1, 1 herd) were circulated on Polish farms. In conclusion, oral fluids can be used for long-read swIAV sequencing when considering appropriate storage and segment amplification protocols, which allows us to monitor swIAV in an animal-friendly and cost-efficient manner.