Compellingly high SARS-CoV-2 susceptibility of Golden Syrian hamsters suggests multiple zoonotic infections of pet hamsters during the COVID-19 pandemic (preprint)

Golden Syrian hamsters (Mesocricetus auratus) are used as a research model for severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2). Millions of Golden Syrian hamsters are also kept as pets in close contact to humans. To determine the minimum infective dose (MID) for assessing the zoonotic transmission risk, and to define the optimal infection dose for experimental studies, we orotracheally inoculated hamsters with SARS-CoV-2 doses from 1*105 to 1*10-4 tissue culture infectious dose 50 (TCID50). Body weight and virus shedding were monitored daily. 1*10-3 TCID50 was defined as the MID, and this was still sufficient to induce virus shedding at levels up to 102.75 TCID50/ml, equaling the estimated MID for humans. Virological and histological data revealed 1*102 TCID50 as the optimal dose for experimental infections. This compellingly high susceptibility resulting in productive infections in Golden Syrian hamsters needs to be considered also as a source of SARS-CoV-2 infections in humans.

What if we perceive SARS-CoV-2 genomes as documents? Topic modelling using Latent Dirichlet Allocation to identify mutation signatures and classify SARS-CoV-2 genomes (preprint)

Topic modeling is frequently employed for discovering structures (or patterns) in a corpus of documents. Its utility in text-mining and document retrieval tasks in various fields of scientific research is rather well known. An unsupervised machine learning approach, Latent Dirichlet Allocation (LDA) has particularly been utilized for identifying latent (or hidden) topics in document collections and for deciphering the words that define one or more topics using a generative statistical model. Here we describe how SARS-CoV-2 genomic mutation profiles can be structured into a Bag of Words to enable identification of signatures (topics) and their probabilistic distribution across various genomes using LDA. Topic models were generated using ~47000 novel corona virus genomes (considered as documents), leading to identification of 16 amino acid mutation signatures and 18 nucleotide mutation signatures (equivalent to topics) in the corpus of chosen genomes through coherence optimization. The document assumption for genomes also helped in identification of contextual nucleotide mutation signatures in the form of conventional N-grams (e.g. bi-grams and tri-grams). We validated the signatures obtained using LDA driven method against the previously reported recurrent mutations and phylogenetic clades for genomes. Additionally, we report the geographical distribution of the identified mutation signatures in SARS-CoV-2 genomes on the global map. Use of the non-phylogenetic albeit classical approaches like topic modeling and other data centric pattern mining algorithms is therefore proposed for supplementing the efforts towards understanding the genomic diversity of the evolving SARS-CoV-2 genomes (and other pathogens/microbes).

Susceptibility of raccoon dogs for experimental SARS-CoV-2 infection (preprint)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in China at the end of 2019, and became pandemic. The zoonotic virus most likely originated from bats, but definite intermediate hosts have not yet been identified. Raccoon dogs (Nyctereutes procyonoides) are kept for fur production, in particular in China, and were suspected as potential intermediate host for both SARS-CoV6 and SARS-CoV2. Here we demonstrate susceptibility of raccoon dogs for SARS-CoV-2 infection after intranasal inoculation and transmission to direct contact animals. Rapid, high level virus shedding, in combination with minor clinical signs and pathohistological changes, seroconversion and absence of viral adaptation highlight the role of raccoon dogs as a potential intermediate host. The results are highly relevant for control strategies and emphasize the risk that raccoon dogs may represent a potential SARS-CoV-2 reservoir. Our results support the establishment of adequate surveillance and risk mitigation strategies for kept and wild raccoon dogs. Article Summary LineRaccoon dogs are susceptible to and efficiently transmit SARS-CoV2 and may serve as intermediate host

Next-generation diagnostics: virus capture facilitates a sensitive viral diagnosis for epizootic and zoonotic pathogens including SARS-CoV-2 (preprint)

BackgroundThe detection of pathogens in clinical and environmental samples using high-throughput sequencing (HTS) is often hampered by large amounts of background information, which is especially true for viruses with small genomes. Enormous sequencing depth can be necessary to compile sufficient information for identification of a certain pathogen. Generic HTS combining with in-solution capture enrichment can markedly increase the sensitivity for virus detection in complex diagnostic samples. MethodsA virus panel based on the principle of biotinylated RNA-baits was developed for specific capture enrichment of epizootic and zoonotic viruses (VirBaits). The VirBaits set was supplemented by a SARS-CoV-2 predesigned bait set for testing recent SARS-CoV-2 positive samples. Libraries generated from complex samples were sequenced via generic HTS and afterwards enriched with the VirBaits set. For validation, an internal proficiency test for emerging epizootic and zoonotic viruses (African swine fever virus, Ebolavirus, Marburgvirus, Nipah henipavirus, Rift Valley fever virus) was conducted. ResultsThe VirBaits set consists of 177,471 RNA-baits (80-mer) based on about 18,800 complete viral genomes targeting 35 epizootic and zoonotic viruses. In all tested samples, viruses with both DNA and RNA genomes were clearly enriched ranging from about 10-fold to 10,000-fold for viruses including distantly related viruses with at least 72% overall identity to viruses represented in the bait set. Viruses showing a lower overall identity (38% and 46%) to them were not enriched but could nonetheless be detected based on capturing conserved genome regions. The internal proficiency test supports the improved virus detection using the combination of HTS plus targeted enrichment but also point to the risk of carryover between samples. ConclusionsThe VirBaits approach showed a high diagnostic performance, also for distantly related viruses. The bait set is modular and expandable according to the favored diagnostics, health sector or research question. The risk of carryover needs to be taken into consideration. The application of the RNA-baits principle turned out to be user-friendly, and even non-experts (without sophisticated bioinformatics skills) can easily use the VirBait workflow. The rapid extension of the established VirBaits set adapted to actual outbreak events is possible without any problems as shown for SARS-CoV-2.