Biology subjects

Gurtner, C.

Publications and source records attributed to Gurtner, C..

3 recordsLinked to original sources

bioRxiv2021

Establishment of well-differentiated camelid airway cultures to study Middle East respiratory syndrome coronavirus

In 2012, Middle East respiratory syndrome coronavirus (MERS-CoV) emerged in Saudi Arabia and was mostly associated with severe respiratory illness in humans. Dromedary camels are the zoonotic reservoir for MERS-CoV. To investigate the biology of MERS-CoV in camelids, we developed a well-differentiated airway epithelial cell (AEC) culture model for Llama glama and Camelus bactrianus. Histological characterization revealed progressive epithelial cellular differentiation with well-resemblance to autologous ex vivo tissues. We demonstrate that MERS-CoV displays a divergent cell tropism and replication kinetics profile in both AEC models. Furthermore, we observed that in the camelid AEC models MERS-CoV replication can be inhibited by both type I and III interferons (IFNs). In conclusion, we successfully established camelid AEC cultures that recapitulate the in vivo airway epithelium and reflect MERS-CoV infection in vivo. In combination with human AEC cultures, this system allows detailed characterization of the molecular basis of MERS-CoV cross-species transmission in respiratory epithelium.

microbiology↗

bioRxiv2021

Computer-Assisted Mitotic Count Using a Deep Learning-based Algorithm Improves Inter-Observer Reproducibility and Accuracy in canine cutaneous mast cell tumors

The mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intra-observer discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying/classifying mitotic figures (MFs). Recent progress in the field of artificial intelligence has allowed the development of high-performance algorithms that may improve standardization of the MC. As algorithmic predictions are not flawless, the computer-assisted review by pathologists may ensure reliability. In the present study we have compared partial (MC-ROI preselection) and full (additional visualization of MF candidate proposal and display of algorithmic confidence values) computer-assisted MC analysis to the routine (unaided) MC analysis by 23 pathologists for whole slide images of 50 canine cutaneous mast cell tumors (ccMCTs). Algorithmic predictions aimed to assist pathologists in detecting mitotic hotspot locations, reducing omission of MF and improving classification against imposters. The inter-observer consistency for the MC significantly increased with computer assistance (interobserver correlation coefficient, ICC = 0.92) compared to the unaided approach (ICC = 0.70). Classification into prognostic stratifications had a higher accuracy with computer assistance. The algorithmically preselected MC-ROIs had a consistently higher MCs than the manually selected MC-ROIs. Compared to a ground truth (developed with immunohistochemistry for phosphohistone H3), pathologist performance in detecting individual MF was augmented when using computer assistance (F1-score of 0.68 increased to 0.79) with a reduction in false negatives by 38%. The results of this study prove that computer assistance may lead to a more reproducible and accurate MCs in ccMCTs.

pathology↗

bioRxiv2020

Susceptibility of well-differentiated airway epithelial cell cultures from domestic and wildlife animals to SARS-CoV-2

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has spread globally, and the number of cases continues to rise all over the world. Besides humans, the zoonotic origin, as well as intermediate and potential spillback host reservoirs of SARS-CoV-2 are unknown. To circumvent ethical and experimental constraints, and more importantly, to reduce and refine animal experimentation, we employed our airway epithelial cell (AEC) culture repository composed of various domesticated and wildlife animal species to assess their susceptibility to SARS-CoV-2. In this study, we inoculated well-differentiated animal AEC cultures of monkey, cat, ferret, dog, rabbit, pig, cattle, goat, llama, camel, and two neotropical bat species with SARS-CoV-2. We observed that SARS-CoV-2 only replicated efficiently in monkey and cat AEC culture models. Whole-genome sequencing of progeny virus revealed no obvious signs of nucleotide transitions required for SARS-CoV-2 to productively infect monkey and cat epithelial airway cells. Our findings, together with the previously reported human-to-animal spillover events warrants close surveillance to understand the potential role of cats, monkeys, and closely related species as spillback reservoirs for SARS-CoV-2.

microbiology↗