Nasal Epithelial Cells of Donor Origin after Allogeneic Hematopoietic Cell Transplantation are Generated at a Faster Rate in the First 3 Months Compared with Later Posttransplantation

Detection of donor-type epithelial cells (ECs) after allogeneic hematopoietic cell transplantation (allo-HCT) using XY chromosome fluorescein in situ hybridization (FISH) has suggested that hematopoietic stem cells carry a degree of developmental plasticity. This is controversial, given artifacts of XY-based detection and the possibility of hematopoietic–nonhematopoietic cell fusion. Moreover, the kinetics of donor-type ECs (quantity at different time points after transplant) is unknown. Here, we document unequivocally the existence of donor-type ECs using a method obviating the artifacts of XY-FISH and study their kinetics. Nasal scrapings and blood specimens were collected from 60 allo-HCT survivors between 7 days and 22 years posttransplantation. DNA extracted from laser-captured nasal ECs (ie, CK+CD45− cells) and blood leukocytes was polymerase chain reaction–amplified for a panel of 16 short tandem repeat markers. The median percentage of donor-type ECs (among nasal ECs) was 0% on day 7 posttransplantation, 2.8% at 3 months posttransplantation, and 8.5% at 12-22 years posttransplantation. Cell fusion was ruled out by FISH analysis for two autosomes. We conclude that donor-type nasal ECs exist after HCT, and that their percentage rises rapidly in the first 3 months posttransplantation and more slowly thereafter

Relevant SARS-CoV-2 Genome Variation through Six Months of Worldwide Monitoring

Real-time genome monitoring of the SARS-CoV-2 pandemic outbreak is of utmost importance for designing diagnostic tools, guiding antiviral treatment and vaccination strategies. In this study, we present an accurate method for temporal and geographical comparison of mutational events based on GISAID database genome sequencing. Among 42523 SARS-CoV-2 genomes analyzed, we found 23202 variants compared to the reference genome. The Ti/Tv (transition/transversion) ratio was used to filter out possible false-positive errors. Transition mutations generally occurred more frequently than transversions. Our clustering analysis revealed remarkable hotspot mutation patterns for SARS-CoV-2. Mutations were clustered based on how their frequencies changed over time according to each geographical location. We observed some clusters showing a clear variation in mutation frequency and continuously evolving in the world. However, many mutations appeared in specific periods without a clear pattern over time. Various important nonsynonymous mutations were observed, mainly in Oceania and Asia. More than half of these mutations were observed only once. Four hotspot mutations were found in all geographical locations at least once: T265I (NSP2), P314L (NSP12), D614G (S), and Q57H (ORF3a). The current analysis of SARS-CoV-2 genomes provides valuable information on the geographical and temporal mutational evolution of SARS-CoV-2.Peer Reviewe