Gil, J. C. | Search bioRxiv

bioRxiv2026

Predicting neutron radiation exposure characteristics from an in vitro human skin model using RNA-seq

Accurate assessment of low-dose neutron radiation exposure remains a central challenge in biodosimetry, particularly for applications requiring non-invasive sample types such as skin. Here, we characterized the transcriptional response of a three-dimensional in vitro human skin model (EpiDermFT) to neutron irradiation at doses up to 0.75 Gy, measured from pre-exposure through a 14-day post-exposure period. RNA sequencing revealed greater than 800 significantly altered genes, including upregulation of FOS, FOSB, CDKN1A, MDM2, and GADD45A, and downregulation of NRG1, H3C11, and CENPX. Gene ontology enrichment indicated activation of DNA damage checkpoint signaling, cell cycle arrest, and stress-response pathways, alongside suppression of nucleosome assembly and DNA replication processes. Machine learning models trained on transcriptomic features exhibited strong predictive performance across biodosimetric endpoints. Classification models accurately distinguished irradiated from sham samples (AUC > 0.99), and regression models achieved high accuracy for estimating both absorbed dose (R2 = 0.97) and days post-exposure (R2 = 0.99). The latter, while highly predictive, may partially reflect transcriptional shifts associated with progressive degradation of the in vitro tissue model over time. Collectively, these findings demonstrate that RNA-based molecular signatures from human skin tissue provide a robust framework for quantitative estimation of neutron radiation exposure and temporal response dynamics.

genomics↗

bioRxiv2022

Canada goose fecal microbiota correlate with geography more than host-associated co-factors

The gut microbiota has many positive effects on the host, but how the microbiota is shaped and influenced can vary greatly. These factors affect the composition, diversity, and function of host-associated microbiota; however, these factors vary greatly from organism to organism and clade to clade. The avian microbiota often correlates more with the sampling locations rather than host-associated co-factors. These correlations between location and microbiota often only include a few sampling locations within the species range. To better understand the connection between geographic distance and the microbiota, were collected from non-migratory Canada geese across the United States. We expected host-associated factors to have minimal effect on the microbiota and geese microbiota will be strongly correlated to geography. We hypothesized more proximal geese will be exposed to more similar environmental microbes and will have more similar microbiota. Canada geese microbiota are largely similar across the entire sampling range. Several bacterial taxa were shared by more than half of the geese. Four phyla were found in the majority of the samples: Firmicutes, Proteobacteria, Bacteroidetes, and Actinobacteria. Three genera were also present in the majority of the samples: Helicobacter, Subdoligranulum, and Faecalibacterium. There were minimal differences in alpha diversity with respect to age, sex, and flyway. There were significant correlations between geography and beta diversity. Supervised machine learning models were able to predict the location of a fecal sample based on taxonomic data alone. Distance decay analysis show a positive relationship between geographic distance and beta diversity. Our work provides novel insights into the microbiota of the ubiquitous Canada goose and further supports the claim that the avian microbiota is largely dominated by the hosts environment. This work also suggests that there is a minimum distance that must be reached before significant differences in the microbiota between two individuals can be observed.

microbiology↗