Biology subjects
Publications and source records attributed to Doherty, S..
Withdrawal StatementThe authors have withdrawn this manuscript as we carry out reanalysis and reinterpretation of our results. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.
The domestic cat (Felis catus) descends from the African wildcat subspecies Felis lybica lybica. Its global distribution alongside humans testifies to its successful adaptation to anthropogenic environments. Uncertainty remains regarding whether domestic cats originated in the Levant, Egypt or elsewhere in its natural range, and on the timing and circumstances of their dispersal into Europe. By analysing 87 ancient and modern cat genomes, we demonstrate that domestic cats did not spread to Europe with Neolithic farmers, as previously thought. Conversely, our results suggest that they were introduced to Europe over the last 2,000 years, most likely from North Africa. We also demonstrate that a separate earlier (1st millennium BCE) introduction of wildcats from Northwest Africa originated the present-day wild population in Sardinia.
The earliest cats in human settlements in China were not domestic cats (Felis catus), but native leopard cats (Prionailurus bengalensis). To trace when and how domestic cats arrived in East Asia, we analyzed 22 feline bones from 14 sites across China spanning 5,000 years. Nuclear and mitochondrial genomes revealed that leopard cats began occupying anthropogenic scenes around 5,400 years ago and last appeared in 150 CE. Following several centuries gap of archeological feline remains, the first known domestic cat (706-883 CE) in China was identified in Shaanxi during the Tang Dynasty. Genomic analysis suggested a white or partially white coat and a link to a contemporaneous domestic cat from Kazakhstan, indicating a likely dispersal route via the Silk Road. The two felids once independently occupied ancient anthropogenic environments in China but followed divergent paths and reached different destinations in human-animal interactions.
Alveolar macrophages (AMs) are lower-airway resident myeloid cells and are among the first to respond to inhaled pathogens. Here, we interrogate AM innate sensing to Pathogen Associated Molecular Patterns (PAMPs) and determine AMs have decreased responses to low- dose LPS compared to other macrophages, as measured by TNF, IL-6, Ifnb, and Ifit3. We find the reduced response to low-dose LPS correlates with minimal TLR4 and CD14 surface expression, despite sufficient internal expression of TLR4. Additionally, we find that AMs do not produce IL-10 in response to a variety of PAMPs due to low expression of transcription factor c- Maf and that lack of IL-10 production contributes to an enhancement of pro-inflammatory responses by Type I IFN. Our findings demonstrate that AMs have cell-intrinsic dampened responses to LPS, which is enhanced by type I IFN exposure. These data implicate conditions where AMs may have reduced or enhanced sentinel responses to bacterial infections. HIGHLIGHTSO_LIAlveolar macrophages (AMs) do not produce TNF or IL-6 in response to low-dose LPS due to minimal surface expression of TLR4 and CD14 C_LIO_LILack of AM IL-10 production is dependent on low c-Maf expression C_LIO_LIExogenous c-Maf expression increases AM IL-10 production C_LIO_LIIFN{beta} enhances AM TNF and IL-6 responses to low-dose LPS and this is dependent on a lack of IL-10 C_LI
Species interactions play a fundamental role in ecosystems. However, few ecological communities have complete data describing such interactions, which is an obstacle to understanding how ecosystems function and respond to perturbations. Because it is often impractical to collect empirical data for all interactions in a community, various methods have been developed to infer interactions. Machine learning is increasingly being used for making interaction predictions, with random forest being one of the most frequently used of these methods. However, performance of random forest in inferring predator-prey interactions in terrestrial vertebrates and its sensitivity to training data quality remain untested. We examined predator-prey interactions in two diverse, primarily terrestrial vertebrate classes: birds and mammals. Combining data from a global interaction dataset and a specific community (Simpson Desert, Australia), we tested how well random forest predicted predator-prey interactions for mammals and birds using species ecomorphological and phylogenetic traits. We also tested how variation in training data quality--manipulated by removing records and switching interaction records to non-interactions--affected model performance. We found that random forest could predict predator-prey interactions for birds and mammals using ecomorphological or phylogenetic traits, correctly predicting up to 88% and 67% of interactions and non-interactions in the global and community-specific datasets, respectively. These predictions were accurate even when there were no records in the training data for focal species. In contrast, false non-interactions for focal predators in training data strongly degraded model performance. Our results demonstrate that random forest can identify predator-prey interactions for birds and mammals that have few or no interaction records. Furthermore, our study provides guidance on how to prepare training data to optimise machine-learning classifiers for predicting species interactions, which could help ecologists (i) address knowledge gaps and explore network-related questions in data-poor situations, and (ii) predict interactions for range-expanding species.