Doherty, S. | bioRxiv Science

bioRxiv2024

Absence of c-Maf and IL-10 enables Type I IFN enhancement of innate responses to low-dose LPS in alveolar macrophages

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

immunology↗

bioRxiv2022

Predicting predator-prey interactions in terrestrial endotherms using random forest

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.

ecology↗