Biology subjects

Moss, R.

Publications and source records attributed to Moss, R..

4 recordsLinked to original sources

bioRxiv2026

GM-CSF regulates ILC states and myeloid cell signaling during ulceration in Crohns disease

Macrophage (M-), granulocyte (G-), and granulocyte-macrophage (GM-) colony-stimulating factors (CSFs) regulate myeloid cell function, yet their relative roles during inflammation remain poorly defined. To uncover how CSFs shape spatial immune niches in Crohns disease, we performed Xenium single-cell spatial transcriptomics on ileal tissues, revealing cell-type-specific expression and source-target interactions for each CSF. GM-CSF, unlike M-CSF or G-CSF, was locally enriched in ulcerated regions where lymphocytes adjacent to macrophage aggregates signaled through STAT5 phosphorylation. To study functional consequences, we developed a csf2rb-/- zebrafish model of intestinal injury. Using this model, we found that loss of GM-CSF signaling exacerbated epithelial damage and inflammation, whereas recombinant human GM-CSF limited injury by restraining ILC1 expansion, sustaining ILC3 maintenance, and promoting IL-22 production. Cross-species single-cell analysis revealed conserved ILC gene modules and GM-CSF-dependent transcriptional networks linking lymphoid and myeloid populations. These findings establish GM-CSF as a critical spatial regulator of myeloid- lymphoid crosstalk and intestinal immune homeostasis in Crohns disease.

immunology↗

bioRxiv2026

Applying integrated population models to guide conservation planning: Western Capercaillie in Scotland

O_LIMany conservation interventions prove ineffective because they lack a rigorous evidence-base. Multifactorial drivers and data limitations often hinder anticipatory planning and are difficult to tackle with standard methods. Predictive modelling approaches that integrate diverse data sources with biological hypotheses can bridge this gap by clarifying the drivers of decline and evaluating management options under uncertainty. C_LIO_LI We developed a Bayesian integrated population model (IPM) applicable to lekking species, combining incomplete data from different life stages and seasons while accounting for observation error. IPMs are widely recognised for reducing bias relative to single-data-stream analyses, yet their application in conservation planning remains limited. C_LIO_LIWe applied the model to the threatened Western Capercaillie population in Scotland, which has been declining since at least the 1980s despite decades of conservation efforts, a typical case requiring urgent, evidence-based management. By fitting the model to 30 years of biased and incomplete monitoring data, we investigated associations between demographic processes and their potential drivers, including weather variables, fence collision mortality and a proxy of predation pressure. We used model predictions to evaluate the joint effectiveness of proposed management actions aimed at improving vital rates, including fence management and diversionary feeding. C_LIO_LIData integration improved population estimate precision by 17-49% relative to standalone national survey estimates, confirming that the decline continued from 1990-2023. Reproductive rate was related to the pattern of April warming, negatively affected by pre-breeding precipitation and positively by vole abundance, the latter consistent with the alternative prey hypothesis. Model predictions indicated that combining diversionary feeding with fence removal produced the most favourable conservation outcomes, but growth remained limited and uncertainty included possible continued decline. C_LIO_LI5. This modelling approach forms a key component of Scotlands Capercaillie Emergency Plan 2025-2030, guiding management by assessing population-level responses and predicting conservation intervention outcomes. In future, the model could support formal adaptive management through iterative evaluation of interventions as new data become available. Beyond this case study, our integrated approach offers a transferable framework for managing multifactorial declines in other threatened species under uncertainty. C_LI

ecology↗

bioRxiv2019

A sub-exponential branching process to study early epidemic dynamics with application to Ebola

Exponential growth is a mathematically convenient model for the early stages of an outbreak of an infectious disease. However, for many pathogens (such as Ebola virus) the initial rate of transmission may be sub-exponential, even before transmission is affected by depletion of susceptible individuals.\n\nWe present a stochastic multi-scale model capable of representing sub-exponential transmission: an in-homogeneous branching process extending the generalised growth model. To validate the model, we fit it to data from the Ebola epidemic in West Africa (2014-2016). We demonstrate how a branching process can be fit to both time series of confirmed cases and chains of infection derived from contact tracing. Our estimates of the parameters suggest transmission of Ebola virus was sub-exponential during this epidemic. Both the time series data and the chains of infections lead to consistent parameter estimates. Differences in the data sets meant consistent estimates were not a foregone conclusion. Finally, we use a simulation study to investigate the properties of our methodology. In particular, we examine the extent to which the estimates obtained from time series data and those obtained from chains of infection data agree.\n\nOur method, based on a simple branching process, is well suited to real-time analysis of data collected during contact tracing. Identifying the characteristic early growth dynamics (exponential or sub-exponential), including an estimate of uncertainty, during the first phase of an epidemic should prove a useful tool for preliminary outbreak investigations.\n\nAuthor SummaryEpidemic forecasts have the potential to support public health decision making in outbreak scenarios for diseases such as Ebola and influenza. In particular, reliable predictions of future incidence data may guide surveillance and intervention responses. Existing methods for producing forecasts, based upon mechanistic transmission models, often make an implicit assumption that growth is exponential, at least while susceptible depletion remains negligible. However, empirical studies suggest that many infectious disease outbreaks display sub-exponential growth early in the epidemic. Here we introduce a mechanistic model of early epidemic growth that allows for sub-exponential growth in incidence. We demonstrate how the model can be applied to the types of data that are typically available in (near) real-time, including time series data on incidence as well as individual-level case series and chains of transmission data. We apply our methods to publically available data from the 2014-2016 West Africa Ebola epidemic and demonstrate that early epidemic growth was sub-exponential. We also investigate the statistical properties of our model through a simulation re-estimation study to identify it performance characteristics and avenues for further methodological research.

bioinformatics↗

bioRxiv2018

Accounting for healthcare-seeking behaviours and testing practices in real-time influenza forecasts

For diseases such as influenza, where the majority of infected persons experience mild (if any) symptoms, surveillance systems are sensitive to changes in healthcare-seeking and clinical decision-making behaviours. This presents a challenge when trying to interpret surveillance data in near-real-time (e.g., in order to provide public health decision-support). Australia experienced a particularly large and severe influenza season in 2017, perhaps in part due to (a) mild cases being more likely to seek healthcare; and (b) clinicians being more likely to collect specimens for RT-PCR influenza tests. In this study we used weekly Flutracking surveillance data to estimate the probability that a person with influenza-like illness (ILI) would seek healthcare and have a specimen collected. We then used this estimated probability to calibrate near-real-time seasonal influenza forecasts at each week of the 2017 season, to see whether predictive skill could be improved. While the number of self-reported influenza tests in the weekly surveys are typically very low, we were able to detect a substantial change in healthcare seeking behaviour and clinician testing behaviour prior to the high epidemic peak. Adjusting for these changes in behaviour in the forecasting framework improved predictive skill. Our analysis demonstrates a unique value of community-level surveillance systems, such as Flutracking, when interpreting traditional surveillance data.

epidemiology↗