Biology subjects

Duggan, J.

Publications and source records attributed to Duggan, J..

4 recordsLinked to original sources

bioRxiv2025

Maladaptive inflammatory signaling in old mice impairs colonic regeneration by promoting a sustained fetal-like epithelial state

Aging is associated with a decline in the regenerative capacity of many tissues. Central to this decline is a complex interplay between inflammation and stem cell function. How these two processes are linked and influence regenerative capacity remains unclear. Here, we undertake a comprehensive assessment of age-related changes in the mouse colon at single-cell resolution. A survey of immune and epithelial compartments revealed a hyperactivated inflammatory state in the colon of old mice characterized by the induction of an interferon {gamma} (IFN{gamma}) response signature in immune cells. This does not result in increased inflammation under homeostasis, but triggers a disproportionate inflammatory response, disrupting regeneration after challenge with the enteropathogen Citrobacter rodentium. Colons of old mice exhibit higher production of IFN{gamma} by T and innate lymphoid cells (ILCs) that are associated with reduced Lgr5+ stem cells and decreased epithelial proliferation. Interestingly, we find aged intestinal epithelial cells to be hypersensitive to IFN{gamma} signaling, inducing a regeneration-associated fetal-like gene expression signature that, in turn, renders these cells more sensitive to IFN{gamma}-induced apoptosis. Our findings reveal an age-related imbalance in the interaction between the immune and epithelial compartments in the colon, priming the system for excessive inflammatory responses and the emergence of a hypersensitive epithelial cell state thus derailing proper repair of the intestinal epithelium after injury.

immunology↗

bioRxiv2023

Anchoring the mean generation time in the SEIR to mitigate biases in R0 estimates due to uncertainty in the distribution of the epidemiological delays

The basic reproduction number,[R] 0, is of paramount importance in the study of infectious disease dynamics. Primarily,[R] 0 serves as an indicator of the transmission potential of an emerging infectious disease and the effort required to control the invading pathogen. However, its estimates from compartmental models are strongly conditioned by assumptions in the model structure, such as the distributions of the latent and infectious periods (epidemiological delays). To further complicate matters, models with dissimilar delay structures produce equivalent incidence dynamics. Following a simulation study, we reveal that the nature of such equivalency stems from a linear relationship between[R] 0 and the mean generation time, along with adjustments to other parameters in the model. Leveraging this knowledge, we propose and successfully test an alternative parameterisation of the SEIR model that produces accurate[R] 0 estimates regardless of the distribution of the epidemiological delays, at the expense of biases in other quantities deemed of lesser importance. We further explore this approachs robustness by testing various transmissibility levels, generation times, and data fidelity (overdispersion). Finally, we apply the proposed approach to data from the 1918 influenza pandemic. We anticipate that this work will mitigate biases in estimating[R] 0.

ecology↗

bioRxiv2020

Remdesivir-Ivermectin combination displays synergistic interaction with improved in vitro antiviral activity against SARS-CoV-2

A key element for the prevention and management of COVID-19 is the development of effective therapeutics. Drug combination strategies of repurposed drugs offer several advantages over monotherapies, including the potential to achieve greater efficacy, the potential to increase the therapeutic index of drugs and the potential to reduce the emergence of drug resistance. Here, we report on the in vitro synergistic interaction between two FDA approved drugs, remdesivir and ivermectin resulting in enhanced antiviral activity against SARS-CoV-2. These findings warrant further investigations into the clinical potential of this combination, together with studies to define the underlying mechanism.

microbiology↗

bioRxiv2018

Unsupervised Extraction of Epidemic Syndromes from Participatory Influenza Surveillance Self-reported Symptoms

Seasonal influenza surveillance is usually carried out by sentinel general practitioners who compile weekly reports based on the number of influenza-like illness (ILI) clinical cases observed among visited patients. This practice for surveillance is generally affected by two main issues: i) reports are usually released with a lag of about one week or more, ii) the definition of a case of influenza-like illness based on patients symptoms varies from one surveillance system to the other, i.e. from one country to the other. The availability of novel data streams for disease surveillance can alleviate these issues; in this paper, we employed data from Influenzanet, a participatory web-based surveillance project which collects symptoms directly from the general population in real time. We developed an unsupervised probabilistic framework that combines time series analysis of symptoms counts and performs an algorithmic detection of groups of symptoms, hereafter called syndromes. Symptoms counts were collected through the participatory web-based surveillance platforms of a consortium called Influenzanet which is found to correlate with Influenza-like illness incidence as detected by sentinel doctors. Our aim is to suggest how web-based surveillance data can provide an epidemiological signal capable of detecting influenza-like illness temporal trends without relying on a specific case definition. We evaluated the performance of our framework by showing that the temporal trends of the detected syndromes closely follow the ILI incidence as reported by the traditional surveillance, and consist of combinations of symptoms that are compatible with the ILI definition. The proposed framework was able to predict quite accurately the ILI trend of the forthcoming influenza season based only on the available information of the previous years. Moreover, we assessed the generalisability of the approach by evaluating its potentials for the detection of gastrointestinal syndromes. We evaluated the approach against the traditional surveillance data and despite the limited amount of data, the gastrointestinal trend was successfully detected. The result is a real-time flexible surveillance and prediction tool that is not constrained by any disease case definition.\n\nAuthor summaryThis study suggests how web-based surveillance data can provide an epidemiological signal capable of detecting influenza-like illness temporal trends without relying on a specific case definition. The proposed framework was able to predict quite accurately the ILI trend of the forthcoming influenza season based only on the available information of the previous years. Moreover, we assessed the generalisability of the approach by evaluating its potentials for the detection of gastrointestinal syndromes. We evaluated the approach against the traditional surveillance data and despite the limited amount of data, the gastrointestinal trend was successfully detected. The result is a real-time flexible surveillance and prediction tool that is not constrained by any disease case definition.

epidemiology↗