Biology subjects

Kutzer, M. A. M.

Publications and source records attributed to Kutzer, M. A. M..

3 recordsLinked to original sources

bioRxiv2025

Detecting infection-related mortality using dynamical statistical indicators of high-resolution activity time series

Predicting transitions between health, disease, and death across biological systems remains an important challenge with significant implications for both ecological management and medical intervention. Although the principles underlying these transitions are increasingly recognised, accurate and tractable dynamical indicators of health-to-disease transitions remain rare, especially at the level of individual organisms. Here, we use dynamical statistical indicators of high-resolution activity time series to predict infection-related mortality. By analysing locomotor activity data from infected Drosophila melanogaster flies, we find that individual dynamical indicators, such as the mean, variance, autocorrelation, and permutation entropy, differed between flies that survived and those that died during the experiment. When these indicators were used to train a Random Forest model, the classifier performed well (AUC = 0.94), demonstrating an accuracy of 87.9% in discriminating between infected flies that would die from infection and those that would survive, with the strongest discriminatory power detected over 12 hours prior to death. Our findings show that combining these easy-to-compute, dynamical statistical indicators with machine learning enhances the ability to predict health deterioration in the Drosophila model. Conceptually, our findings emphasize that the integration of dynamical statistical metrics from physiological or behavioural time-series with machine learning approaches may offer a promising avenue for real-time health monitoring in both ecological and clinical settings. HighlightsO_LIHigh-resolution locomotor activity time series distinguish infected flies that live or die C_LIO_LISimple dynamical indicators (mean, SD, CV, lag-1 autocorrelation) jointly improve outcome discrimination C_LIO_LIPermutation entropy declines over time and is reduced in flies approaching acute infection-related death C_LIO_LIRandom Forest models classify infection survival outcomes with high accuracy (AUC 0.94) C_LIO_LIDivergence in activity dynamics is detectable over 12-16 hours before death in flies that succumb to infection C_LI

ecology↗

bioRxiv2024

A Decade of Progress: Insights of Open Data Practices in Biosciences at the University of Edinburgh

Open science promotes the accessibility of scientific research and data, emphasising transparency, reproducibility, and collaboration. This study assesses the openness and FAIRness (Findable, Accessible, Interoperable, and Reusable) of data-sharing practices within the biosciences at the University of Edinburgh from 2014 to 2023. We analysed 555 research papers across biotechnology, regenerative medicine, infectious diseases, and non-communicable diseases. Our scoring system evaluated data completeness, reusability, accessibility, and licensing, finding a progressive shift towards better data-sharing practices. The fraction of publications that share all relevant data increased significantly, from 7% in 2014 to 45% in 2023. Data involving genomic sequences were shared more frequently than image data or data on human subjects or samples. The presence of data availability statement (DAS) or preprint sharing correlated with more and better data sharing, particularly in terms of completeness. We discuss local and systemic factors underlying the current and future Open data sharing. Evaluating the automated ODDPub (Open Data Detection in Publications) tool on this manually-scored dataset demonstrated high specificity in identifying cases where no data was shared. ODDPub sensitivity improved with better documentation in the DAS. This positive trend highlights improvements in data-sharing, advocating for continued advances and addressing challenges with data types and documentation.

bioinformatics↗

bioRxiv2023

Mitochondrial background can explain variable costs of immune deployment

Organismal health and survival depend on the ability to mount an effective immune response against infection. Yet, immune defence may be energy-demanding, resulting in fitness costs if investment in immune function deprives other physiological processes of resources. While evidence of costly immunity resulting in reduced longevity and reproduction is common, the role of energy-producing mitochondria on the magnitude of these costs is unknown. Here we employed Drosophila melanogaster cybrid lines, where several mitochondrial genotypes (mitotypes) were introgressed onto a single nuclear genetic background, to explicitly test the role of mitochondrial variation on the costs of immune stimulation. We exposed female flies carrying one of nine distinct mitotypes to either a benign, heat-killed bacterial pathogen (stimulating immune deployment while avoiding pathology), or to a sterile control, and measured lifespan, fecundity, and locomotor activity. We observed mitotype-specific costs of immune stimulation and identified a positive genetic correlation between lifespan and the proportion of time cybrids spent moving while alive. Our results suggests that costs of immunity are highly variable depending on the mitochondrial genome, adding to a growing body of work highlighting the important role of mitochondrial variation in host-pathogen interactions.

evolutionary biology↗