More Like this

1. Within-host priority effects and epidemic timing determine outbreak severity in co-infected populations

   https://doi.org/10.1098/rspb.2020.0046  

   Clay, Patrick A.; Duffy, Meghan A.; Rudolf, Volker H. (March 2020, Proceedings of the Royal Society B: Biological Sciences)

   Co-infections of hosts by multiple pathogen species are ubiquitous, but predicting their impact on disease remains challenging. Interactions between co-infecting pathogens within hosts can alter pathogen transmission, with the impact on transmission typically dependent on the relative arrival order of pathogens within hosts (within-host priority effects). However, it is unclear how these within-host priority effects influence multi-pathogen epidemics, particularly when the arrival order of pathogens at the host-population scale varies. Here, we combined models and experiments with zooplankton and their naturally co-occurring fungal and bacterial pathogens to examine how within-host priority effects influence multi-pathogen epidemics. Epidemiological models parametrized with within-host priority effects measured at the single-host scale predicted that advancing the start date of bacterial epidemics relative to fungal epidemics would decrease the mean bacterial prevalence in a multi-pathogen setting, while models without within-host priority effects predicted the opposite effect. We tested these predictions with experimental multi-pathogen epidemics. Empirical dynamics matched predictions from the model including within-host priority effects, providing evidence that within-host priority effects influenced epidemic dynamics. Overall, within-host priority effects may be a key element of predicting multi-pathogen epidemic dynamics in the future, particularly as shifting disease phenology alters the order of infection within hosts. 

   more » « less
2. Summarizing the solution space in tumor phylogeny inference by multiple consensus trees

   https://doi.org/10.1093/bioinformatics/btz312  

   Aguse, Nuraini; Qi, Yuanyuan; El-Kebir, Mohammed (July 2019, Bioinformatics)

   Abstract MotivationCancer phylogenies are key to studying tumorigenesis and have clinical implications. Due to the heterogeneous nature of cancer and limitations in current sequencing technology, current cancer phylogeny inference methods identify a large solution space of plausible phylogenies. To facilitate further downstream analyses, methods that accurately summarize such a set T of cancer phylogenies are imperative. However, current summary methods are limited to a single consensus tree or graph and may miss important topological features that are present in different subsets of candidate trees. ResultsWe introduce the Multiple Consensus Tree (MCT) problem to simultaneously cluster T and infer a consensus tree for each cluster. We show that MCT is NP-hard, and present an exact algorithm based on mixed integer linear programming (MILP). In addition, we introduce a heuristic algorithm that efficiently identifies high-quality consensus trees, recovering all optimal solutions identified by the MILP in simulated data at a fraction of the time. We demonstrate the applicability of our methods on both simulated and real data, showing that our approach selects the number of clusters depending on the complexity of the solution space T. Availability and implementationhttps://github.com/elkebir-group/MCT. Supplementary informationSupplementary data are available at Bioinformatics online. 

   more » « less
3. Risk of death during acute infection is accelerating across diverse host-pathogen systems and consistent with multiple models of host-pathogen interaction

   https://doi.org/10.1128/msphere.00953-24  

   O'Sullivan, Tim; Karakoç, Canan; Wollein_Waldetoft, Kristofer; Brown, Sam P (May 2025, mSphere)

   Buchler, Nicolas E (Ed.)

   ABSTRACT Infectious diseases remain a major cause of global mortality, yet basic questions concerning the relationship between within-host processes governing pathogen burden (pathogen replication, immune responses) and population-scale (epidemiological) patterns of mortality remain obscure. We use a structured literature review to leverage the extensive biomedical data generated by controlled host infections to address the epidemiological question of whether infection-induced mortality is constant, accelerating, or follows some other pattern of change and to infer the within-host mechanistic basis of this pattern. We show that across diverse lethal infection models, the risk of death increases approximately exponentially in time since infection, in a manner phenomenologically similar to the dynamics of all-cause death. We further show that this pattern of accelerating risk is consistent with multiple alternate mechanisms of pathogen growth and host-pathogen interaction, underlining the limitations of current experimental approaches to connect within-host processes to epidemiological patterns. We review critical experimental questions that our work highlights, requiring additional non-invasive data on pathogen burden throughout the course of infection.IMPORTANCEHere, we ask a simple question: what are the dynamics of pathogen-induced death? Death is a central phenotype in both biomedical and epidemiological infectious disease biology, yet very little work has attempted to link the biomedical focus on pathogen dynamics within a host and the epidemiological focus on populations of infected hosts. To systematically characterize the dynamics of death in controlled animal infections, we analyzed 209 data sets spanning diverse lethal animal infection models. Across experimental models, we find robust support for an accelerating risk of death since the time of infection, contrasting with conventional epidemiological models that assume a constant elevated risk of death. Using math models, we show that multiple processes of growth and virulence are consistent with accelerating risk of death, and we end with a discussion of critical experiments to resolve how within-host biomedical processes map onto epidemiological patterns of disease. 

   more » « less
4. Season of death, pathogen persistence and wildlife behaviour alter number of anthrax secondary infections from environmental reservoirs

   https://doi.org/10.1098/rspb.2023.2568  

   Dolfi, Amélie C; Kausrud, Kyrre; Rysava, Kristyna; Champagne, Celeste; Huang, Yen-Hua; Barandongo, Zoe R; Turner, Wendy C (February 2024, Proceedings of the Royal Society B: Biological Sciences)

   An important part of infectious disease management is predicting factors that influence disease outbreaks, such asR, the number of secondary infections arising from an infected individual. EstimatingRis particularly challenging for environmentally transmitted pathogens given time lags between cases and subsequent infections. Here, we calculatedRforBacillus anthracisinfections arising from anthrax carcass sites in Etosha National Park, Namibia. Combining host behavioural data, pathogen concentrations and simulation models, we show thatRis spatially and temporally variable, driven by spore concentrations at death, host visitation rates and early preference for foraging at infectious sites. While spores were detected up to a decade after death, most secondary infections occurred within 2 years. Transmission simulations under scenarios combining site infectiousness and host exposure risk under different environmental conditions led to dramatically different outbreak dynamics, from pathogen extinction (R< 1) to explosive outbreaks (R> 10). These transmission heterogeneities may explain variation in anthrax outbreak dynamics observed globally, and more generally, the critical importance of environmental variation underlying host–pathogen interactions. Notably, our approach allowed us to estimate the lethal dose of a highly virulent pathogen non-invasively from observational studies and epidemiological data, useful when experiments on wildlife are undesirable or impractical. 

   more » « less
5. Accurate and efficient cell lineage tree inference from noisy single cell data: the maximum likelihood perfect phylogeny approach

   https://doi.org/10.1093/bioinformatics/btz676  

   Wu, Yufeng (August 2019, Bioinformatics)

   Schwartz, Russell (Ed.)

   Abstract Motivation Cells in an organism share a common evolutionary history, called cell lineage tree. Cell lineage tree can be inferred from single cell genotypes at genomic variation sites. Cell lineage tree inference from noisy single cell data is a challenging computational problem. Most existing methods for cell lineage tree inference assume uniform uncertainty in genotypes. A key missing aspect is that real single cell data usually has non-uniform uncertainty in individual genotypes. Moreover, existing methods are often sampling based and can be very slow for large data. Results In this article, we propose a new method called ScisTree, which infers cell lineage tree and calls genotypes from noisy single cell genotype data. Different from most existing approaches, ScisTree works with genotype probabilities of individual genotypes (which can be computed by existing single cell genotype callers). ScisTree assumes the infinite sites model. Given uncertain genotypes with individualized probabilities, ScisTree implements a fast heuristic for inferring cell lineage tree and calling the genotypes that allow the so-called perfect phylogeny and maximize the likelihood of the genotypes. Through simulation, we show that ScisTree performs well on the accuracy of inferred trees, and is much more efficient than existing methods. The efficiency of ScisTree enables new applications including imputation of the so-called doublets. Availability and implementation The program ScisTree is available for download at: https://github.com/yufengwudcs/ScisTree. Supplementary information Supplementary data are available at Bioinformatics online. 

   more » « less