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  1. Article ; Online: Estimating the reproduction number and transmission heterogeneity from the size distribution of clusters of identical pathogen sequences.

    Tran-Kiem, Cécile / Bedford, Trevor

    Proceedings of the National Academy of Sciences of the United States of America

    2024  Volume 121, Issue 15, Page(s) e2305299121

    Abstract: Quantifying transmission intensity and heterogeneity is crucial to ascertain the threat posed by infectious diseases and inform the design of interventions. Methods that jointly estimate the reproduction ... ...

    Abstract Quantifying transmission intensity and heterogeneity is crucial to ascertain the threat posed by infectious diseases and inform the design of interventions. Methods that jointly estimate the reproduction number
    MeSH term(s) Humans ; Phylogeny ; Communicable Diseases ; Contact Tracing
    Language English
    Publishing date 2024-04-03
    Publishing country United States
    Document type Journal Article
    ZDB-ID 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.2305299121
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1148: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  2. Article ; Online: An atlas of continuous adaptive evolution in endemic human viruses.

    Kistler, Kathryn E / Bedford, Trevor

    Cell host & microbe

    2023  Volume 31, Issue 11, Page(s) 1898–1909.e3

    Abstract: Through antigenic evolution, viruses such as seasonal influenza evade recognition by neutralizing antibodies. This means that a person with antibodies well tuned to an initial infection will not be protected against the same virus years later and that ... ...

    Abstract Through antigenic evolution, viruses such as seasonal influenza evade recognition by neutralizing antibodies. This means that a person with antibodies well tuned to an initial infection will not be protected against the same virus years later and that vaccine-mediated protection will decay. To expand our understanding of which endemic human viruses evolve in this fashion, we assess adaptive evolution across the genome of 28 endemic viruses spanning a wide range of viral families and transmission modes. Surface proteins consistently show the highest rates of adaptation, and ten viruses in this panel are estimated to undergo antigenic evolution to selectively fix mutations that enable the escape of prior immunity. Thus, antibody evasion is not an uncommon evolutionary strategy among human viruses, and monitoring this evolution will inform future vaccine efforts. Additionally, by comparing overall amino acid substitution rates, we show that SARS-CoV-2 is accumulating protein-coding changes at substantially faster rates than endemic viruses.
    MeSH term(s) Humans ; Influenza, Human ; Antibodies, Neutralizing/genetics ; Mutation ; Influenza Vaccines ; SARS-CoV-2/genetics ; Antibodies, Viral ; Hemagglutinin Glycoproteins, Influenza Virus
    Chemical Substances Antibodies, Neutralizing ; Influenza Vaccines ; Antibodies, Viral ; Hemagglutinin Glycoproteins, Influenza Virus
    Language English
    Publishing date 2023-10-25
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2278004-X
    ISSN 1934-6069 ; 1931-3128
    ISSN (online) 1934-6069
    ISSN 1931-3128
    DOI 10.1016/j.chom.2023.09.012
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6578: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  3. Article: Fitness models provide accurate short-term forecasts of SARS-CoV-2 variant frequency.

    Abousamra, Eslam / Figgins, Marlin / Bedford, Trevor

    medRxiv : the preprint server for health sciences

    2024  

    Abstract: Genomic surveillance of pathogen evolution is essential for public health response, treatment strategies, and vaccine development. In the context of SARS-COV-2, multiple models have been developed including Multinomial Logistic Regression (MLR) ... ...

    Abstract Genomic surveillance of pathogen evolution is essential for public health response, treatment strategies, and vaccine development. In the context of SARS-COV-2, multiple models have been developed including Multinomial Logistic Regression (MLR) describing variant frequency growth as well as Fixed Growth Advantage (FGA), Growth Advantage Random Walk (GARW) and Piantham parameterizations describing variant
    Language English
    Publishing date 2024-01-22
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.11.30.23299240
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Correction: Evidence for adaptive evolution in the receptor-binding domain of seasonal coronaviruses OC43 and 229e.

    Kistler, Kathryn E / Bedford, Trevor

    eLife

    2022  Volume 11

    Language English
    Publishing date 2022-09-14
    Publishing country England
    Document type Published Erratum
    ZDB-ID 2687154-3
    ISSN 2050-084X ; 2050-084X
    ISSN (online) 2050-084X
    ISSN 2050-084X
    DOI 10.7554/eLife.83277
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  5. Article: MASCOT-Skyline integrates population and migration dynamics to enhance phylogeographic reconstructions.

    Müller, Nicola F / Bouckaert, Remco R / Wu, Chieh-Hsi / Bedford, Trevor

    bioRxiv : the preprint server for biology

    2024  

    Abstract: The spread of infectious diseases is shaped by spatial and temporal aspects, such as host population structure or changes in the transmission rate or number of infected individuals over time. These spatiotemporal dynamics are imprinted in the genome of ... ...

    Abstract The spread of infectious diseases is shaped by spatial and temporal aspects, such as host population structure or changes in the transmission rate or number of infected individuals over time. These spatiotemporal dynamics are imprinted in the genome of pathogens and can be recovered from those genomes using phylodynamics methods. However, phylodynamic methods typically quantify either the temporal or spatial transmission dynamics, which leads to unclear biases, as one can potentially not be inferred without the other. Here, we address this challenge by introducing a structured coalescent skyline approach, MASCOT-Skyline that allows us to jointly infer spatial and temporal transmission dynamics of infectious diseases using Markov chain Monte Carlo inference. To do so, we model the effective population size dynamics in different locations using a non-parametric function, allowing us to approximate a range of population size dynamics. We show, using a range of different viral outbreak datasets, potential issues with phylogeographic methods. We then use these viral datasets to motivate simulations of outbreaks that illuminate the nature of biases present in the different phylogeographic methods. We show that spatial and temporal dynamics should be modeled jointly even if one seeks to recover just one of the two. Further, we showcase conditions under which we can expect phylogeographic analyses to be biased, particularly different subsampling approaches, as well as provide recommendations of when we can expect them to perform well. We implemented MASCOT-Skyline as part of the open-source software package MASCOT for the Bayesian phylodynamics platform BEAST2.
    Language English
    Publishing date 2024-03-13
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2024.03.06.583734
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: An Atlas of Adaptive Evolution in Endemic Human Viruses

    Kistler, Kathryn E. / Bedford, Trevor

    bioRxiv

    Abstract: Through antigenic evolution, viruses like seasonal influenza evade recognition by neutralizing antibodies elicited by previous infection or vaccination. This means that a person with antibodies well-tuned to an initial infection will not be protected ... ...

    Abstract Through antigenic evolution, viruses like seasonal influenza evade recognition by neutralizing antibodies elicited by previous infection or vaccination. This means that a person with antibodies well-tuned to an initial infection will not be protected against the same virus years later and that vaccine-mediated protection will decay. It is not fully understood which of the many endemic human viruses evolve in this fashion. To expand that knowledge, we assess adaptive evolution across the viral genome in 28 endemic viruses, spanning a wide range of viral families and transmission modes. We find that surface proteins consistently show the highest rates of adaptation, and estimate that ten viruses in this panel undergo antigenic evolution to selectively fix mutations that enable the virus to escape recognition by prior immunity. We compare overall rates of amino acid substitution between these antigenically-evolving viruses and SARS-CoV-2, showing that SARS-CoV-2 viruses are accumulating protein-coding changes at substantially faster rates than these endemic viruses.
    Keywords covid19
    Language English
    Publishing date 2023-05-22
    Publisher Cold Spring Harbor Laboratory
    Document type Article ; Online
    DOI 10.1101/2023.05.19.541367
    Database COVID19

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  7. Article ; Online: Dimensionality reduction distills complex evolutionary relationships in seasonal influenza and SARS-CoV-2

    Nanduri, Sravani / Black, Allison / Bedford, Trevor / Huddleston, John

    bioRxiv

    Abstract: Public health researchers and practitioners commonly infer phylogenies from viral genome sequences to understand transmission dynamics and identify clusters of genetically-related samples. However, viruses that reassort or recombine violate phylogenetic ... ...

    Abstract Public health researchers and practitioners commonly infer phylogenies from viral genome sequences to understand transmission dynamics and identify clusters of genetically-related samples. However, viruses that reassort or recombine violate phylogenetic assumptions and require more sophisticated methods. Even when phylogenies are appropriate, they can be unnecessary or difficult to interpret without specialty knowledge. For example, pairwise distances between sequences can be enough to identify clusters of related samples or assign new samples to existing phylogenetic clusters. In this work, we tested whether dimensionality reduction methods could capture known genetic groups within two human pathogenic viruses that cause substantial human morbidity and mortality and frequently reassort or recombine, respectively: seasonal influenza A/H3N2 and SARS-CoV-2. We applied principal component analysis (PCA), multidimensional scaling (MDS), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP) to sequences with well-defined phylogenetic clades and either reassortment (H3N2) or recombination (SARS-CoV-2). For each low-dimensional embedding of sequences, we calculated the correlation between pairwise genetic and Euclidean distances in the embedding and applied a hierarchical clustering method to identify clusters in the embedding. We measured the accuracy of clusters compared to previously defined phylogenetic clades, reassortment clusters, or recombinant lineages. We found that MDS maintained the strongest correlation between pairwise genetic and Euclidean distances between sequences and best captured the intermediate placement of recombinant lineages between parental lineages. Clusters from t-SNE most accurately recapitulated known phylogenetic clades and recombinant lineages. Both MDS and t-SNE accurately identified reassortment groups. We show that simple statistical methods without a biological model can accurately represent known genetic relationships for relevant human pathogenic viruses. Our open source implementation of these methods for analysis of viral genome sequences can be easily applied when phylogenetic methods are either unnecessary or inappropriate.
    Keywords covid19
    Language English
    Publishing date 2024-02-08
    Publisher Cold Spring Harbor Laboratory
    Document type Article ; Online
    DOI 10.1101/2024.02.07.579374
    Database COVID19

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  8. Article ; Online: Evidence for adaptive evolution in the receptor-binding domain of seasonal coronaviruses OC43 and 229e.

    Kistler, Kathryn E / Bedford, Trevor

    eLife

    2021  Volume 10

    Abstract: Seasonal coronaviruses (OC43, 229E, NL63, and HKU1) are endemic to the human population, regularly infecting and reinfecting humans while typically causing asymptomatic to mild respiratory infections. It is not known to what extent reinfection by these ... ...

    Abstract Seasonal coronaviruses (OC43, 229E, NL63, and HKU1) are endemic to the human population, regularly infecting and reinfecting humans while typically causing asymptomatic to mild respiratory infections. It is not known to what extent reinfection by these viruses is due to waning immune memory or antigenic drift of the viruses. Here we address the influence of antigenic drift on immune evasion of seasonal coronaviruses. We provide evidence that at least two of these viruses, OC43 and 229E, are undergoing adaptive evolution in regions of the viral spike protein that are exposed to human humoral immunity. This suggests that reinfection may be due, in part, to positively selected genetic changes in these viruses that enable them to escape recognition by the immune system. It is possible that, as with seasonal influenza, these adaptive changes in antigenic regions of the virus would necessitate continual reformulation of a vaccine made against them.
    MeSH term(s) Adaptation, Physiological/genetics ; Antigens, Viral/genetics ; Biological Evolution ; Computer Simulation ; Coronavirus/genetics ; Coronavirus/metabolism ; Gene Expression Regulation, Viral ; Humans ; Influenza A virus/genetics ; Measles virus/genetics ; Phylogeny ; Seasons
    Chemical Substances Antigens, Viral
    Language English
    Publishing date 2021-01-19
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2687154-3
    ISSN 2050-084X ; 2050-084X
    ISSN (online) 2050-084X
    ISSN 2050-084X
    DOI 10.7554/eLife.64509
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article: Fitting stochastic epidemic models to gene genealogies using linear noise approximation.

    Tang, Mingwei / Dudas, Gytis / Bedford, Trevor / Minin, Vladimir N

    The annals of applied statistics

    2023  Volume 17, Issue 1, Page(s) 1–22

    Abstract: Phylodynamics is a set of population genetics tools that aim at reconstructing demographic history of a population based on molecular sequences of individuals sampled from the population of interest. One important task in phylodynamics is to estimate ... ...

    Abstract Phylodynamics is a set of population genetics tools that aim at reconstructing demographic history of a population based on molecular sequences of individuals sampled from the population of interest. One important task in phylodynamics is to estimate changes in (effective) population size. When applied to infectious disease sequences such estimation of population size trajectories can provide information about changes in the number of infections. To model changes in the number of infected individuals, current phylodynamic methods use non-parametric approaches (e.g., Bayesian curve-fitting based on change-point models or Gaussian process priors), parametric approaches (e.g., based on differential equations), and stochastic modeling in conjunction with likelihood-free Bayesian methods. The first class of methods yields results that are hard to interpret epidemiologically. The second class of methods provides estimates of important epidemiological parameters, such as infection and removal/recovery rates, but ignores variation in the dynamics of infectious disease spread. The third class of methods is the most advantageous statistically, but relies on computationally intensive particle filtering techniques that limits its applications. We propose a Bayesian model that combines phylodynamic inference and stochastic epidemic models, and achieves computational tractability by using a linear noise approximation (LNA) - a technique that allows us to approximate probability densities of stochastic epidemic model trajectories. LNA opens the door for using modern Markov chain Monte Carlo tools to approximate the joint posterior distribution of the disease transmission parameters and of high dimensional vectors describing unobserved changes in the stochastic epidemic model compartment sizes (e.g., numbers of infectious and susceptible individuals). In a simulation study, we show that our method can successfully recover parameters of stochastic epidemic models. We apply our estimation technique to Ebola genealogies estimated using viral genetic data from the 2014 epidemic in Sierra Leone and Liberia.
    Language English
    Publishing date 2023-01-24
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2376910-5
    ISSN 1941-7330 ; 1932-6157
    ISSN (online) 1941-7330
    ISSN 1932-6157
    DOI 10.1214/21-aoas1583
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: State-dependent evolutionary models reveal modes of solid tumour growth.

    Lewinsohn, Maya A / Bedford, Trevor / Müller, Nicola F / Feder, Alison F

    Nature ecology & evolution

    2023  Volume 7, Issue 4, Page(s) 581–596

    Abstract: Spatial properties of tumour growth have profound implications for cancer progression, therapeutic resistance and metastasis. Yet, how spatial position governs tumour cell division remains difficult to evaluate in clinical tumours. Here, we demonstrate ... ...

    Abstract Spatial properties of tumour growth have profound implications for cancer progression, therapeutic resistance and metastasis. Yet, how spatial position governs tumour cell division remains difficult to evaluate in clinical tumours. Here, we demonstrate that faster division on the tumour periphery leaves characteristic genetic patterns, which become evident when a phylogenetic tree is reconstructed from spatially sampled cells. Namely, rapidly dividing peripheral lineages branch more extensively and acquire more mutations than slower-dividing centre lineages. We develop a Bayesian state-dependent evolutionary phylodynamic model (SDevo) that quantifies these patterns to infer the differential division rates between peripheral and central cells. We demonstrate that this approach accurately infers spatially varying birth rates of simulated tumours across a range of growth conditions and sampling strategies. We then show that SDevo outperforms state-of-the-art, non-cancer multi-state phylodynamic methods that ignore differential sequence evolution. Finally, we apply SDevo to single-time-point, multi-region sequencing data from clinical hepatocellular carcinomas and find evidence of a three- to six-times-higher division rate on the tumour edge. With the increasing availability of high-resolution, multi-region sequencing, we anticipate that SDevo will be useful in interrogating spatial growth restrictions and could be extended to model non-spatial factors that influence tumour progression.
    MeSH term(s) Humans ; Phylogeny ; Bayes Theorem ; Neoplasms/genetics
    Language English
    Publishing date 2023-03-09
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
    ISSN 2397-334X
    ISSN (online) 2397-334X
    DOI 10.1038/s41559-023-02000-4
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