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Article ; Online: Epidemiological inference for emerging viruses using segregating sites.

Park, Yeongseon / Martin, Michael A / Koelle, Katia

2023 Volume 14, Issue 1, Page(s) 3105

Abstract: Epidemiological models are commonly fit to case and pathogen sequence data to estimate parameters and to infer unobserved disease dynamics. Here, we present an inference approach based on sequence data that is well suited for model fitting early on ... ...

Abstract	Epidemiological models are commonly fit to case and pathogen sequence data to estimate parameters and to infer unobserved disease dynamics. Here, we present an inference approach based on sequence data that is well suited for model fitting early on during the expansion of a viral lineage. Our approach relies on a trajectory of segregating sites to infer epidemiological parameters within a Sequential Monte Carlo framework. Using simulated data, we first show that our approach accurately recovers key epidemiological quantities under a single-introduction scenario. We then apply our approach to SARS-CoV-2 sequence data from France, estimating a basic reproduction number of approximately 2.3-2.7 under an epidemiological model that allows for multiple introductions. Our approach presented here indicates that inference approaches that rely on simple population genetic summary statistics can be informative of epidemiological parameters and can be used for reconstructing infectious disease dynamics during the early expansion of a viral lineage.
MeSH term(s)	Humans ; COVID-19/epidemiology ; SARS-CoV-2/genetics ; Viruses/genetics ; Communicable Diseases ; Basic Reproduction Number ; Bayes Theorem
Language	English
Publishing date	2023-05-29
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-023-38809-7
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Comment on "Genomic epidemiology of superspreading events in Austria reveals mutational dynamics and transmission properties of SARS-CoV-2".

Martin, Michael A / Koelle, Katia

Science translational medicine

2021 Volume 13, Issue 617, Page(s) eabh1803

Abstract: A reanalysis of SARS-CoV-2 deep sequencing data from donor-recipient pairs indicates that transmission bottlenecks are very narrow (one to three virions). ...

Abstract	A reanalysis of SARS-CoV-2 deep sequencing data from donor-recipient pairs indicates that transmission bottlenecks are very narrow (one to three virions).
MeSH term(s)	Austria ; COVID-19 ; Genomics ; Humans ; Mutation/genetics ; SARS-CoV-2
Language	English
Publishing date	2021-10-27
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2518854-9
ISSN	1946-6242 ; 1946-6234
ISSN (online)	1946-6242
ISSN	1946-6234
DOI	10.1126/scitranslmed.abh1803
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Zs.A 6941: Show issues

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Article ; Online: Transmission Bottleneck Size Estimation from De Novo Viral Genetic Variation.

Shi, Yike Teresa / Harris, Jeremy D / Martin, Michael A / Koelle, Katia

Molecular biology and evolution

2023 Volume 41, Issue 1

Abstract: Sequencing of viral infections has become increasingly common over the last decade. Deep sequencing data in particular have proven useful in characterizing the roles that genetic drift and natural selection play in shaping within-host viral populations. ... ...

Abstract	Sequencing of viral infections has become increasingly common over the last decade. Deep sequencing data in particular have proven useful in characterizing the roles that genetic drift and natural selection play in shaping within-host viral populations. They have also been used to estimate transmission bottleneck sizes from identified donor-recipient pairs. These bottleneck sizes quantify the number of viral particles that establish genetic lineages in the recipient host and are important to estimate due to their impact on viral evolution. Current approaches for estimating bottleneck sizes exclusively consider the subset of viral sites that are observed as polymorphic in the donor individual. However, these approaches have the potential to substantially underestimate true transmission bottleneck sizes. Here, we present a new statistical approach for instead estimating bottleneck sizes using patterns of viral genetic variation that arise de novo within a recipient individual. Specifically, our approach makes use of the number of clonal viral variants observed in a transmission pair, defined as the number of viral sites that are monomorphic in both the donor and the recipient but carry different alleles. We first test our approach on a simulated dataset and then apply it to both influenza A virus sequence data and SARS-CoV-2 sequence data from identified transmission pairs. Our results confirm the existence of extremely tight transmission bottlenecks for these 2 respiratory viruses.
MeSH term(s)	Genetic Drift ; Influenza A virus/genetics ; Selection, Genetic ; Genetic Variation
Language	English
Publishing date	2023-12-29
Publishing country	United States
Document type	Journal Article
ZDB-ID	998579-7
ISSN	1537-1719 ; 0737-4038
ISSN (online)	1537-1719
ISSN	0737-4038
DOI	10.1093/molbev/msad286
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Zs.A 2137: Show issues

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Article: Transmission bottleneck size estimation from

Shi, Teresa / Harris, Jeremy D / Martin, Michael A / Koelle, Katia

bioRxiv : the preprint server for biology

2023

Abstract	Sequencing of viral infections has become increasingly common over the last decade. Deep sequencing data in particular have proven useful in characterizing the roles that genetic drift and natural selection play in shaping within-host viral populations. They have also been used to estimate transmission bottleneck sizes from identified donor-recipient pairs. These bottleneck sizes quantify the number of viral particles that establish genetic lineages in the recipient host and are important to estimate due to their impact on viral evolution. Current approaches for estimating bottleneck sizes exclusively consider the subset of viral sites that are observed as polymorphic in the donor individual. However, allele frequencies can change dramatically over the course of an individual's infection, such that sites that are polymorphic in the donor at the time of transmission may not be polymorphic in the donor at the time of sampling and allele frequencies at donor-polymorphic sites may change dramatically over the course of a recipient's infection. Because of this, transmission bottleneck sizes estimated using allele frequencies observed at a donor's polymorphic sites may be considerable underestimates of true bottleneck sizes. Here, we present a new statistical approach for instead estimating bottleneck sizes using patterns of viral genetic variation that arose
Language	English
Publishing date	2023-08-14
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.08.14.553219
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Article ; Online: Heterogeneity in viral populations increases the rate of deleterious mutation accumulation.

Allman, Brent / Koelle, Katia / Weissman, Daniel

Genetics

2022 Volume 222, Issue 2

Abstract: RNA viruses have high mutation rates, with the majority of mutations being deleterious. We examine patterns of deleterious mutation accumulation over multiple rounds of viral replication, with a focus on how cellular coinfection and heterogeneity in ... ...

Abstract	RNA viruses have high mutation rates, with the majority of mutations being deleterious. We examine patterns of deleterious mutation accumulation over multiple rounds of viral replication, with a focus on how cellular coinfection and heterogeneity in viral output affect these patterns. Specifically, using agent-based intercellular simulations we find, in agreement with previous studies, that coinfection of cells by viruses relaxes the strength of purifying selection and thereby increases the rate of deleterious mutation accumulation. We further find that cellular heterogeneity in viral output exacerbates the rate of deleterious mutation accumulation, regardless of whether this heterogeneity in viral output is stochastic or is due to variation in the cellular multiplicity of infection. These results highlight the need to consider the unique life histories of viruses and their population structure to better understand observed patterns of viral evolution.
MeSH term(s)	Coinfection ; Models, Genetic ; Mutation ; Mutation Accumulation ; Mutation Rate ; RNA Viruses/genetics ; Selection, Genetic
Language	English
Publishing date	2022-08-22
Publishing country	United States
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, Non-U.S. Gov't
ZDB-ID	2167-2
ISSN	1943-2631 ; 0016-6731
ISSN (online)	1943-2631
ISSN	0016-6731
DOI	10.1093/genetics/iyac127
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Article ; Online: Fitness Estimation for Viral Variants in the Context of Cellular Coinfection.

Zhu, Huisheng / Allman, Brent E / Koelle, Katia

Viruses

2021 Volume 13, Issue 7

Abstract: Animal models are frequently used to characterize the within-host dynamics of emerging zoonotic viruses. More recent studies have also deep-sequenced longitudinal viral samples originating from experimental challenges to gain a better understanding of ... ...

Abstract	Animal models are frequently used to characterize the within-host dynamics of emerging zoonotic viruses. More recent studies have also deep-sequenced longitudinal viral samples originating from experimental challenges to gain a better understanding of how these viruses may evolve in vivo and between transmission events. These studies have often identified nucleotide variants that can replicate more efficiently within hosts and also transmit more effectively between hosts. Quantifying the degree to which a mutation impacts viral fitness within a host can improve identification of variants that are of particular epidemiological concern and our ability to anticipate viral adaptation at the population level. While methods have been developed to quantify the fitness effects of mutations using observed changes in allele frequencies over the course of a host's infection, none of the existing methods account for the possibility of cellular coinfection. Here, we develop mathematical models to project variant allele frequency changes in the context of cellular coinfection and, further, integrate these models with statistical inference approaches to demonstrate how variant fitness can be estimated alongside cellular multiplicity of infection. We apply our approaches to empirical longitudinally sampled H5N1 sequence data from ferrets. Our results indicate that previous studies may have significantly underestimated the within-host fitness advantage of viral variants. These findings underscore the importance of considering the process of cellular coinfection when studying within-host viral evolutionary dynamics.
MeSH term(s)	Animals ; Coinfection/virology ; Evolution, Molecular ; Ferrets ; Gene Frequency ; Genetic Fitness ; Humans ; Influenza A Virus, H5N1 Subtype/genetics ; Models, Genetic ; Mutation ; Orthomyxoviridae Infections/virology
Language	English
Publishing date	2021-06-23
Publishing country	Switzerland
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2516098-9
ISSN	1999-4915 ; 1999-4915
ISSN (online)	1999-4915
ISSN	1999-4915
DOI	10.3390/v13071216
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Insights from SARS-CoV-2 sequences.

Martin, Michael A / VanInsberghe, David / Koelle, Katia

Science (New York, N.Y.)

2021 Volume 371, Issue 6528, Page(s) 466–467

MeSH term(s)	Basic Reproduction Number ; COVID-19/epidemiology ; COVID-19/virology ; Evolution, Molecular ; Genome, Viral ; Humans ; Molecular Epidemiology ; Phylogeny ; Phylogeography ; SARS-CoV-2/genetics
Language	English
Publishing date	2021-01-28
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	128410-1
ISSN	1095-9203 ; 0036-8075
ISSN (online)	1095-9203
ISSN	0036-8075
DOI	10.1126/science.abf3995
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Zs.A 27: 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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Article: Semi-infectious particles contribute substantially to influenza virus within-host dynamics when infection is dominated by spatial structure.

Farrell, Alex / Phan, Tin / Brooke, Christopher B / Koelle, Katia / Ke, Ruian

Virus evolution

2023 Volume 9, Issue 1, Page(s) vead020

Abstract: Influenza is an ribonucleic acid virus with a genome that comprises eight segments. Experiments show that the vast majority of virions fail to express one or more gene segments and thus cannot cause a productive infection on their own. These particles, ... ...

Abstract	Influenza is an ribonucleic acid virus with a genome that comprises eight segments. Experiments show that the vast majority of virions fail to express one or more gene segments and thus cannot cause a productive infection on their own. These particles, called semi-infectious particles (SIPs), can induce virion production through complementation when multiple SIPs are present in an infected cell. Previous within-host influenza models did not explicitly consider SIPs and largely ignore the potential effects of coinfection during virus infection. Here, we constructed and analyzed two distinct models explicitly keeping track of SIPs and coinfection: one without spatial structure and the other implicitly considering spatial structure. While the model without spatial structure fails to reproduce key aspects of within-host influenza virus dynamics, we found that the model implicitly considering the spatial structure of the infection process makes predictions that are consistent with biological observations, highlighting the crucial role that spatial structure plays during an influenza infection. This model predicts two phases of viral growth prior to the viral peak: a first phase driven by fully infectious particles at the initiation of infection followed by a second phase largely driven by coinfections of fully infectious particles and SIPs. Fitting this model to two sets of data, we show that SIPs can contribute substantially to viral load during infection. Overall, the model provides a new interpretation of the
Language	English
Publishing date	2023-03-21
Publishing country	England
Document type	Journal Article
ZDB-ID	2818949-8
ISSN	2057-1577
ISSN	2057-1577
DOI	10.1093/ve/vead020
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Article: Fitness Estimation for Viral Variants in the Context of Cellular Coinfection

Zhu, Huisheng / Allman, Brent E. / Koelle, Katia

Viruses. 2021 June 23, v. 13, no. 7

2021

Abstract	Animal models are frequently used to characterize the within-host dynamics of emerging zoonotic viruses. More recent studies have also deep-sequenced longitudinal viral samples originating from experimental challenges to gain a better understanding of how these viruses may evolve in vivo and between transmission events. These studies have often identified nucleotide variants that can replicate more efficiently within hosts and also transmit more effectively between hosts. Quantifying the degree to which a mutation impacts viral fitness within a host can improve identification of variants that are of particular epidemiological concern and our ability to anticipate viral adaptation at the population level. While methods have been developed to quantify the fitness effects of mutations using observed changes in allele frequencies over the course of a host’s infection, none of the existing methods account for the possibility of cellular coinfection. Here, we develop mathematical models to project variant allele frequency changes in the context of cellular coinfection and, further, integrate these models with statistical inference approaches to demonstrate how variant fitness can be estimated alongside cellular multiplicity of infection. We apply our approaches to empirical longitudinally sampled H5N1 sequence data from ferrets. Our results indicate that previous studies may have significantly underestimated the within-host fitness advantage of viral variants. These findings underscore the importance of considering the process of cellular coinfection when studying within-host viral evolutionary dynamics.
Keywords	alleles ; gene frequency ; mixed infection ; mutation ; statistical inference
Language	English
Dates of publication	2021-0623
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2516098-9
ISSN	1999-4915
ISSN	1999-4915
DOI	10.3390/v13071216
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Reanalysis of deep-sequencing data from Austria points towards a small SARS-COV-2 transmission bottleneck on the order of one to three virions

Martin, Michael A. / Koelle, Katia

bioRxiv

Abstract: An early analysis of SARS-CoV-2 deep-sequencing data that combined epidemiological and genetic data to characterize the transmission dynamics of the virus in and beyond Austria concluded that the size of the virus9s transmission bottleneck was large, on ... ...

Abstract	An early analysis of SARS-CoV-2 deep-sequencing data that combined epidemiological and genetic data to characterize the transmission dynamics of the virus in and beyond Austria concluded that the size of the virus9s transmission bottleneck was large, on the order of 1000 virions. We performed new computational analyses using these deep-sequenced samples from Austria. Our analyses included characterization of transmission bottleneck sizes across a range of variant calling thresholds and examination of patterns of shared low-frequency variants between transmission pairs in cases where de novo genetic variation was present in the recipient. From these analyses, among others, we found that SARS-CoV-2 transmission bottlenecks are instead likely to be very tight, on the order of 1 to 3 virions. These findings have important consequences for understanding how SARS-CoV-2 evolves between hosts and the processes shaping genetic variation observed at the population level.
Keywords	covid19
Language	English
Publishing date	2021-02-22
Publisher	Cold Spring Harbor Laboratory
Document type	Article ; Online
DOI	10.1101/2021.02.22.432096
Database	COVID19

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