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Jones BR, Joy JB. Simulating within host human immunodeficiency virus 1 genome evolution in the persistent reservoir. Virus Evol 2020; 6:veaa089. [PMID: 34040795 PMCID: PMC8132731 DOI: 10.1093/ve/veaa089] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
The complexities of viral evolution can be difficult to elucidate. Software simulating viral evolution provides powerful tools for exploring hypotheses of viral systems, especially in situations where thorough empirical data are difficult to obtain or parameters of interest are difficult to measure. Human immunodeficiency virus 1 (HIV-1) infection has no durable cure; this is primarily due to the virus’ ability to integrate into the genome of host cells, where it can remain in a transcriptionally latent state. An effective cure strategy must eliminate every copy of HIV-1 in this ‘persistent reservoir’ because proviruses can reactivate, even decades later, to resume an active infection. However, many features of the persistent reservoir remain unclear, including the temporal dynamics of HIV-1 integration frequency and the longevity of the resulting reservoir. Thus, sophisticated analyses are required to measure these features and determine their temporal dynamics. Here, we present software that is an extension of SANTA-SIM to include multiple compartments of viral populations. We used the resulting software to create a model of HIV-1 within host evolution that incorporates the persistent HIV-1 reservoir. This model is composed of two compartments, an active compartment and a latent compartment. With this model, we compared five different date estimation methods (Closest Sequence, Clade, Linear Regression, Least Squares, and Maximum Likelihood) to recover the integration dates of genomes in our model’s HIV-1 reservoir. We found that the Least Squares method performed the best with the highest concordance (0.80) between real and estimated dates and the lowest absolute error (all pairwise t tests: P < 0.01). Our software is a useful tool for validating bioinformatics software and understanding the dynamics of the persistent HIV-1 reservoir.
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Affiliation(s)
- Bradley R Jones
- BC Centre for Excellence in HIV/AIDS, 608-1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
| | - Jeffrey B Joy
- BC Centre for Excellence in HIV/AIDS, 608-1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
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Jariani A, Warth C, Deforche K, Libin P, Drummond AJ, Rambaut A, Matsen Iv FA, Theys K. SANTA-SIM: simulating viral sequence evolution dynamics under selection and recombination. Virus Evol 2019; 5:vez003. [PMID: 30863552 PMCID: PMC6407609 DOI: 10.1093/ve/vez003] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Simulations are widely used to provide expectations and predictive distributions under known conditions against which to compare empirical data. Such simulations are also invaluable for testing and comparing the behaviour and power of inference methods. We describe SANTA-SIM, a software package to simulate the evolution of a population of gene sequences forwards through time. It models the underlying biological processes as discrete components: replication, recombination, point mutations, insertion-deletions, and selection under various fitness models and population size dynamics. The software is designed to be intuitive to work with for a wide range of users and executable in a cross-platform manner.
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Affiliation(s)
- Abbas Jariani
- Laboratory for Genetics and Genomics, Center of Microbial and Plant Genetics, KU Leuven, Leuven, Belgium.,Laboratory for Systems Biology, VIB, Leuven, Belgium
| | - Christopher Warth
- Program in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | | | - Pieter Libin
- Laboratory Clinical and Evolutionary Virology, Rega Institute for Medical Research - KU Leuven, Leuven, Belgium.,Artifical Intelligence Lab, Department of Computer Science, Vrije Universiteit Brussel, Brussels, Belgium
| | - Alexei J Drummond
- Centre for Computational Evolution, University of Auckland, Auckland, New Zealand.,Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule Zurich, Basel, Switzerland
| | - Andrew Rambaut
- Ashworth Laboratories, Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Frederick A Matsen Iv
- Program in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Kristof Theys
- Laboratory Clinical and Evolutionary Virology, Rega Institute for Medical Research - KU Leuven, Leuven, Belgium
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Bioinformatics Applications in Advancing Animal Virus Research. RECENT ADVANCES IN ANIMAL VIROLOGY 2019. [PMCID: PMC7121192 DOI: 10.1007/978-981-13-9073-9_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Viruses serve as infectious agents for all living entities. There have been various research groups that focus on understanding the viruses in terms of their host-viral relationships, pathogenesis and immune evasion. However, with the current advances in the field of science, now the research field has widened up at the ‘omics’ level. Apparently, generation of viral sequence data has been increasing. There are numerous bioinformatics tools available that not only aid in analysing such sequence data but also aid in deducing useful information that can be exploited in developing preventive and therapeutic measures. This chapter elaborates on bioinformatics tools that are specifically designed for animal viruses as well as other generic tools that can be exploited to study animal viruses. The chapter further provides information on the tools that can be used to study viral epidemiology, phylogenetic analysis, structural modelling of proteins, epitope recognition and open reading frame (ORF) recognition and tools that enable to analyse host-viral interactions, gene prediction in the viral genome, etc. Various databases that organize information on animal and human viruses have also been described. The chapter will converse on overview of the current advances, online and downloadable tools and databases in the field of bioinformatics that will enable the researchers to study animal viruses at gene level.
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Petitjean M, Badel A, Veitia RA, Vanet A. Synthetic lethals in HIV: ways to avoid drug resistance : Running title: Preventing HIV resistance. Biol Direct 2015; 10:17. [PMID: 25888435 PMCID: PMC4399722 DOI: 10.1186/s13062-015-0044-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2014] [Accepted: 02/23/2015] [Indexed: 12/19/2022] Open
Abstract
Background RNA viruses rapidly accumulate genetic variation, which can give rise to synthetic lethal (SL) and deleterious (SD) mutations. Synthetic lethal mutations (non-lethal when alone but lethal when combined in one genome) have been studied to develop cancer therapies. This principle can also be used against fast-evolving RNA-viruses. Indeed, targeting protein sites involved in SD + SL interactions with a drug would render any mutation of such sites, lethal. Results Here, we set up a strategy to detect intragenic pairs of SL and SD at the surface of the protein to predict less escapable drug target sites. For this, we detected SD + SL, studying HIV protease (PR) and reverse transcriptase (RT) sequence alignments from two groups of VIH+ individuals: treated with drugs (T) or not (NT). Using a series of statistical approaches, we were able to propose bona fide SD + SL couples. When focusing on spatially close co-variant SD + SL couples at the surface of the protein, we found 5 SD + SL groups (2 in the protease and 3 in the reverse transcriptase), which could be good candidates to form pockets to accommodate potential drugs. Conclusions Thus, designing drugs targeting these specific SD + SL groups would not allow the virus to mutate any residue involved in such groups without losing an essential function. Moreover, we also show that the selection pressure induced by the treatment leads to the appearance of new mutations, which change the mutational landscape of the protein. This drives the existence of differential SD + SL couples between the drug-treated and non-treated groups. Thus, new anti-viral drugs should be designed differently to target such groups. Reviewers This article was reviewed by Neil Greenspan Csaba Pal and István Simon. Electronic supplementary material The online version of this article (doi:10.1186/s13062-015-0044-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Michel Petitjean
- Univ Paris Diderot, Sorbonne Paris Cité, F-75013, Paris, France. .,MTI, INSERM UMR-S 973, F-75013, Paris, France.
| | - Anne Badel
- Univ Paris Diderot, Sorbonne Paris Cité, F-75013, Paris, France. .,MTI, INSERM UMR-S 973, F-75013, Paris, France.
| | - Reiner A Veitia
- Univ Paris Diderot, Sorbonne Paris Cité, F-75013, Paris, France. .,CNRS, UMR7592, Institut Jacques Monod, F-75013, Paris, France.
| | - Anne Vanet
- Univ Paris Diderot, Sorbonne Paris Cité, F-75013, Paris, France. .,CNRS, UMR7592, Institut Jacques Monod, F-75013, Paris, France. .,Atelier de Bio Informatique, F-75005, Paris, France.
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Unraveling the web of viroinformatics: computational tools and databases in virus research. J Virol 2014; 89:1489-501. [PMID: 25428870 DOI: 10.1128/jvi.02027-14] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
The beginning of the second century of research in the field of virology (the first virus was discovered in 1898) was marked by its amalgamation with bioinformatics, resulting in the birth of a new domain--viroinformatics. The availability of more than 100 Web servers and databases embracing all or specific viruses (for example, dengue virus, influenza virus, hepatitis virus, human immunodeficiency virus [HIV], hemorrhagic fever virus [HFV], human papillomavirus [HPV], West Nile virus, etc.) as well as distinct applications (comparative/diversity analysis, viral recombination, small interfering RNA [siRNA]/short hairpin RNA [shRNA]/microRNA [miRNA] studies, RNA folding, protein-protein interaction, structural analysis, and phylotyping and genotyping) will definitely aid the development of effective drugs and vaccines. However, information about their access and utility is not available at any single source or on any single platform. Therefore, a compendium of various computational tools and resources dedicated specifically to virology is presented in this article.
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Petitjean M, Vanet A. VIRAPOPS2 supports the influenza virus reassortments. SOURCE CODE FOR BIOLOGY AND MEDICINE 2014; 9:18. [PMID: 25183993 PMCID: PMC4144320 DOI: 10.1186/1751-0473-9-18] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2014] [Accepted: 08/07/2014] [Indexed: 11/10/2022]
Abstract
BACKGROUND For over 400 years, due to the reassortment of their segmented genomes, influenza viruses evolve extremely quickly and cause devastating epidemics. This reassortment arises because two flu viruses can infect the same cell and therefore the new virions' genomes will be composed of segment reassortments of the two parental strains. A treatment developed against parents could then be ineffective if the virions' genomes are different enough from their parent's genomes. It is therefore essential to simulate such reassortment phenomena to assess the risk of apparition of new flu strain. FINDINGS So we decided to upgrade the forward simulator VIRAPOPS, containing already the necessary options to handle non-segmented viral populations. This new version can mimic single or successive reassortments, in birds, humans and/or swines. Other options such as the ability to treat populations of positive or negative sense viral RNAs, were also added. Finally, we propose output options giving statistics of the results. CONCLUSION In this paper we present a new version of VIRAPOPS which now manages the viral segment reassortments and the negative sense single strain RNA viruses, these two issues being the cause of serious public health problems.
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Affiliation(s)
- Michel Petitjean
- Univ Paris Diderot, Sorbonne Paris Cité, F-75013 Paris, France ; MTI, INSERM UMR-S 973, F-75013 Paris, France
| | - Anne Vanet
- Univ Paris Diderot, Sorbonne Paris Cité, F-75013 Paris, France ; CNRS, UMR7592, Institut Jacques Monod, F-75013 Paris, France ; Atelier de Bio Informatique, F-75005 Paris, France
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