1,721,470 research outputs found
Evolution of the interferon alpha gene family in eutherian mammals
No full textInterferon alpha (IFNA) genes code for proteins with important signaling roles during the innate immune response. Phylogenetically, IFNA family members in eutherians (placental mammals) cluster together in a species-specific manner except for closely related species (i.e. Homo sapiens and Pan troglodytes) where gene-specific clustering is evident. Previous research has been unable to clarify whether gene conversion or recent gene duplication accounts for gene-specific clustering, partly because the similarity of members of the IFNA family within species has made it historically difficult to identify the exact composition of IFNA gene families. IFNA gene families were fully characterized in recently available genomes from Canis familiaris, Macaca mulatta, P. troglodytes and Rattus norvegicus, and combined with previously characterized IFNA gene families from H. sapiens and Mus musculus, for the analysis of both whole and partial gene conversion events using a variety of statistical methods. Gene conversion was inferred in every eutherian species analyzed and comparison of the IFNA gene family locus between primate species revealed independent gene duplication in M. mulatta. Thus, both gene conversion and gene duplication have shaped the evolution of the IFNA gene family in eutherian species. Scenarios may be envisaged whereby the increased production of a specific IFN-? protein would be beneficial against a particular pathogenic infection. Gene conversion, similar to duplication, provides a mechanism by which the protein product of a specific IFNA gene can be increased
Molecular modeling and phylogenetic analyses highlight the role of amino acid 347 of the N1 subtype neuraminidase in influenza virus host range and interspecies adaptation
Full text linkThe N1 neuraminidases (NAs) of avian and pandemic human influenza viruses contain tyrosine and asparagine, respectively, at position 347 on the rim of the catalytic site; the biological significance of this difference is not clear. Here, we used molecular dynamics simulation to model the effects of amino acid 347 on N1 NA interactions with sialyllacto-N-tetraoses 6'SLN-LC and 3'SLN-LC, which represent NA substrates in humans and birds, respectively. Our analysis predicted that Y347 plays an important role in the NA preference for the avian-type substrates. The Y347N substitution facilitates hydrolysis of human-type substrates by resolving steric conflicts of the Neu5Ac2-6Gal moiety with the bulky side chain of Y347, decreasing the free energy of substrate binding, and increasing the solvation of the Neu5Ac2-6Gal bond. Y347 was conserved in all N1 NA sequences of avian influenza viruses in the GISAID EpiFlu database with two exceptions. First, the Y347F substitution was present in the NA of a specific H6N1 poultry virus lineage and was associated with the substitutions G228S and/or E190V/L in the receptor-binding site (RBS) of the hemagglutinin (HA). Second, the highly pathogenic avian H5N1 viruses of the Gs/Gd lineage contained sporadic variants with the NA substitutions Y347H/D, which were frequently associated with substitutions in the HA RBS. The Y347N substitution occurred following the introductions of avian precursors into humans and pigs with N/D347 conserved during virus circulation in these hosts. Comparative evolutionary analysis of site 347 revealed episodic positive selection across the entire tree and negative selection within most host-specific groups of viruses, suggesting that substitutions at NA position 347 occurred during host switches and remained under pervasive purifying selection thereafter. Our results elucidate the role of amino acid 347 in NA recognition of sialoglycan substrates and emphasize the significance of substitutions at position 347 as a marker of host range and adaptive evolution of influenza viruses
Codon volatility does not reflect selective pressure on the HIV-1 genome
No full textCodon volatility is defined as the proportion of a codon's point-mutation neighbors that encode different amino acids. The cumulative volatility of a gene in relation to its associated genome was recently reported to be an indicator of selection pressure. We used this approach to measure selection on all available full-length HIV-1 subtype B genomes in the Los Alamos HIV Sequence Database, and compared these estimates against those obtained via established likelihood- and distance-based comparative methods. Volatility failed to correlate with the results of any of the comparative methods demonstrating that it is not a reliable indicator of selection pressure.<br/
Specificity of the interaction between Neuraminidase N1 of the avian influenza A virus H1N1 1918 and a2-3 or a2-6 glycan receptors of avian and human cell targets
No full textAutomated phylogenetic detection of recombination using a genetic algorithm
No full textThe evolution of homologous sequences affected by recombination or gene conversion cannot be adequately explained by a single phylogenetic tree. Many tree-based methods for sequence analysis, for example, those used for detecting sites evolving nonneutrally, have been shown to fail if such phylogenetic incongruity is ignored. However, it may be possible to propose several phylogenies that can correctly model the evolution of nonrecombinant fragments. We propose a model-based framework that uses a genetic algorithm to search a multiple-sequence alignment for putative recombination break points, quantifies the level of support for their locations, and identifies sequences or clades involved in putative recombination events. The software implementation can be run quickly and efficiently in a distributed computing environment, and various components of the methods can be chosen for computational expediency or statistical rigor. We evaluate the performance of the new method on simulated alignments and on an array of published benchmark data sets. Finally, we demonstrate that prescreening alignments with our method allows one to analyze recombinant sequences for positive selection
Estimating selection pressures on HIV-1 using phylogenetic likelihood models
No full textHuman immunodeficiency virus (HIV-1) can rapidly evolve due to selection pressures exerted by HIV-specific immune responses, antiviral agents, and to allow the virus to establish infection in different compartments in the body. Statistical models applied to HIV-1 sequence data can help to elucidate the nature of these selection pressures through comparisons of non-synonymous (or amino acid changing) and synonymous (or amino acid preserving) substitution rates. These models also need to take into account the non-independence of sequences due to their shared evolutionary history. We review how we have developed these methods and have applied them to characterize the evolution of HIV-1 in vivo. To illustrate our methods, we present an analysis of compartment-specific evolution of HIV-1 em) in blood and cerebrospinal fluid and of site-to-site variation in the gag gene of subtype C HIV-1. Copyright (C) 2008 John Wiley & Sons, Ltd
Additional benchmarks and algorithm listings.
Full text linkThe supplementary text contains (A) benchmarking of run time and memory usage on simulations without selection; (B) benchmarking of memory usage with selection; (C) an analysis of the effect of simplification interval on run times; (D) details for the simuPOP implementation; and (E) more details, and a listing, of the simplification algorithm. (PDF)</p
Computational models of HIV-1 resistance to gene therapy elucidate therapy design principles.
Full text linkGene therapy is an emerging alternative to conventional anti-HIV-1 drugs, and can potentially control the virus while alleviating major limitations of current approaches. Yet, HIV-1's ability to rapidly acquire mutations and escape therapy presents a critical challenge to any novel treatment paradigm. Viral escape is thus a key consideration in the design of any gene-based technique. We develop a computational model of HIV's evolutionary dynamics in vivo in the presence of a genetic therapy to explore the impact of therapy parameters and strategies on the development of resistance. Our model is generic and captures the properties of a broad class of gene-based agents that inhibit early stages of the viral life cycle. We highlight the differences in viral resistance dynamics between gene and standard antiretroviral therapies, and identify key factors that impact long-term viral suppression. In particular, we underscore the importance of mutationally-induced viral fitness losses in cells that are not genetically modified, as these can severely constrain the replication of resistant virus. We also propose and investigate a novel treatment strategy that leverages upon gene therapy's unique capacity to deliver different genes to distinct cell populations, and we find that such a strategy can dramatically improve efficacy when used judiciously within a certain parametric regime. Finally, we revisit a previously-suggested idea of improving clinical outcomes by boosting the proliferation of the genetically-modified cells, but we find that such an approach has mixed effects on resistance dynamics. Our results provide insights into the short- and long-term effects of gene therapy and the role of its key properties in the evolution of resistance, which can serve as guidelines for the choice and optimization of effective therapeutic agents
Bayesian inference of ancestral recombination graphs
No full textWe present a novel algorithm, implemented in the software ARGinfer, for probabilistic inference of the Ancestral Recombination Graph under the Coalescent with Recombination. Our Markov Chain Monte Carlo algorithm takes advantage of the Succinct Tree Sequence data structure that has allowed great advances in simulation and point estimation, but not yet probabilistic inference. Unlike previous methods, which employ the Sequentially Markov Coalescent approximation, ARGinfer uses the Coalescent with Recombination, allowing more accurate inference of key evolutionary parameters. We show using simulations that ARGinfer can accurately estimate many properties of the evolutionary history of the sample, including the topology and branch lengths of the genealogical tree at each sequence site, and the times and locations of mutation and recombination events. ARGinfer approximates posterior probability distributions for these and other quantities, providing interpretable assessments of uncertainty that we show to be well calibrated. ARGinfer is currently limited to tens of DNA sequences of several hundreds of kilobases, but has scope for further computational improvements to increase its applicability
HPV clearance and the neglected role of stochasticity.
Full text linkClearance of anogenital and oropharyngeal HPV infections is attributed primarily to a successful adaptive immune response. To date, little attention has been paid to the potential role of stochastic cell dynamics in the time it takes to clear an HPV infection. In this study, we combine mechanistic mathematical models at the cellular level with epidemiological data at the population level to disentangle the respective roles of immune capacity and cell dynamics in the clearing mechanism. Our results suggest that chance-in form of the stochastic dynamics of basal stem cells-plays a critical role in the elimination of HPV-infected cell clones. In particular, we find that in immunocompetent adolescents with cervical HPV infections, the immune response may contribute less than 20% to virus clearance-the rest is taken care of by the stochastic proliferation dynamics in the basal layer. In HIV-negative individuals, the contribution of the immune response may be negligible
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