Browsing by Author "Murrell, Ben"

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    Open Access
    Broadly neutralizing antibody responses in a large longitudinal sub-Saharan HIV primary infection cohort
    (Public Library of Science, 2016) Landais, Elise; Huang, Xiayu; Havenar-Daughton, Colin; Murrell, Ben; Price, Matt A; Wickramasinghe, Lalinda; Ramos, Alejandra; Bian, Charoan B; Simek, Melissa; Allen, Susan; Karita, Etienne; Kilembe, William; Lakhi, Shabir; Inambao, Mubiana; Kamali, Anatoli; Sanders, Eduard J; Anzala, Omu; Edward, Vinodh; Bekker, Linda-Gail; Tang, Jianming; Gilmour, Jill; Kosakovsky-Pond, Sergei L; Phung, Pham; Wrin, Terri; Crotty, Shane; Godzik, Adam; Poignard, Pascal
    Author Summary Understanding how HIV-1-broadly neutralizing antibodies (bnAbs) develop during natural infection is essential to the design of an efficient HIV vaccine. We studied kinetics and correlates of neutralization breadth in a large sub-Saharan African longitudinal cohort of 439 participants with primary HIV-1 infection. Broadly nAb responses developed in 15% of individuals, on average three years after infection. Broad neutralization was associated with high viral load, low CD4+ T cell counts, virus subtype C infection and HLA*A3(-) genotype. A correlation with high overall plasma IgG levels and anti-Env binding titers was also found. Specificity mapping of the bnAb responses showed that glycan-dependent epitopes, in particular the N332 region, were most commonly targeted, in contrast to other bnAb epitopes, suggesting that the HIV Env N332-glycan epitope region may be a favorable target for vaccine design.
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    Identification of broadly neutralizing antibody epitopes in the HIV-1 envelope glycoprotein using evolutionary models
    (BioMed Central Ltd, 2013) Lacerda, Miguel; Moore, Penny; Ngandu, Nobubelo; Seaman, Michael; Gray, Elin; Murrell, Ben; Krishnamoorthy, Mohan; Nonyane, Molati; Madiga, Maphuti; Wibmer, Constantinos; Sheward, Daniel; Bailer, Robert; Gao, Hongmei; Greene, Kelli; Karim, Salim S; M
    BACKGROUND:Identification of the epitopes targeted by antibodies that can neutralize diverse HIV-1 strains can provide important clues for the design of a preventative vaccine. METHODS: We have developed a computational approach that can identify key amino acids within the HIV-1 envelope glycoprotein that influence sensitivity to broadly cross-neutralizing antibodies. Given a sequence alignment and neutralization titers for a panel of viruses, the method works by fitting a phylogenetic model that allows the amino acid frequencies at each site to depend on neutralization sensitivities. Sites at which viral evolution influences neutralization sensitivity were identified using Bayes factors (BFs) to compare the fit of this model to that of a null model in which sequences evolved independently of antibody sensitivity. Conformational epitopes were identified with a Metropolis algorithm that searched for a cluster of sites with large Bayes factors on the tertiary structure of the viral envelope. RESULTS: We applied our method to ID50 neutralization data generated from seven HIV-1 subtype C serum samples with neutralization breadth that had been tested against a multi-clade panel of 225 pseudoviruses for which envelope sequences were also available. For each sample, between two and four sites were identified that were strongly associated with neutralization sensitivity (2ln(BF)>6), a subset of which were experimentally confirmed using site-directed mutagenesis. CONCLUSIONS: Our results provide strong support for the use of evolutionary models applied to cross-sectional viral neutralization data to identify the epitopes of serum antibodies that confer neutralization breadth.
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    Identifying predictors of evolutionary dispersion with phylogeographic generalised linear models
    (2017) Wolff-Piggott, Timothy; Lacerda, Miguel; Murrell, Ben
    Discrete phylogeographic models enable the inference of the geographic history of biological organisms along phylogenetic trees. Frequently applied in the context of epidemiological modelling, phylogeographic generalised linear models were developed to allow for the evaluation of multiple predictors of spatial diffusion. The standard phylogeographic generalised linear model formulation, however, assumes that rates of spatial diffusion are a noiseless deterministic function of the set of covariates, admitting no other unobserved sources of variation. Under a variety of simulation scenarios, we demonstrate that the lack of a term modelling stochastic noise results in high false positive rates for predictors of spatial diffusion. We further show that the false positive rate can be controlled by including a random effect term, thus allowing unobserved sources of rate variation. Finally, we apply this random effects model to three recently published datasets and contrast the results of analysing these datasets with those obtained using the standard model. Our study demonstrates the prevalence of false positive results for predictors under the standard phylogeographic model in multiple simulation scenarios and, using empirical data from the literature, highlights the importance of a model accounting for random variation.
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    Non-Negative Matrix Factorization for Learning Alignment-Specific Models of Protein Evolution
    (Public Library of Science, 2011) Murrell, Ben; Weighill, Thomas; Buys, Jan; Ketteringham, Robert; Moola, Sasha; Benade, Gerdus; Buisson, Lise du; Kaliski, Daniel; Hands, Tristan; Scheffler, Konrad
    Models of protein evolution currently come in two flavors: generalist and specialist. Generalist models (e.g. PAM, JTT, WAG) adopt a one-size-fits-all approach, where a single model is estimated from a number of different protein alignments. Specialist models (e.g. mtREV, rtREV, HIVbetween) can be estimated when a large quantity of data are available for a single organism or gene, and are intended for use on that organism or gene only. Unsurprisingly, specialist models outperform generalist models, but in most instances there simply are not enough data available to estimate them. We propose a method for estimating alignment-specific models of protein evolution in which the complexity of the model is adapted to suit the richness of the data. Our method uses non-negative matrix factorization (NNMF) to learn a set of basis matrices from a general dataset containing a large number of alignments of different proteins, thus capturing the dimensions of important variation. It then learns a set of weights that are specific to the organism or gene of interest and for which only a smaller dataset is available. Thus the alignment-specific model is obtained as a weighted sum of the basis matrices. Having been constrained to vary along only as many dimensions as the data justify, the model has far fewer parameters than would be required to estimate a specialist model. We show that our NNMF procedure produces models that outperform existing methods on all but one of 50 test alignments. The basis matrices we obtain confirm the expectation that amino acid properties tend to be conserved, and allow us to quantify, on specific alignments, how the strength of conservation varies across different properties. We also apply our new models to phylogeny inference and show that the resulting phylogenies are different from, and have improved likelihood over, those inferred under standard models.
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    Recreational fish-finders - an inexpensive alternative to scientific echo-sounders for unravelling the links between marine top predators and their prey
    (Public Library of Science, 2015) McInnes, Alistair M; Khoosal, Arjun; Murrell, Ben; Merkle, Dagmar; Lacerda, Miguel; Nyengera, Reason; Coetzee, Janet C; Edwards, Loyd C; Ryan, Peter G; Rademan, Johan
    Studies investigating how mobile marine predators respond to their prey are limited due to the challenging nature of the environment. While marine top predators are increasingly easy to study thanks to developments in bio-logging technology, typically there is scant information on the distribution and abundance of their prey, largely due to the specialised nature of acquiring this information. We explore the potential of using single-beam recreational fish-finders (RFF) to quantify relative forage fish abundance and draw inferences of the prey distribution at a fine spatial scale. We compared fish school characteristics as inferred from the RFF with that of a calibrated scientific split-beam echo-sounder (SES) by simultaneously operating both systems from the same vessel in Algoa Bay, South Africa. Customized open-source software was developed to extract fish school information from the echo returns of the RFF. For schools insonified by both systems, there was close correspondence between estimates of mean school depth (R 2 = 0.98) and school area (R 2 = 0.70). Estimates of relative school density (mean volume backscattering strength; S v ) measured by the RFF were negatively biased through saturation of this system given its smaller dynamic range. A correction factor applied to the RFF-derived density estimates improved the comparability between the two systems. Relative abundance estimates using all schools from both systems were congruent at scales from 0.5 km to 18 km with a strong positive linear trend in model fit estimates with increasing scale. Although absolute estimates of fish abundance cannot be derived from these systems, they are effective at describing prey school characteristics and have good potential for mapping forage fish distribution and relative abundance. Using such relatively inexpensive systems could greatly enhance our understanding of predator-prey interactions.