Category Archives: Science

Safety and immunogenicity of novel recombinant BCG and modified vaccinia virus Ankara vaccines in neonate rhesus macaques.

J Virol. 2010 Aug;84(15):7815-21. Epub 2010 May 19.
Rosario M, Fulkerson J, Soneji S, Parker J, Im EJ, Borthwick N, Bridgeman A, Bourne C, Joseph J, Sadoff JC, Hanke T

Although major inroads into making antiretroviral therapy available in resource-poor countries have been made, there is an urgent need for an effective vaccine administered shortly after birth, which would protect infants from acquiring human immunodeficiency virus type 1 (HIV-1) through breast-feeding. Bacillus Calmette-Guérin (BCG) is given to most infants at birth, and its recombinant form could be used to prime HIV-1-specific responses for a later boost by heterologous vectors delivering the same HIV-1-derived immunogen. Here, two groups of neonate Indian rhesus macaques were immunized with either novel candidate vaccine BCG.HIVA(401) or its parental strain AERAS-401, followed by two doses of recombinant modified vaccinia virus Ankara MVA.HIVA. The HIVA immunogen is derived from African clade A HIV-1. All vaccines were safe, giving local reactions consistent with the expected response at the injection site. No systemic adverse events or gross abnormality was seen at necropsy. Both AERAS-401 and BCG.HIVA(401) induced high frequencies of BCG-specific IFN-gamma-secreting lymphocytes that declined over 23 weeks, but the latter failed to induce detectable HIV-1-specific IFN-gamma responses. MVA.HIVA elicited HIV-1-specific IFN-gamma responses in all eight animals, but, except for one animal, these responses were weak. The HIV-1-specific responses induced in infants were lower compared to historic data generated by the two HIVA vaccines in adult animals but similar to other recombinant poxviruses tested in this model. This is the first time these vaccines were tested in newborn monkeys. These results inform further infant vaccine development and provide comparative data for two human infant vaccine trials of MVA.HIVA.

Journal Impact Factors – a good free tool

(originally posted on Kitserve.org.uk)

Recently, I’ve taken on more consulting work outside my own immediate area. http://www.eigenfactor.org, a free impact factor tool, has been incredibly handy. Here’s why.

Getting to grips (well on some level, at least) with a new system is a bit exciting and not a little empowering, too – like the first time you really understood crystalization as a kid (remember those copper sulphate crystals in the jar?)

The problem is that journals always fall into four categories in my book;

  1. Top level ones like Science, Nature, PLoS and PNAS,
  2. Reviews and stuff that are usually a good place to start,
  3. Key articles in specialist journals, and
  4. Crapola which you don’t need to bother with (to start with, at least).

The trouble is, while 1, 2, and 4 pretty much find themselves, working out which journals to look in for the specialist stuff when you start in a new field is pretty hard. For instance, the Journal of General Virology, Journal of Virology, and Virology all deal, obviously, with viruses and their biology… but which is the more authoritative?

The Impact Factor

If you’ve trained as a scientist, you’re probably sagely muttering ‘impact factor’. If you’ve ever worked as one you’re probably screaming it.

So what is this ‘impact factor’? Sounds like something to do with ballistics. Basically it’s a measure of the amount of influence a given published scientific article has on other articles. Since an article’s authors reference (or ‘cite’) other articles from all kinds of journals and books for background information and to support their own assertions, it follows that an article considered to be important to professionals in a field will be cited more frequently than an irrelevant one.

So good articles are cited more frequently. That helps us find those (both http://pubmed.org and http://scholar.google.com will tell you how many times a given article has been cited). And it’s a fairly simple matter to aggregate the mean number of citations per article in a particular journal and express that as a ratio or percentile of others in its field (you can also apply the same process to deciding whether or not to hire a particular scientist if you’re an institution or funding body – a nasty and growing trend which explains the screaming mentioned above…)

Use your judgement

There is a whole set of complex arguments about the best way to do that, and I won’t go into them here, not least because in my opinion at the end of the day you should always use your own good professional judgement when evaluating an article’s importance – no impact factor can fully do that for you. Ask yourself:

  • Do I actually know enough about the area yet to work out in general what the hell this article means, let alone if it’s any good?
  • If the citations seem particularly high (or low) for this journal/authors/general quality of paper, am I missing something?

If the answer to the first question is ‘no’ you’d better go off and read a few more reviews…

Eigenfactor

Anyway, why am I boning eigenfactor.org so hard at the moment? Well, a couple of reasons really:

  1. It’s free
  2. It has good coverage, and
  3. A great search interface, which is simple to use.

There are a few other useful things about their interface and data filtering, but for me those are the three main reasons. The ‘free’ thing’s great, obviously. But I really like the coverage they have and search interface because it quickly lets me find my way into a subject – when you start typing a journal or discipline into the box it autocompletes for you really smoothly. Ace huh?

Applying this to our virology journals, we find that their impact factors differ quite widely:

  • J. Gen. Virol. – 3.092
  • J. Virol. – 5.332
  • Virology – 3.765

So the Journal of Virology is the winner! Cool. Now I’m off to bone up on vif gene inactivation…

Estimating the Date of Origin of An HIV-1 Circulating Recombinant Form

Virology. 2009 Apr 25;387(1):229-34. Epub 2009 Mar 9.
Tee KK, Pybus OG, Parker J, Ng KP, Kamarulzaman A, Takebe Y.

HIV is capable of frequent genetic exchange through recombination. Despite the pandemic spread of HIV-1 recombinants, their times of origin are not well understood. We investigate the epidemic history of a HIV-1 circulating recombinant form (CRF) by estimating the time of the recombination event that lead to the emergence of CRF33_01B, a recently described recombinant descended from CRF01_AE and subtype B. The gag, pol and env genes were analyzed using a combined coalescent and relaxed molecular clock model, implemented in a Bayesian Markov chain Monte Carlo framework. Using linked genealogical trees we calculated the time interval between the common ancestor of CRF33_01B and the ancestors it shares with closely related parental lineages. The recombination event that generated CRF33_01B (t(rec)) occurred sometime between 1991 and 1993, suggesting that recombination is common in the early evolutionary history of HIV-1. The proof-of-concept approach provides a new tool for the investigation of HIV molecular epidemiology and evolution.

Correlating Viral Phenotypes With Phylogeny: Accounting for Phylogenetic Uncertainty

Infect Genet Evol. 2008 May;8(3):239-46. Epub 2007 Aug 21.
Parker J, Rambaut A, Pybus OG.

Many recent studies have sought to quantify the degree to which viral phenotypic characters (such as epidemiological risk group, geographic location, cell tropism, drug resistance state, etc.) are correlated with shared ancestry, as represented by a viral phylogenetic tree. Here, we present a new Bayesian Markov-Chain Monte Carlo approach to the investigation of such phylogeny-trait correlations. This method accounts for uncertainty arising from phylogenetic error and provides a statistical significance test of the null hypothesis that traits are associated randomly with phylogeny tips. We perform extensive simulations to explore and compare the behaviour of three statistics of phylogeny-trait correlation. Finally, we re-analyse two existing published data sets as case studies. Our framework aims to provide an improvement over existing methods for this problem.