My published negative result…

IMAGINE your excitement as a budding young researcher taking on your first piece of research as part of an undergraduate summer studentship. The project is to characterise a gene that makes a type of medically important bacteria resistant to a key group of antibiotics—the tetracyclines. The gene in question is described in a peer-reviewed specialist journal, but no-one is quite sure how the gene works. So why are we interested? If we’re to understand and address the problem of antibiotic resistance, one of the many things we need to do is understand their mechanisms of resistance. This gene appears very different from any other gene that performs a similar function, so it has been classed into its own ‘family’ of resistance determinant, which appears in reviews and textbooks. It has also been screened for, and found, in a notable ‘superbug’, VRSA (the vancomycin resistant big brother of MRSA). The presence of this gene may also have influenced whether or not a patient was given tetracycline.

The only problem is, the gene in question is not an antibiotic resistance gene, but you won’t know this until you have spent the summer working on it. Indeed, it won’t be until the project is inherited as a pet project by a postdoc that we’ll know this. The fact is, the gene had already been recognised for what it really was over a decade earlier—not that this was ever reported. The person who immediately dismissed the gene’s published function was in fact the one time PhD supervisor of the postdoc who picked up the project, and a world expert on the family that our mystery the gene really belongs to, but I’ll come back to that.

My post last week, ‘On publishing negative results…‘, briefly described the issue of positive publication bias in scientific and medical literature, and was a pre-amble to the story of my own experience publishing negative results. So let me now tell you about how I tried, and succeeded, at getting ostensibly negative results published.

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On publishing negative results…

IN the last week Ben Goldacre’s ire has been felt, and rightly so, because what the Ire of Goldacre has been pointing at is a systematic bias in the publication of science and medical information. Ben’s focus relates to the way in which big pharmaceutical companies manipulate an overwhelmingly positive academic publication record, accusing them of selectively burying the results of negative trial data and publishing only the positive trial data. This serves the interests of pharmaceutical companies, but not those of the patients or doctors. You can see a video of Ben discussing this here.

The problem of publication bias in the scientific and medical literature is that positive results get published, and negative results – or those from studies attempting to replicate previous studies – by and large, don’t. There are several problems with this, and with which I’ve had practical experience:

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The ‘negatome’ – a database of negative information…

Research bloggingWE researchers often joke that no-one ever publishes negative results, but that doesn’t mean to say that negative results aren’t extremely useful. On one level, knowledge of such negative results can prevent you repeating the same mistakes that countless other researchers, in other labs, have undoubtedly made over the years. On the other hand, they can provide a valuable dataset with which to generate new and useful information. One such example is the ‘Negatome Database‘, which has been reported by Smialowski et al.1 in Nucleic Acids Research advance access (November 17, 2009).

The Negatome is a collection of protein and domain (functional units of proteins) pairs that are unlikely to be engaged in direct physical interactions. But why on Earth would we want to know about proteins that don’t interact with each other; in fact, why do we need to know about proteins that interact at all?

Macromolecular machineResearchers recognize that that a cell doesn’t function purely by the action of individual proteins, but instead by large macromolecular complexes mediated by many interacting proteins.  The image to the left indicates an example macromolecular ‘machine’, in this case those involved in signal processing at the neuronal synapses (and which are likely to be working quite hard right now!).

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