The reduced effectiveness of antibiotics is not only an important issue for human health. For example, plants can have a gene inserted which enables them to secrete an antibiotic against fungi. Siemen Schoustra believes that the added value of these genetically-modified crops is smaller than thought, as the fungus soon becomes permanently resistant to the antibiotic so that the plants still become diseased. The first resistant plant fungi have already been found in India.

Evolution is the cause of the resistance. However this resistance should disappear again, as resistant fungi and bacteria grow less well in an environment without the substance they are resistant to and are therefore outstripped by their faster-growing non-resistant counterparts. Yet, some variants remain permanently resistant.

Permanent resistance occurs in two stages. The fungus first of all becomes resistant due to a change in its DNA and then a second such change ensures that the resistant type eventually grows just as quickly as the non-resistant types. The result is a sort of super fungus, which is resistant and can also grow quickly under all circumstances. This leads to the resistance becoming permanent and therefore the effectiveness of antibiotics being reduced.

Researchers are trying to hinder the development of resistance in bacteria and fungi in an attempt to prevent the antibiotics from becoming obsolete. Their efforts are primarily focussed on understanding and preventing the first stage, whereas the second stage is often omitted. Schoustra's research reveals that hindering the second stage (compensating for the negative effects of resistance) is extremely difficult, as bacteria and fungi can compensate for the negative effects of resistance in so many different ways. Therefore, the only solution is to reduce the dependence on and the use of antibiotics.

The research was funded by the Netherlands Organisation for Scientific Research.

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BAMarrayTM software incorporates the Bayesian Analysis of Variance for Microarrays (BAM) methodology developed jointly at the Case and Cleveland Clinic Foundation Departments of Biostatistics and Epidemiology by J. Sunil Rao of Case and Hemant Ishwaran of the Cleveland Clinic Foundation. The methodology relies on a special type of inferential regularization that allows it to find more truly differentially expressing genes.

"The technique allows researchers to discover statistically significant genes from gene chip experiments that are normally hidden in statistical noise," said Ishwaran.

"Researchers here at Case have already found our methodology useful," said Rao. "We are thrilled that this software is now available to scientists everywhere."

"We've positioned this software for quick distribution and ease of use," said Ian Spatz, the case manager in the Case Technology Transfer Office responsible for the project. "I fully expect significant discoveries to be made using this powerful tool."

Joe Jankowski, assistant vice-president for biomedical sciences, added "This software is a great example of in-house technology validation. With Case's support, Rao and Ishwaran have been able to make a complex analysis technique simple and easy to use."

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