A Digital Test for Toxic Genes
News Jan 30, 2014
Like little factories, cells metabolize raw materials and convert them into chemical compounds. Biotechnologists take advantage of this ability, using microorganisms to produce pharmaceuticals and biofuels. To boost output to an industrial scale and create new types of chemicals, biotechnologists manipulate the microorganisms' natural metabolism, often by "overexpressing" certain genes in the cell. But such metabolic engineering is hampered by the fact that many genes become toxic to the cell when overexpressed.
Now, Allon Wagner, Uri Gophna, and Eytan Ruppin of Tel Aviv University'sBlavatnik School of Computer Science and Department of Molecular Microbiology and Biotechnology, along with researchers at the Weizmann Institute of Science, have developed a computer algorithm that predicts which metabolic genes are lethal to cells when overexpressed. The findings, published in Proceedings of the National Academy of Sciences, could help guide metabolic engineering to produce new chemicals in more cost-effective ways.
"In the lab, biotechnologists often determine which genes can be overexpressed using trial and error," said Wagner. "We can save them a lot of time and money by ruling out certain possibilities and highlighting other, more promising ones."
Gaining an EDGE
When metabolic genes are expressed, the genetic information they contain is converted into proteins, which catalyze the chemical reactions necessary for life. Overexpression means that greater-than-normal amounts of proteins are produced. Biotechnologists typically overexpress native genes of an industrial microorganism to boost a certain metabolic pathway in the cell, thus increasing the production of desired compounds. Sometimes they overexpress foreign genes — genes transferred from other organisms — in an industrial microbe to build new metabolic pathways and allow it to synthesize new compounds. But they often find that their efforts are hindered by the toxicity of the genes that they wish to overexpress.
Prof. Ruppin's laboratory builds large-scale software models of cellular metabolism, one of the most fundamental aspects of life. These models convert physical, chemical, and biological information into a set of mathematical equations, allowing scientists to learn how cells work and explore what happens if they are tweaked in certain ways. The newly developed algorithm, Expression Dependent Gene Effects, or EDGE, predicts what happens if scientists manipulate cells to overexpress certain genes. EDGE allows biotechnologists to foresee cases in which the overexpressed genes become toxic and then direct their efforts toward other genes.
To validate their method, TAU researchers used genetic manipulation tools to overexpress 26 different genes in E. coli bacterial cells. Comparing the results of their computer simulations with the actual growth of the overexpressed strains that was measured in the lab, they saw that EDGE was able to predict which of the overexpressed genes turned out to be lethal to E. coli. EDGE was also successful in identifying cases of foreign genes that were toxic to E. coli, as the researchers learned from comparing the simulations' results with data collected by their collaborators at the Weizmann Institute of Science.
EDGE's applications appear to extend beyond bacteria. The researchers conducted tests showing that the genes EDGE predicted to be toxic when overexpressed are expressed at low levels not only in microorganisms like bacteria, but also in multicellular organisms, including humans. The researchers say these results reflect the vital evolutionary need to keep the expression of potentially deleterious genes in check.
"Although EDGE's current focus is biotechnology, gene overexpression also plays a central part in many human diseases, particularly in cancer. We hope that future work will apply EDGE to those directions," Wagner said.
Computer bits are binary, with a value of 0 or 1. By contrast, neurons in the brain can have all kinds of different internal states, depending on the input that they received. This allows the brain to process information in a more energy-efficient manner than a computer. A new study hopes to bring the two closer together.
MIT researchers have developed a cryptographic system that could help neural networks identify promising drug candidates in massive pharmacological datasets, while keeping the data private. Secure computation done at such a massive scale could enable broad pooling of sensitive pharmacological data for predictive drug discovery.
Previous work by the International Multiple Sclerosis Genetics Consortium (IMSGC) has identified 233 genetic risk variants. However, these only account for about 20% of overall disease risk, with the remaining genetic culprits proving elusive. A new study has tracked down four of these hard-to-find genes.READ MORE