The Six Faces of E. Coli

A myriad of environmental changes inspire only a handful of responses

Biologists’ favorite bacterium grows almost anywhere—from the human gut to the pounding surf. But E. coli’s remarkable adaptability apparently stems from being predictable rather than accommodating. In a recent computer simulation, thousands of environments provoked only a handful of shifts in the microbe’s physiology. The work was published in Proceedings of the National Academy of Sciences in December 2005.

 

“A network comprised of thousands of molecules, in response to a myriad of inputs, takes on relatively few overall responses,” says senior author Bernhard Palsson, PhD, professor of bioengineering at the University of California, San Diego. The systems biology study of E. coli metabolism might help scientists understand how cells function and adapt to different environments.

 

A map of possible states for E. coli metabolism. The axes represent the Hamming distance—a mathematical comparison of the output from different simulations. The closer two points, the more similar are those results. The ovals show the terminal electron acceptor for different types of respiration: NO2/NO3 for anaerobic; O2 for aerobic; and fumarate or DMSO or TMAO for anaerobic fermentation. Courtesy of Christian Barrett, UCSD.To simulate E. coli’s environment, Palsson and his colleagues first drew up a list of nutrients that could meet the microbe’s needs—carbon, nitrogen, sulfur, etc. From this, they generated an exhaustive list of media that could support its growth. Then they wrote mathematical algorithms—based on 1,010 genes—for each step in E. coli’s wellunderstood metabolic process.

 

Combining the different inputs with these mathematical algorithms, they “grew” E. coli in 108,728 hypothetical simulated Petri dishes, of which 15,580 nurtured bacteria growth. Each of these in silico cultures produced a simulated gene expression profile, which researchers visualized in 3-D using a statistical tool known as principal component analysis.

 

The 3-D space was mostly empty: physiological outcomes appeared as thirteen clusters organized into six groups. Cells based their metabolic decisions largely on two factors: the availability of glucose as an energy source; and the identity of the terminal electron receptor(s)—the molecules that dictate whether the cell carries out aerobic respiration, anaerobic respiration, or fermentation. These responses are reasonably similar to laboratory experiments, Palsson says, but he was surprised by the limited scope of all possible responses.

 

The researchers chose to study E. coli because it has the best-characterized DNA on the planet, but the technique could apply to other organisms. For example, ecologists might map microbial communities in an ounce of soil to see how hundreds of microbes’ metabolisms interact. And engineers might use the technique to design whole bacterial ecosystems for useful tasks, such as eating toxic waste.

 

According to Costas Maranas, PhD, professor of chemical engineering at Pennsylvania State University, the study will help “to flesh out dominant organizing principles for complex systems.” In addition, he says, “One could look at whether the dominant behaviors that they have elucidated will hold under different kinds of perturbations, [such as] genetic perturbations.”

 

But the larger question of how all the complexity in the E. coli genome results in only a few metabolic activities, Palsson says, “is something that we still have to study, and understand.”



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