Noise Pollution: There Goes the Cellular Neighborhood

Reprinted with permission from AAAS COLORFUL NOISE: Bacterial cells simultaneously expressing two different fluorescent proteins (red and green) from identical promoters. Because of stochasticity or noise in the process of gene expression, even two nearly identical genes often produce unequal amounts of protein. Listen. Gene expression tends to be noisy. As beautifully regulated as it sometimes seems, the path from gene to message to protein picks up interference; a nagging variability in expression rates that can be found between seemingly identical organisms and even between identical genes in the same cell. For researchers, this noise, commonly known as stochasticity, renders results difficult to interpret. Reports dot the literature about unexplained randomness, such as variable chemotactic responses observed in clonal bacteria grown under homogenous conditions. Eukaryotes, too, share this randomness: Protein levels in an egg cell account for variable phenotypes in genetically identical lab mice; that variability from a single cell can affect the entire lineage. Elsewhere, stochasticity has been implicated in the loss of synchrony in circadian clocks,1 in random lysis or lysogeny of bacteriophage-lambda,2 and in decreased precision of cell signals.3 Recent work has shown how transcription or translation contribute to noise. Although scientists have known about gene-expression noise for more than 40 years, it wasn't until 1997 that Stanford researchers Harley McAdams and Adam Arkin proposed a model for stochastic mechanisms in gene expression.4 Since then, noise has become a focus within the field of systems biology. For those who would hope to draw a wiring diagram of the networks that comprise life, the random chatter of variability must be elucidated. After all, the effects can be drastic. "People think that genetics determines your individuality," says Alexander van Oudenaarden, Massachusetts Institute of Technology. "That's true and important, but even if you have a genetically identical twin, you can have a variable phenotype that may ultimately affect survival." Very precise switches control cell-cycle checkpoints and cellular differentiation. The development of a human fetus from an egg is remarkably precise. How this happens in the face of stochasticity is a key question. "It's remarkable," says McAdams. "How in the world could you possibly do that?" Precision in development implies that cells must have evolved circuitry that controls the noise, permitting efficient information processing, says Peter Swain, a McGill University professor who studies stochasticity. Understanding the mechanisms that allow cellular precision within a quagmire of randomness will enhance the ability to see life's networks more clearly. MODELS TO MEASURES Since Arkin and McAdams proposed stochasticity, researchers have since set out to measure noise and experimentally determine how transcription or translation contributes to it. Three recent papers get to the heart of this.5-7 "Very little was known before these studies were done as to where this stochasticity comes from and what parameters determine the magnitude of stochasticity and the resulting population variability," says Mads Kaern, a Boston University postdoc and coauthor on one paper. Extrinsic and intrinsic noise, according to some, contribute to overall variation in clonal populations.5 Extrinsic noise stems from concentration and location fluctuations in cellular components such as regulatory proteins and polymerases, which are global to a single cell but vary from one cell to another. Alternatively, intrinsic noise is inherent in the biochemical process of gene expression. Random microscopic events such as thermal fluctuations in RNA polymerase, open-complex formation, or random molecular partitioning during a cell division event, even in a population of identical cells, determine what reactions will occur and their order. Further, these events will limit the precision of gene regulation. Swain, Michael Elowitz, and colleagues at Rockefeller University operationally define intrinsic noise for a given gene as "the extent to which the activities of two identical copies of that gene, in the same intracellular environment, fail to correlate."5 To discriminate between both types of noise, the Elowitz group constructed strains of Escherichia coli that express the cyan and yellow alleles of green fluorescent protein (GFP) in the chromosome. Identical promoters controlled the two reporter genes, which were integrated at loci equidistant from and on opposites sides of the origin of replication. When viewed, through certain fluorescence filters, cells with the same amount of each protein appear yellow, indicating low noise levels. Cells expressing one fluorescent protein more than the other appeared either red or green, indicating high noise levels. In one-color experimental systems, each cell may vary in brightness as a result of extrinsic noise. McGill's Swain says using this two-color system controls for extrinsic noise. Another group, led by van Oudenaarden, determined how transcription and translation contribute to noise in Bacillus subtilis.6 They mutated promoters to vary transcriptional strength and altered ribosomal binding sites to amend translational efficiency, which allowed them to study the influence of each independently on the noise level. The researchers found that both contribute to noise, but the translational contribution is stronger. "Strong translation resulted in strong [protein] bursts and high noise," says van Oudenaarden. The work in bacteria prompted Jim Collins, of Boston University, to study this phenomenon in eukaryotes.7 In budding yeast, his team used transcriptional regulation in the yeast GAL1 promoter to understand the contribution of transcription to noise. One of the significant findings that distinguishes this paper from those on prokaryotes is the phenomenon of reinitiation. Highly expressed genes may be associated with promoters that undergo reinitiation, whereby a stabilized protein scaffold, including the TATA binding protein, remains on the promoter for multiple rounds of transcription. This allows more rapid production of transcript, because there is no need to reassemble the preinitiation complex de novo. "We believe this is important in its contribution to the level of noise in gene expression," says Will Blake, a postdoc in Collins' lab. Says Kaern: "Promoters [that] control expression of key regulatory genes might benefit from a low extent of reinitiation, and therefore, a low level of noise, as opposed to other instances where noise could perhaps be beneficial and allow cells to have a huge variability in population phenotype." The Collins group also studied how translation affects noise. In a quirk of nature, codons that appear infrequently in the genetic code are often translated less efficiently. Using the codon adaptation index, a reliable indicator of translational efficiency in yeast, the Collins group modified the codon content of GFP, creating two mutant versions expressing 50% and 75% of the fully expressing GFP. Using these constructs to measure noise, they found that the more efficiently translated genes had a slightly greater level of noise. But, increased translational efficiency, coupled with a noisy transcriptional state, amplified noise considerably, providing further evidence that noise levels are strongly influenced by transcription, in contrast to bacteria, which are influenced more by translation. Prior to this work, the theory of gene-expression noise was a physical conjecture backed up with a mathematical model. It was speculation, says Arkin, now at the University of California, Berkeley. "Now there is convincing evidence that what we postulated is correct. They showed [that] our ducks might actually be in a row." Noise in a cascading gene networkClick for larger version (40K) RIGHT, LEFT, RIGHT Besides knowing how organisms protect themselves from annihilation, understanding noise has other applications. Cells must calculate direction of locomotion, which is accomplished through a protein scaffolding network that basically has the same function as a resistor and capacitor in a computer, says van Oudenaarden. "Noise becomes very important here." Understanding how noise propagates in networks will help researchers learn how bacteria create and use these networks. When envisioning artificial circuits that can turn genes off or on at will or control cell locomotion, it's easy to appreciate noise. Types of engineered circuits that may prove useful include: pathways for production of industrial or medically important molecules, such as antibiotics or detergent proteins; cells and viruses specifically designed to perform multistep degradations or cell-specific activity for gene therapy; and circuits that could probe the cellular environment.8 Many regulatory problems in humans, such as diabetes and some cancers, result from failures of regulatory circuits. Thinking ahead, genetically altered bacteria with a genetic switch for insulin production could be placed under the skin to sense blood glucose levels, secrete insulin, and then switch off when blood glucose normalizes. "This is pie in the sky right now," says McAdams. But it's not out of the realm of possibility. It's like the beginning of any new field. Because these are the first mechanistic descriptions of gene-expression noise, everyone is in a state of observation, taking in the new information, formulating new strategies for examining noise, and tracking down the biological implications. Linda B. Schultz (LBSchultz@sciwriting.com) is a freelance writer in Suwanee, Ga. References 1. N. Barkai et al., "Biological rhythms: Circadian clocks limited by noise," Nature, 403:267-8, 2000. 2. A. Arkin et al., "Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells," Genetics, 149:1633-48, 1998. 3. O.G. Berg et al., "Fluctuations and quality of control in biological cells: Zero-order ultrasensitivity reinvestigated," Biophys J, 79:1228-36, 2000. 4. H.H. McAdams et al., "Stochastic mechanisms in gene expression," Proc Natl Acad Sci, 94:814-9, 1997. 5. M.B. Elowitz et al., "Stochastic gene expression in a single cell," Science, 297:1183-6, 2002. 6. E.M. Ozbudak et al., "Regulation of noise in the expression of a singe gene," Nat Gen, 31:69-73, 2002. 7. W.J. Blake et al., "Noise in eukaryotic gene expression," Nature, 422:633-7, 2003. 8. H.H. McAdams et al., "Genetic regulatory circuits: Advances toward a genetic circuit engineering discipline," Curr Biol, 10:318-20, 2000. function sendData() { document.frm.pathName.value = location.pathname; result = false if (document.frm.score[0].checked) result = true; if (document.frm.score[1].checked) result = true; if (document.frm.score[2].checked) result = true; if (document.frm.score[3].checked) result = true; if (document.frm.score[4].checked) result = true; if (!result) alert("Please select") return result; } .myradio{background-color : #94bee5} Please indicate on a 1 - 5 scale how strongly you would recommend this article to your colleagues? Not recommended 1 2 3 4 5 Highly recommended Please register your vote

Noise Pollution: There Goes the Cellular Neighborhood

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