Researchers report that they have used a combination of computational and experimental methods to examine how a variety of cells produce different proteins and to measure noise, i.e., the variability in the amount of each protein they express for every step along the production process.
The team discovered that for 85% of genes, the noise magnitude is higher in the last step as compared to the first step. Noise has been shown to play a role in viral infection, antibiotic resistance, and drug resistance in cancer cells, according to the scientists.
“We are trying to determine whether differences in one step along the assembly line influences the final amount of proteins produced more than other steps,” says Leor S. Weinberger, Ph.D., the William and Ute Bowes Distinguished Professor and director of the Center for Cell Circuitry at the Gladstone Institutes.
“When thinking about gene expression, we used to be unsure how each step contributed to the final outcome,” adds Maike Hansen, Ph.D. postdoctoral scholar in Dr. Weinberger’s laboratory and first author of the new study. “But we discovered that one step works very differently than we thought. It’s as if you always thought the production process was very streamlined, but then realized it’s actually much noisier.”
The researchers say their study, (“Cytoplasmic Amplification of Transcriptional Noise Generates Substantial Cell-to-Cell Variability”), published Cell Systems, indicates that the scientific community may have been misinterpreting an important step in gene expression for a long time. This could impact work by synthetic and systems biologists, as well as cell biologists, they point out.
“Transcription is an episodic process characterized by probabilistic bursts, but how the transcriptional noise from these bursts is modulated by cellular physiology remains unclear. Using simulations and single-molecule RNA counting, we examined how cellular processes influence cell-to-cell variability (noise),” write the investigators.
“The results show that RNA noise is higher in the cytoplasm than the nucleus in ∼85% of genes across diverse promoters, genomic loci, and cell types (human and mouse). Measurements show further amplification of RNA noise in the cytoplasm, fitting a model of biphasic mRNA conversion between translation- and degradation-competent states. This multistate translation-degradation of mRNA also causes substantial noise amplification in protein levels, ultimately accounting for ∼74% of intrinsic protein variability in cell populations. Overall, the results demonstrate how noise from transcriptional bursts is intrinsically amplified by mRNA processing, leading to a large super-Poissonian variability in protein levels.”
The group’s next step will be to investigate what mechanisms the cells employ to control noise.
“We’ve discovered an important step that increases cell-to-cell differences. These differences contribute to difficulties in treating various diseases,” says Dr, Weinberger, who is also a professor of pharmaceutical chemistry at the University of California, San Francisco. “Once we understand the mechanisms involved, we can start to exploit them for therapeutic targets.”
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