steven hoffman

Most students learn that DNA → RNA → protein. This sounds straightforward, but transcription (or that DNA → RNA bit) is surprisingly unpredictable, even in genetically identical cells. And, unfortunately, many tools commonly used in biology miss the way that gene expression actually works. For several decades, most biologists assumed that transcription followed a Poisson process. In other words, imagine that you are a gene within a cell moving through time. At each small step forward in time, you roll a die, and there's a fixed probability that this gene will get transcribed into mRNA. Most of the time, nothing happens; but sometimes you get a new mRNA. In a Poisson distribution, as time goes on, the probability that the cell has not made a transcript at all trends exponentially toward zero. Scientists believed this because they did not have methods to refute it. All the techniques we had to study gene expression were indirect. They might put a gene encoding green fluorescent protein into a cell, for example, let the cell transcribe and translate that gene, and then use a microscope to measure how brightly the cell glowed. They'd count up the fluorescent signal and try to work backward to infer how many mRNAs were responsible. But obviously these were proxies, and there's no correlation (in many genes) between protein levels and mRNA levels. So in 2005, Ido Golding and colleagues published what I think is one of the most beautiful and important papers in modern biology. They figured out how to watch and count mRNAs in individual living bacteria, and used this method to test whether transcription really followed a Poisson process. First, they engineered E. coli to make a green fluorescent protein fused to something called the MS2 coat protein. MS2 binds to a particular RNA sequence, called an MS2 binding site. So the bacteria were producing large amounts of this green fluorescent protein, which was floating around inside the cells. Second, they introduced another genetic construct, this time encoding a red fluorescent protein. But at the end of this gene, they added an enormous array of 96 MS2 binding sites. So what happens is that, when the cell transcribes the gene encoding the red fluorescent protein, the resulting mRNA also contains this long string of MS2 binding sites, which the green protein (fused to MS2) quickly gloms onto. And now, instead of having green fluorescence diffused throughout the cell, you get these very bright, green spots or "puncta" where the GFP molecules have accumulated on an mRNA. And because each mRNA recruits dozens of GFP molecules, the researchers could calibrate the brightness of these spots and estimate how many individual transcripts were present in a single cell. Importantly, they could also activate the red fluorescent gene using a small molecule called IPTG. (The gene was placed "downstream" from a so-called inducible promoter.) This meant they could add IPTG, start a stopwatch, take pictures over time, and then watch the bright green spots appear as the bacteria produced mRNAs. And they found that gene expression is sort of Poisson, but with a huge caveat. In a true Poisson process, the variance equals the mean. So if a gene produces an average of 10 mRNA molecules over some period of time, the variance in the number produced should also be 10. The transcripts appear at random times, sometimes close together, sometimes farther apart, but each event is independent. Instead, Golding and his colleagues found that the variance in mRNA abundance was about FOUR TIMES larger than a Poisson process would predict. This happens because transcription is BURSTY. A gene remains silent for a long time, then suddenly turns on and makes a bunch of mRNAs really quickly, and shuts off again. A cell might produce four transcripts over a few minutes and then make nothing for the next half hour. (The full model is this: When a gene is ON, transcription follows a Poisson process. But the gene can switch OFF at any moment. And because each ON period lasts a different amount of time, the number of transcripts produced in each burst follows a geometric distribution. In their experiments, genes were inactive for about 37 minutes on average, then active for about six minutes, producing an average of 2.2 transcripts per burst.) This study changed how we study biology. It's so important, because it influences the way we study RNAs in cells today. Depending on when you kill cells and extract their molecules, for example, some might have no transcripts from a particular gene, while others might have lots of them. That variation doesn't necessarily mean the cells are fundamentally different; some of them might have just had a recent burst of transcription, while others haven't. Or consider two genes that have exactly the same expression level. One might produce transcripts slowly and frequently, while the other produces them in large, infrequent bursts. If you just measure how many mRNAs are present, you can't tell how those transcripts were produced, or how the genes are regulated. I made a little interactive to show how this works and hope it is helpful.

Huge news. A gene therapy for deafness, targeting a mutation in the OTOF gene, is approved. Restored hearing in 9 of 12 children, well enough that they could stop using cochlear implants. The treatment will be free in the United States.