Niko McCarty.

This is an ongoing thread for my series, "30 Essays to Make You Love Biology." ❤️🧬

I'll pin it on my profile.

My podcasts are way too long and way too technical. Few people have time to sit through 90 minutes of hardcore immunology!

But if there's just one idea to take away from my interview with Jake Glanville at Centivax, a company developing universal flu vaccines and antivenoms, then I think it's this:

First, influenza (and many other viruses or pathogens) mutate so rapidly that we keep getting infected by slightly different versions of them. And so the challenge of making a universal flu vaccine is to elicit antibodies that recognize many different strains, including ones that don't yet exist.

The normal approach to this problem is just to mix together antigens from many different strains and inject them into humans all together. But your immune system then generates separate antibodies against each antigen, without necessarily producing antibodies that recognize ALL of them.

(Imagine a vaccine with 20 different versions of influenza's hemagglutinin protein. Your immune system might make antibodies against version A, others against B, and others against C. But what happens when influenza mutates again, producing version 21? Your body has no antibodies.)

What we really want are antibodies that recognize regions of the virus that rarely change! These so-called "conserved epitopes" are more likely to be shared across past, present, and future strains of ther pathogen.

So Jake, noticing this problem, invented a thing called conservation–concentration coupling. The gist is that, instead of injecting ALL of the antigens into a human at high doses, you deliberately lower the concentration of each one. There's only a tiny bit of each antigen in the vaccine, in other words; too little to elicit an immune response on its own. (If a B cell comes along and recognizes only antigen A, that B-cell won't become strongly activated!)

If, however, a B cell comes along that recognizes some converved region across MANY of the antigens, and it can bind to all of them, then it WILL become activated. Even though each individual antigen is present at a very low concentration, the B cell effectively encounters 20 times as much antigen, or 20x the normal dose. It is stimulated enough to proliferate, while the B cells recognizing only individual strains get "outcompeted."

In other words, you're using antigen concentration to bias the immune system toward making broadly reactive antibodies. This is the key idea behind Centivax, and they've gotten really compelling data that this actually works in mice and humans (~180 patients in their phase I, which is now finished.)

A biologist in Ohio is living with a rare form of cystic fibrosis. His particular mutation is not amenable to existing drugs for the disease.

And that’s why, for the last 10+ years, he has been trying to make a gene therapy to cure it. New essay today in @AsimovPress.

Most people living with cystic fibrosis still make copies of the cystic fibrosis transmembrane conductance regulator (CFTR) protein. These proteins are often defective, but modern drugs can help them fold or function properly.

And these drugs have been enormously successful. For decades, treatments like antibiotics and mechanical vibration vests have helped clear bacteria from the airways and raise life expectancy for people with cystic fibrosis from about 10 years to 40. Newer drugs that restore CFTR function have pushed life expectancy even higher; many patients today can live relatively normal lives.

But about 5–10% of people with cystic fibrosis have mutations that prevent them from making essentially any CFTR protein at all. These are often called nonsense or frameshift mutations. And because these patients don't make the protein, the existing CFTR-modulating drugs have nothing to act upon.

@jacobwitten1, the author of today's essay, is one of these patients. His laboratory at Case Western Reserve University is developing lipid nanoparticles that can deliver RNA into lung cells, with the ultimate goal of restoring CFTR function. These particles could carry RNA strands encoding the missing protein, or gene-editing tools that fix the mutation directly.

This is a deeply personal essay about the last 30 years of cystic fibrosis research, the extraordinary difficulty of delivering gene therapies into the lungs, and what it’s like to try to cure your own disease. We’re extremely honored that Jacob decided to write this! The piece is charming and irreverent and, I think, a reflection of his personality.

press.asimov.com/articles/cysti…

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.

A simple yet elegant study, showing how phages infect and then destroy individual bacterial cells.

They put E. coli into a Mother Machine (a thin microfluidics chamber) with a microscope overhead. You can stain a phage with SYTOX Orange (see video #1) and watch as it binds to a cell and injects its genome. Or, you can image cells at 100 frames per second and watch as they rupture with phages (as in video #2).

If you repeat this for hundreds of cells across conditions, like type of phage or temperature or anything else, and then plot the data, you find some extreme heterogeneity in phage infection and lysis dynamics!