New & Noteworthy

Another Small Victory for Lamarck

July 29, 2015

Yeast gives us an example of an adaptation that is positively Lamarckian! Image of Jean Baptiste de Lamarck via Wikimedia Commons

Examples of various ways that the environment affects gene expression have become so commonplace that new examples don’t make much of a splash anymore. It is as if Lamarck and Darwin had never argued about how natural selection works.

The main reason we aren’t surprised anymore is that we have a pretty good handle on how most of these changes are happening. Something in our environment causes chemical groups to be added or removed from our DNA and/or its associated proteins, causing a change in gene expression. These kinds of epigenetic changes happen a lot and are now seen as the norm.

What doesn’t happen much at all is that the underlying DNA gets changed in a predictable way to change gene expression in response to something in the environment. Which is why a recent study in PNAS by Jack and coworkers makes you stand up and take notice.

In this study the researchers provide evidence that suggests that yeast will expand the number of copies of its rDNA locus to match the level of nutrients in the environment (presumably to make more ribosomes to take advantage of all those nutrients). The yeast is responding to the environment by changing the content of its genome rather than just changing how it is used.

This is big. It is almost as if Lamarck was right about some part of natural selection. Oh wait, that’s exactly what it is! 

What makes this so cool is that it suggests that natural selection doesn’t necessarily just happen when a random genetic variant wins out in a population. Sometimes the environment itself can induce the winning genome change–and these aren’t just epigenetic changes. (Go Lamarck!)

Jack and coworkers focused on the rDNA locus of the yeast Saccharomyces cerevisiae. This is a fairly fluid part of the yeast genome that consists of multiple copies of the 35S and 5S pre-rRNA genes. The average yeast cell has around 180 copies of this locus and there is a normal range of 150-200 per cell. If a yeast somehow ends up with 80 or fewer copies, it quickly increases the number back to that golden 150-200 range via a Fob1 dependent mechanism.

The authors created a strain of yeast that lacked Fob1 and had only 35 tandem repeats in its rDNA region. This strain, rDNA35, could not expand its rDNA unless Fob1 was added back. They now had a strain in which they could test what affected rDNA expansion, by transforming the strain with a plasmid expressing Fob1 and growing the transformants under different conditions.

The most surprising experiment was the final one of the paper. The authors grew the rDNA35 strain in either 2% or 0.5% glucose and found that rDNA amplification was slowed significantly in low glucose. The authors interpret this to mean that the genome change, the expansion of rDNA, is dependent on nutrient availability. A signaling pathway is able to adjust the rate of rDNA expansion.

Yeast will, of course, grow more slowly at low levels of glucose than they will at higher levels. But the authors were able to show that slow growth was not the reason for the slowed expansion of rDNA at low glucose. They were able to separate the two effects by overexpressing Pnc1, a nicotinamidase.

Overexpression of Pnc1 led to a decreased rate of copy number increase even at the higher glucose levels without affecting growth rate. So rDNA expansion can be separated from slow growth under the right conditions. And as you’ll see below, Pnc1 makes perfect sense given how at least part of nutrient level-dependent rDNA amplification works.

In looking for factors that might affect the rate of rDNA expansion, Jack and coworkers focused on the TOR signaling pathway, since previous work had suggested that it might be important in this process and it is known to respond to nutrient availability. The authors confirmed it was a key player by showing that rapamycin, a TOR inhibitor, kept the rDNA35 strain from expanding its rDNA in the presence of FOB1.

Again they ran into the problem of disentangling cell growth and the rDNA expansion, as rapamycin slows cell growth. The next set of experiments showed that the lack of expansion was almost certainly not due to the slower growth rate.

It is known that rapamycin affects histone deacetylases (HDACs) including Sir2. Jack and coworkers found that nicotinamide, a Sir2 inhibitor, increased the rate of expansion of rDNA without affecting growth rate. So rDNA amplification was not dependent on growth rate.

Which brings us back to Pnc1, that enzyme that cleaves nicotinamide! Presumably endogenous levels of nicotinamide are able to inhibit Sir2 and so encourage the rDNA expansion. Overexpressing Pnc1 releases Sir2 which can then impede the expansion of rDNA.

While that was a bit complicated, the idea is simple and potentially profound. Yeast can sense the level of nutrients in their environment at least partially through the TOR signaling pathway and adjust the actual content of their genome accordingly.

The involvement of nicotinamide in this regulatory process makes this result even cooler, as it has important roles in aging and cellular metabolism from yeast to man. For example, it plays a key role in the life extending properties of caloric restriction in yeast and possibly in more complicated eukaryotes as well. (Click here for a fascinating look at NAD, a compound that contains nicotinamide.)

So, in the presence of low nutrient levels, yeast expand their rDNA much more slowly than they would at higher nutrient levels. Yeast can tailor its genome in response to its environment so it can better utilize that environment.

This work raises the fascinating possibility that this process might even happen at genomic regions other than the rDNA locus. Yeast still has plenty of surprises in store—including giving a Lamarck a little boost.

by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics

Two Tales of Two Tails

July 22, 2015

Tails let animals sense and interact with their environment at a distance from their bodies. It turns out that some proteins use their “tails” in a similar way. Except they take it one step further.

Some mythical creatures have tails that coil around each other. Septin proteins are not all that different! Image via Wikimedia Commons

In addition to sensing the environment, they can also use their tails as sort of fishing poles to catch proteins they need to interact with. And they do this with great specificity…it is like having that perfect lure that always catches one type of fish.

A great example of this is the septin family of proteins which includes many members that are “tailed” proteins. Septins are highly conserved proteins that typically have a globular GTP-binding domain adjacent to an elongated C-terminal extension.

Septins form structures that act as the boundaries between different cellular parts. In budding yeast cells, they form the septum that separates the mother and bud, and recruit the cytokinesis machinery that allows the daughter cell to separate from the mother cell. In larger animals, they can be found in places such as the dendritic spines of neurons, sperm flagella, or cilia. And in humans, septin mutations have been linked to cancer and neurological diseases.

Until now, the details of how septins recruit other proteins to boundary sites have been elusive. But in two new papers in GENETICS, Finnigan and coworkers in the Thorner lab at Berkeley dove into this question and gained real insight into the lures these proteins use.

In their first paper they reported an extremely comprehensive genetic analysis to dissect the functions of two of the least characterized septins, Shs1 and Cdc11. In the second paper they used both genetic and physical methods to show how these septins recruit myosin to the septum to form the contractile ring that pinches off the bud from the mother.

The bottom line: their C-terminal tails are extremely important. They intertwine with other proteins’ tails like love-struck seahorses. And their specificity comes from these same tails—certain tails only coil around other tails.  

The S. cerevisiae genome encodes a family of septins that assemble with each other to form octameric rods that consist of four different septins. The rods have both end-to-end and side-to-side interactions with each other, forming a ladder-like superstructure.

The septins Cdc11 and Shs1 are the most closely related members of the septin family, and the most recently evolved. They cap the ends of the septin rods. In otherwise wild-type cells, Cdc11 is essential for life while Shs1 is not.

Because SHS1 can be deleted without causing a major phenotype, the first step in investigating its function was to find genetic conditions under which its function becomes more obvious. The authors created four different genetic backgrounds in which the function of other septins was compromised by different mutations. Cells that had mutations in both SHS1 and in other septin genes had obvious problems, such as elongated buds or the inability to grow at high temperatures.

Now Finnigan and colleagues were set to do a detailed genetic analysis to figure out what different parts of Shs1 do by testing mutant versions in these different backgrounds. We can’t possibly recapitulate all the results here, but we’ll do our best to cover the highlights.

Almost all septins, whether in yeast or mammals, end with a tail: a long stretch called the C-terminal extension (CTE) that contains sequence patterns characteristic of a coiled-coil structure. The researchers found that the coiled coil regions of Shs1 and Cdc11were essential to their functions. (And no, they didn’t create any mutations by writing their names in the coiled coil sequence!)

Finnigan and colleagues tried swapping CTEs between different septins. When Cdc11 carried the Shs1 CTE and vice versa, the cells grew just fine. However, this swappability didn’t extend to other septins that are positioned internally in the septin rods. The CTEs of the end subunits Cdc11 and Shs1 could be exchanged for each other, but these CTEs only worked when they were on the ends of the rods.

Since coiled coils are often involved in interactions between proteins, Finnigan and colleagues wondered whether the essential function of the Cdc11 and Shs1 CTEs might be to recruit other proteins to the bud neck.

To test this, they searched published data to identify proteins that are well-known to be localized to the bud neck at the time in the cell cycle when septins are present. They found 30 such proteins, and overexpressed GFP-tagged versions of each in a strain where both Cdc11 and Shs1 lacked their CTEs.

Of the 30 proteins, only overexpressed Bni5 suppressed the growth defect of this strain. To test directly whether binding to Bni5 is a critical function of Shs1, Finnigan and colleagues fused the two genes to each other, so that Bni5 replaced the CTE of Shs1. This fusion protein could compensate for the lack of both Cdc11 and Shs1 CTEs.

To confirm that the important function of the CTEs is to hold Bni5 in the right place, they came up with an alternative test using a “nanobody”, which is a very small, very high-affinity single-chain antibody. They replaced the CTE of either Cdc11 or Shs1 with a nanobody that recognized GFP, and expressed a GFP-Bni5 fusion in these strains. In both cases, tethering Bni5 to the septin via the nanobody obviated the need for the CTE.

Finally, the authors asked why it is important for Bni5 to be located on the septin rods. Previous work had suggested that Bni5 recruits Myo1 (myosin), an important component of the contractile ring at the bud neck. They used the same nanobody constructs to test this, simply expressing GFP-Myo1 in the strains where the nanobody replaced the CTEs of Cdc11 or Shs1. Sure enough, tethering Myo1 to the terminal septins eliminated the need for Bni5.

So we now know that tails are absolutely essential for the functions of the alternative terminal septins Shs1 and Cdc11. These fishing poles let them hold on to the Bni5 “bait,” which in turn catches Myo1 to provide the muscle for cytokinesis to occur. Since septins are so highly conserved, it’s probable that these results will be directly applicable to higher organisms: there are mammalian septins that also occupy the end positions of septin rods, analogous to Cdc11 and Shs1. And that’s no fish story!

Not only septins use their tails to fish.

by Maria Costanzo, Ph.D., Senior Biocuration Scientist, SGD

The Gift for the Man Who Has Everything

July 8, 2015

Gifts can be hard to buy for some people. They have everything they need and not many outside interests. What to do?

Having trouble finding that personal gift for that impossible to buy for person? How about a vanity protein with their name written right into the amino acid sequence? Image by D. Barry Starr

You could name a star after them or get them some knick knack they don’t need. Or you could design a personalized protein that has their name in it, solve the structure and present them with the picture.

This is what Deiss and coworkers did to celebrate the 50th birthday of their colleague Andrei N. Lupas, a key figure in studying coiled-coil proteins. They created a personalized protein based on Gcn4 from Saccharomyces cerevisiae. And of course Gcn4 is a coiled-coil protein!

Coiled-coil proteins are the perfect clay for biosculpting a personalized protein. They follow a relatively simple set of rules which makes it easy to predict how they will fold. There isn’t much of the “protein folding problem” with these user-friendly proteins.

Basically these proteins consist of repeated 7 amino acid motifs that each form an alpha helix. They have hydrophobic residues down one face of the helix so that they will tend to oligomerize with each other to keep the hydrophobic residues away from the water. These helices spontaneously coil up like a rope (hence their name).

The 7 amino acids of a repeat are usually represented as a-b-c-d-e-f-g and are arranged in the pattern hxxhcxc, with h being hydrophobic residues, c being charged residues and x being most any other amino acid. So a and d must be hydrophobic, and e and g charged. That’s pretty much it!

Deiss and coworkers used the name Andrei N. Lupas to create a personalized coiled coil. They replaced 12 amino acids in Gcn4 with the amino acids represented by the letters in his name. Well, they were able to do that for most of the letters.

First off, they had to Roman things up a bit and turn the U into a V (there is no amino acid with the single amino acid code U). So here is the amino acid sequence they used and how they lined it up with the 7-amino acid repeats:

In this arrangement, the hydrophobic residues are asparagine, isoleucine, and valine, and the charged residues are aspartic acid, glutamic acid, proline, and serine. Obviously the last two are not optimal, especially the proline. Proline has an especially rigid conformation and is known to wreak havoc with alpha helices.

When the authors analyzed the protein, they found that as predicted, the proline disrupted the part of the alpha helix with which it was associated. But not enough to completely destroy the coiled coil structure. X-ray diffraction showed that this protein was still able to trimerize properly. They had created a distorted but functional personalized protein. What other kind would anyone want!

And it isn’t as if proline is completely absent from the heptad repeats of coiled-coil proteins. A quick search by the authors found two viral fusion proteins, 1ZTM and 3RRT, that could form a trimer even though they too had prolines. In both of these proteins the proline is in the f position.

They also found 4 dimers with a proline in a heptad repeat. In these cases the proline is at b or c. So no known natural coiled-coil proteins have a proline at the e position. Talk about personalized!

How cool is all of this, and who wouldn’t want a protein of their very own? Unfortunately, not everyone can easily have one.

For example, President Barack Obama would have real trouble since there are no amino acids designated with a B or an O and there is no obvious way to transform these letters into ones that are present in the single letter code. Jeb Bush is out too, but maybe we can do something with Hillary Clinton. Let’s see if we can line up the amino acids of her first name to create a personalized Gcn4 just for her.

“HILLARY” isn’t too bad by itself. All the letters are amino acids (yay) and a and d are hydrophobic (isoleucine and alanine). Aspartic acid works very well for e and while probably not perfect, histidine isn’t too bad for g. The tyrosine at position f is not ideal either but is way better than a proline. This thing might replace one heptad repeat in Gcn4 without causing too many problems.

So what about your name? Can you turn yours into a heptad repeat to create your own personalized Gcn4? 

by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics

Where’s That Protein?

July 1, 2015

Waldo will always be hard to find, but we now know exactly where to find more than 4,000 S. cerevisiae proteins, thanks to new methods and an analysis pipeline. Image by William Murphy via Wikimedia Commons

You might be familiar with the Where’s Waldo book series, especially (but not necessarily) if you have kids. They challenge the reader to find Waldo within huge, intricately drawn groups of people. Even though Waldo has his distinctive characteristics—glasses and a striped shirt and hat—he can be very hard to find.

Now imagine that the drawings shift under different conditions, so that Waldo could be in any of several places at different times. And imagine that you’re not just looking for Waldo, but also for thousands of other unique individuals—all tagged in the same way. This is the challenge faced by researchers who want to know where each protein in a cell is located and how its location and abundance respond to different environments.

But, as genetic, robotic, microscopic, and computational tools get more and more sophisticated, it’s becoming possible to pinpoint Waldo and his companions even as they move around within the jam-packed yeast cell.

In two new papers, scientists from the University of Toronto describe a huge effort that entailed over 9 billion quantitative measurements to find the location and measure the abundance of more than 4,000 S. cerevisiae proteins. Chong and colleagues wrote in Cell about the approach and experimental methods, while Koh and colleagues published in G3 about the computational methods and the database that houses all the data, called CYCLoPs for Collection of Yeast Cells and Localization Patterns.

This work couldn’t have been done without a valuable resource that was created some years ago: the yeast GFP collection. It’s a set of strains, each with the green fluorescent protein gene fused to the 3’ end of one open reading frame to express a GFP fusion protein from the ORF’s native promoter. Not every yeast protein can be detected this way: some are expressed too weakly, while others may actually be destabilized by their GFP tags. Still, more than 4,100 of these fusion genes—71% of the proteome—give a visible GFP signal in the cell.

The researchers started with these ~4,100 strains and transformed each with a plasmid expressing red fluorescent protein. This allowed them to visualize the boundaries of each cell. Then they got to work, taking pictures of at least 200 cells of each strain and developing an automated pipeline to analyze them. They ended up analyzing 300,000 micrographs of more than 20 million cells, beating the few dozen Where’s Waldo books by a long shot!

The scientists looked at each protein in wild type, in a mutant strain, and in the presence of two drugs. The mutant strain they studied was deleted for RPD3, which encodes a lysine deacetylase that regulates the stability and interactions of histones and other proteins. The drug treatments were done with several different concentrations of rapamycin (an inhibitor of the TORC1 complex, which is an important regulator of cell growth) or hydroxyurea (a DNA replication inhibitor).

The end result was an enormous collection of data, now stored in the CYCLoPs database, that shows the abundance of each protein in each of 16 cellular compartments under all of these different conditions. These data are much more quantitative and consistent than any protein abundance or localization data that had been obtained before. They are stored in such a way that measurements within single cells can be accessed, and the database can be searched by patterns of changes in localization or abundance as well as for data on a particular protein.

The authors came up with some innovative methods for visualizing this immense dataset to get a high-level overview. One of their most surprising findings was just how many proteins localize to multiple places. We tend to think of the cell as a tidy place where each protein has one particular location, but Chong and colleagues found that it’s extremely common for proteins to be in several spots.

Most often, when proteins are present in more than one place, those places are the nucleus and the cytoplasm. Some proteins had already been shown in small-scale studies to be present in both compartments, or to shuttle between them. But the authors saw an astounding 1,029 proteins localizing to both the nucleus and cytoplasm under standard conditions in wild-type cells.

Not counting the proteins in the nucleus and cytoplasm, another 511 proteins localized to more than one place. Some were seen in up to five different subcellular compartments.

The proteins with multiple locations, as a group, were more likely than the average protein to be phosphorylated. This made sense, because phosphorylation of proteins is known to regulate their localization. And many of these proteins themselves had regulatory roles, controlling processes such as cell division.

The fact that data were collected from single cells means that we can use them to uncover the dynamics of protein movement. For example, if a protein was scored as localizing to both the nucleus and the cytoplasm, does that mean there’s a pool of it in both places at all times, or does it move back and forth? The single-cell data for two representative proteins, Mcm2 and Whi5, showed clearly that any one cell has each of these proteins in either the nucleus or cytoplasm, but not both. But some other proteins hang out in both places at once. And the dynamics of still more roving proteins are just waiting to be revealed.

Researchers will be mining the CYCLoPs resource to find detailed information about specific proteins, pathways, and processes for years to come. The data gathered in the rpd3 mutant and under rapamycin and hydroxyurea treatment served as proof of principle that the system can be used to assess the effects of a variety of mutations and drugs.

So this study puts a spotlight on Waldo in each picture and makes it simple to find him and his friends. This mass of data on where proteins are and how they move around has far-reaching implications for yeast systems biology, and the methodology can now be applied to cells of other organisms as well. In the coming weeks, we’ll make it even simpler for you to access these data from SGD, by adding links for individual proteins to the CYCLoPs database.

by Maria Costanzo, Ph.D., Senior Biocuration Scientist, SGD

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