New & Noteworthy
March 6, 2014
Imagine the heater at your house is run by a homemade copper-zinc battery. You are counting on a delivery of a copper solution that will keep the thing going. Unfortunately it fails to come, which means the battery doesn’t work and you are left out in the cold.
Turns out that something similar can happen in cells too. The respiratory chain that makes most of our energy needs copper to work. In a recent study, Ghosh and coworkers showed that if Coa6p doesn’t do its job delivering copper to the respiratory chain, the cell can’t make enough energy.
This isn’t just interesting biology. In this same study, the researchers showed that mutations in the COA6 gene cause devastating disease in humans and zebrafish. And their discovery that added copper can cure the “disease” in yeast just might have therapeutic applications for humans.
The respiratory chain is a group of large enzyme complexes that sit in the mitochondrial inner membrane and pass electrons from one to another during cellular respiration. This process generates most of the energy that a cell needs. Hundreds of genes, in both the nuclear and mitochondrial genomes, are involved in keeping this respiratory chain working.
Yeast has been the ideal experimental organism for studying these genes, because it can survive just fine without respiration. If it can’t respire for any reason, yeast simply switches over to fermentation, generating the alcohol and CO2 byproducts that we know and love.
Human cells aren’t as versatile though. Genes involved in respiration can cause mitochondrial respiratory chain disease (MRCD) when mutated. This is one of the most common kinds of genetic defect, with over 100 different genes known so far that can cause this phenotype.
Ghosh and colleagues wondered whether there were as-yet-unidentified human genes involved in maintaining the respiratory chain. They reasoned that any such genes would be highly conserved across species, because they are so important to life, and that the proteins they encoded would localize to mitochondria.
One of the candidates, C1orf31, caught their eye for a couple of reasons. First, some variations in this gene had been found in the DNA of a MRCD patient. And second, the yeast homolog, COA6, encoded a mitochondrial protein that had been implicated in assembly of one of the respiratory complexes, Complex IV or cytochrome c oxidase.
They first did some more detailed characterization of COA6 in yeast. They were able to verify that the coa6 null mutant had reduced respiratory growth because it had lower levels of fully assembled Complex IV.
They also looked to see what happens in human cell culture. When they knocked down expression of the human homolog, they also saw less assembly of Complex IV. This suggested that the function of this protein is conserved across species.
Next they turned to a sequencing study of an MRCD patient who had, sadly, died of a heart defect (hypertrophic cardiomyopathy) before reaching his first birthday. The sequence showed a mutation in a conserved cysteine-containing motif of COA6. To see whether this might be the cause of the defect, they created the analogous mutation in yeast COA6. The mutant protein was completely nonfunctional in yeast.
To nail down the physiological role of COA6 in a multicellular organism, they turned to zebrafish. The embryos of these fish are transparent, so it’s easy to follow organ development. Given the phenotype, the fact that they can live without a functional cardiovascular system for a few days after fertilization was important too.
When the researchers knocked down expression of COA6 in zebrafish, they found that the embryos’ hearts failed to develop normally and they eventually died. The abnormal development of the fish hearts paralleled that seen in the human MRCD patient carrying the C1orf31/COA6 mutation. And reduced levels of Complex IV were present in the fish embryos.
Going back to yeast for one more experiment, Ghosh and colleagues decided to see whether Coa6p might be involved in delivering copper to Complex IV. They knew that Complex IV uses copper ions as a cofactor, and furthermore Coa6p had similarities to several other yeast proteins that are known to be involved in the copper delivery.
They tested this by supplying the coa6 null mutant with large amounts of copper. Sure enough, its respiratory growth defect and Complex IV assembly problems were reversed. The delivery of copper kept the energy flowing in these cells. And this result showed that Coa6p is involved in getting copper to Complex IV.
These experiments showcase the need for model organism research even in the face of ever more sophisticated techniques applied to human cells. The mutation in human C1orf31/COA6 was discovered in a next-generation sequencing study, but yeast genetics established the relationship between the mutation and its phenotype. The zebrafish system allowed the researchers to follow the effects of the mutation in an embryo from the earliest moments after fertilization. And the rescue of the yeast mutant by copper supplementation offers an intriguing therapeutic possibility for some types of MRCD. Just another testament to the awesome power of model organism research!
YeastMine now lets you explore human homologs and disease phenotypes. Enter “COA6” into the template Yeast Gene -> OMIM Human Homolog(s) -> OMIM Disease Phenotype(s) to link to the Gene page for human COA6 (the connection between COA6 and disease is too new to be represented in OMIM). To browse some diseases related to mitochondrial function, enter “mitochondrial” into the template OMIM Disease Phenotype(s) -> Human Gene(s) -> Yeast Homolog(s).
March 4, 2014
You can now use SGD’s advanced search tool, YeastMine, to find the human homolog(s) of your favorite yeast gene and their corresponding disease associations. Or, begin with your favorite human gene or disease keyword and retrieve the yeast counterparts of the relevant gene(s). As an example, you can search for the S. cerevisiae homologs of all human genes associated with disorders that contain the keyword “diabetes” (view search).
We have recently loaded data from OMIM (Online Mendelian Inheritance in Man) into our fast, flexible search resource, YeastMine, and provided 3 predefined queries (templates) that make it simple to perform the above searches. Newly updated HomoloGene, Ensembl, TreeFam, and Panther data sets are used to define the homology between S. cerevisiae and human genes. The results table provides identifiers and standard names for the yeast and human genes, as well as OMIM gene and disease identifiers and names. As with other YeastMine templates, results can be saved as lists and analyzed further. You can also now create a list of human names and/or identifiers using the updated Create Lists feature that allows you to specify the organism representing the genes in your list. The query for yeast homologs can then be made against this list.
In addition to human disease homologs, we have incorporated fungal homolog data for 24 additional species of fungi. You can now query for the fungal homologs of a given S. cerevisiae gene using the template “Gene –> Fungal Homologs.” This fungal homology data comes from various sources including FungiDB, the Candida Gene Order Browser (CGOB), and PomBase, and the results link directly to the corresponding gene pages in the relevant databases, including Candida Genome Database (CGD) and Aspergillus Genome Database (AspGD).
All of the new templates that query human and fungal homolog data can be found on the YeastMine Home page under the new tab “Homology.” These templates complement the template “Gene → Non-Fungal and S. cerevisiae Homologs” that retrieves homologs of S. cerevisiae genes in human, rat, mouse, worm, fly, mosquito, and zebrafish.
Watch the Human Disease & Fungal Homologs in SGD’s YeastMine tutorial (below) to learn how to find and use these new templates.
February 25, 2014
Most people know that Incans relied on human runners to get messages across their empire. Basically they had runners stationed at various places and one runner would hand the message off to the next. This relayed message could then quickly travel across the country.
As shown in a new study by Nadal-Ribelles and coworkers, it turns out that something similar happens in yeast when the CDC28 gene is turned up in response to high salt. In this case, the runner is the stress activated protein kinase (SAPK) Hog1p and it is stationed at the 3’ end of the gene. When the cell is subjected to high salt, the message is relayed from the 3’ end of the CDC28 gene to its 5’ end by the Hog1p kinase. The end result is about a 2-fold increase in the amount of Cdc28p made, which allows the cell to enter the cell cycle more quickly after the salty insult.
Unlike the Incans who had their paths all set up in front of them, poor Hog1p has to build its own path. It does this by activating a promoter at the 3’ end of the CDC28 gene that produces an antisense long noncoding RNA (lncRNA) that is needed for the transfer of the Hog1p. It is as if our Incan runner had to build a bridge over a gorge to send his message.
This mechanism isn’t peculiar to the CDC28 gene either. The authors in this study directly show that something similar happens with a second salt sensitive gene, MMF1. And they show that a whole lot more lncRNAs are induced by high salt in yeast as well.
Nadal-Ribelles and coworkers started off by identifying coding and noncoding regions of the yeast genome that respond positively to high salt. The authors found that 343 coding regions and 173 noncoding regions were all induced at 0.4 M NaCl. Both coding and noncoding regions required the SAPK Hog1p for activation.
The authors next focused on CDC28 and its associated antisense lncRNA. After adding high salt, Nadal-Ribelles and coworkers found that Hog1p was both at the start and end of the CDC28 gene – as would be expected, since both CDC28 and the antisense lncRNA required this kinase for transcriptional activation.
Things got interesting when they were able to prevent the lncRNA from being made. When they did this, Hog1p was missing from both the 5′ and 3′ ends of the CDC28 gene and as expected, activation was compromised. But Nadal-Ribelles and coworkers showed that expressing the lncRNA from a plasmid did not allow for CDC28 activation. It appears that where the lncRNA is made is just as important as whether it is made.
Through a set of clever experiments, the authors showed that not only does the lncRNA need to be made in the right place, but it needs to be activated in the right way. When they set up a system where the lncRNA was induced in the right place using a Gal4-VP16 activator, CDC28 was not induced by high salt. A closer look showed that this was most likely due to a lack of Hog1p at the start of the CDC28 gene.
The situation was different when they activated the lncRNA with a Gal4-Msn2p activator which uses Hog1p to increase expression. In this case, CDC28 now responded to high salt and Hog1p was present at both the start and end of the CDC28 gene. But this activation went away if they added a terminator which prevented the full length lncRNA from being made.
Phew, that was a lot! What it means is that for there to be a Hog1p at the business end of the CDC28 gene, there needs to be one at the 3’ end. It also means that for the Hog1p to get to the start of the CDC28 gene, the antisense lncRNA needs to be made.
This would all make sense if maybe the lncRNA was involved in DNA looping, which could get the Hog1p from the end of CDC28 to the start where it can do some good. Nadal-Ribelles and coworkers showed that this indeed was the case, as CDC28 activation required SSU72, a key looping gene. When there was no Ssu72p in a cell, salt induction of CDC28 was severely compromised.
So it looks like an antisense lncRNA in yeast is being used as part of a looping mechanism to provide the cell with a quick way to start dividing once it has dealt with its environmental insult. The authors show that yeast that can properly induce their CDC28 gene enter the cell cycle around 20 minutes faster than yeast that cannot induce the gene. The cells are poised for a quick recovery.
And this is almost certainly not merely a yeast phenomenon. Some recent work in mammalian cells has implicated lncRNAs in recruiting proteins involved in controlling gene activity through a looping mechanism as well (reviewed here). Now that the same thing has been found in yeast, scientists can bring to bear all the powerful tools available to dissect out the mechanism(s) of lncRNA action. And that’s far from a loopy idea…
February 21, 2014
Did you know you can find and contribute teaching and other educational resources to SGD? We have updated our Educational Resources page, found on the SGD Community Wiki. There are links to teaching resources such as classroom materials, courses, and fun sites, as well as pointers to books, dedicated learning sites, and tutorials that can help you learn more about basic genetics. Many thanks to Dr. Erin Strome and Dr. Bethany Bowling of Northern Kentucky University for being the first to contribute to this updated site by providing a series of Bioinformatics Project Modules designed to introduce undergraduates to using SGD and other bioinformatics resources.
We would like to encourage others to contribute additional teaching or general educational resources to this page. To do so, just request a wiki account by contacting us at the SGD Help desk – you will then be able to edit the SGD Community Wiki. If you prefer, we would also be happy to assist you directly with these edits.
Note that there are many other types of information you can add to the SGD Community Wiki, including information about your favorite genes, protocols, upcoming meetings, and job postings. The Community Wiki can be accessed from most SGD pages by clicking on “Community” on the main menu bar and selecting “Wiki.” The Educational Resources page is linked from the left menu bar under “Resources” from all the SGD Community Wiki pages. For more information on this newly updated page, please view the video below, “Educational Resources on the SGD Community Wiki.”
February 18, 2014
There are two very different kinds of sports in the Winter Olympics (and in all sporting competitions really). In one set, it is the athletes alone out on the ice or sliding down the slope, trying to get the best time they can. They can only use themselves as the motivator.
In another set of sports, like speed skating, athletes compete directly with one another. Here they can use each other to push themselves to go faster, farther, etc.
The key to each is obviously the proximity of other athletes. If there are a bunch of athletes around you, you will all do better by feeding off each other’s signals. If you are by yourself, then only you can produce the signals to motivate yourself to go faster.
Youk and Lim show in a new study that the same sort of thing happens in cells that can both secrete and sense the same signal. If there aren’t a lot of cells around they tend to signal themselves, but in a crowded place, they are all signaling each other.
This may seem a bit esoteric but it really isn’t. These sorts of “secrete-and-sense” systems are common in biology. Cell types from bacteria to our own T cells have them, and they allow for a surprisingly wide range of responses. Understanding how these systems work will explain a lot of biology and, perhaps, help scientists create new sensing systems for bioengineered beasts.
Youk and Lim used our favorite organism Saccharomyces cerevisiae to study this widespread signaling system. They created a bevy of strains that can either secrete and sense alpha factor or that can only sense the pheromone. They grew varieties of these two strains together under various conditions to determine when the “secrete-and-sense” strains could also signal to the “sense only” strains. Like our athletes, the cell concentration was important. But so too were the levels of alpha factor and receptor.
The authors first created a strain that senses the presence of alpha factor with the Ste2p receptor and in response turns on GFP through the FUS1 promoter. (The strain is deleted for FAR1 to prevent cell cycle arrest.) As expected, increasing amounts of alpha factor resulted in increased levels of GFP.
It is from this strain they created their “secrete-and-sense” and “sense only” strains. The “secrete-and-sense” strain included a doxycycline inducible promoter driving the alpha factor gene. The more doxycycline, the more alpha factor it makes, resulting in more GFP. To tell the two strains apart in experiments, they added a second reporter, mCherry, under a constitutive promoter to the “sense only” strain. Now in their experiments they can distinguish between the strains that glow only green and those that glow red and, sometimes, green.
The first experiment was simply to see what effect differing cell and alpha factor concentrations had on the two strains’ ability to glow green. At low cell and doxycycline concentrations, only the “secrete-and-sense” strain glowed green. This makes sense, as too little alpha factor was made to get to the relatively distant neighbors. At high cell and doxycycline concentrations, both glowed green almost indistinguishably. Here the system was flooded with enough alpha factor for everyone to respond.
The results were less binary at either low cell and high doxycycline concentrations or high cell and low doxycycline concentrations. Under either of these conditions, the “sense only” strain did glow green although at a much slower rate.
Youk and Kim didn’t stop there. They also tested whether the amount of receptor affected these results. When the two strains expressed high levels of receptor, the amount of alpha factor didn’t matter at low cell concentrations—only the “secrete-and-sense” strain glowed green. This makes sense as the strain can quickly suck up any amount of alpha factor it makes. Again at high cell concentrations the differences disappear.
In a final set of experiments the authors created positive feedback loops and signal degradation systems, which are both very common in nature. The positive feedback loop was created by putting the doxycycline activator, rtTA, under the control of doxycycline, and a signal degradation system was engineered using Bar1p, a protease that degrades alpha factor. Using these systems they were able to show that at low cell concentration, low Bar1p expression, and strong positive feedback, individual cells were either on or off. This sort of activity may be important in nature, where under certain conditions a response may be beneficial and in others a response may not. This bet hedging means that the population can survive under both sets of conditions.
It is amazing that such a simple set of conditions can lead to so many different responses, almost as varied as the performances of Olympic athletes. These findings not only help to explain how these deceptively simple systems work and why they are so common in nature, but might also be incredibly useful in setting up synthetic secrete-and-sense circuits for biotechnology applications.