At A Glance
Name: Ahna Skop
Position: Assistant professor of Genetics, University of Wisconsin-Madison, since 2004
Published a paper in July 2 issue of Science: Dissection of the mammalian midbody proteome reveals conserved cytokinesis mechanisms.
Background: Postdoctoral Research: University of California Berkeley, 2000-2004
Ph.D. in Cell and Molecular Biology, University of Wisconsin-Madison, 2000
BA in Biology and Ceramics, Syracuse University, 1994
How did you get involved with proteomics?
As a grad student here in Wisconsin in John White’s lab I was working on cytokinesis and spindle alignment. I noticed a lot of proteins were localizing to a structure called the midbody, and I didn’t know what they were. Around that time, I read an article by Hartwell and Murray in Nature [From Molecular to Modular Cell Biology], in which they said proteomics will be the hottest thing in 2003. That one article really changed my idea of how to approach the problem that I was interested in. I went searching on PubMed to find everything about proteomics. I got really excited about it, and as I got to know more about it, I thought ‘I can take the structure and find the proteins in it very quickly.’ At the time when I got to Berkeley [as a postdoc with Rebecca Heald and Barbara Meyer], I figured out how to isolate the midbodies. [There were] probably about 100 proteins on a Western. I didn’t know about MudPIT at that time, but I thought there must be a better way to sequence proteins than a gel. So I did a PubMed search for “organelle proteomics,” and up pops John Yates. I just e-mailed him, and [the rest is] History, it was the melding of cell biologists with proteomics people.
How did you collaborate with John Yates?
We initially sent samples, and then I actually went down and did a run and learned the whole procedure myself. We did four runs that are in my paper, and I did one of them together with Hongbin Liu, a postdoc who did most of the mass spec stuff.
[He] put [the midbody samples] in the machine and pumped out the data. We had to make sure the peptides we got from each run were real. We were not seeing certain proteins like kinases, initially, in these big complexes, so we removed filters that they had defaulted. This actually maximized our output.
Was it a problem that the mass spectrometrists did not know so much about the biology?
In essence, that was really good because they were unbiased to the proteins. They didn’t know what the midbody was, they didn’t know about cytokinesis all that well, they didn’t know any proteins, and they did it, and then when they first sent the first run back, I almost passed out. A third of them were known, and that said to me, this is really great. I couldn’t even sleep for weeks, I was very, very excited, because I couldn’t believe how easy it actually was. We probably did about 12 runs after isolating different midbody preps. We optimized our prep in that time to get about a third of the proteins that were known to be involved in the process. […] I think that was good for both of us, because I learned the chemistry and some bioinformatics, and they learned the biology.
How many proteins were in the total list?
577. We combined four mass spec runs into one. If you compare two runs, most of the proteins were there, give or take a few, [but] a lot of the really low-abundance ones like kinases didn’t always show up in every run, so we averaged them so we wouldn’t lose things based on different biochemical isolations and inconsistencies in the mass spec.
What did you do with the information at that point?
A lot of bioinformatics, and many, many months spent on PubMed. I looked through the list and by protein prediction analysis, [we] put them into functional categories: actin-associated proteins, microtubule-associated proteins, secretory proteins, kinases. But the midbody is a transient structure, and it also contains other organelles [like] mitochondria and ribosomes, and there was some contamination with nuclei. I separated everything that was an organelle like ribosomes and put them into different categories, and then looked at the data again. At that point, I got 160 [proteins] that I would potentially [study] for function in cytokinesis.
What functional studies did you do?
At the time, I knew that using mammalian cells was expensive [for] knock[ing] out genes. I had worked in C. elegans before, and I thought C. elegans are cheap and quick, so why not find the homologs. That proved to be very fruitful. The amazing thing is that 90 percent of [the proteins] have homologues in worms [and] it suggests that cytokinesis is very well conserved. It’s actually probably one of the most ancient events to[wards] multi-cellularity — you need to divide yourself to become a multicellular organism. I used RNAi in C. elegans as a screen to figure out whether or not what I got from mass spec was real.
You recently set up your own lab at the University of Wisconsin-Madison. Are you going to do any proteomics there?
I still have an ongoing collaboration with the Yates lab. They also have a facility here in Madison at the biotech center; they have a tandem mass spec machine, very similar to the Yates lab. I will probably use both facilities. I have other things planned for isolating different cell cycle components. I have already worked with the Yates lab, we have done the metaphase spindle already, that’s something I’m going to work on in my lab. And I would like to branch out. The biochemical isolation is really the limiting step. I can take the proteins identified now, it’s like a parts list, and now we can do IPs, TAP-tags and figure out things that interact with the proteins that I am focusing on. Getting a bigger interacting protein network for cell division and cytokinesis, that’s my ultimate goal.
That’s a big project…
It’s a lot of data. I have just hired a grad student who knows a lot of bioinformatics and computing, and that for me is the next step. I have all this data, what do I do? The metaphase spindle list at this point is 1,200 proteins. It’s too much to Blast one by one, which is how I did my initial work. So automating all this will advance it much faster. I need to involve chemists and bioinformaticians into my lab, who want to learn biology. But they can help me out as well; we can learn from each other, and I think that’s how my project […] worked really well, by learning from other people in different fields. And I think I’d like to have my lab that way and work on projects like that. Using this technology to understand and answer the questions I have in biology.
What would you say are some of the difficulties in working across different disciplines?
I think it’s the jargon. You know your biology words, but it’s really hard sometimes to understand each other. It’s like going to a foreign land and trying to order a salad or a sandwich, it’s hard. But then once you figure it out a little bit, it gets easier. I think that’s the biggest obstacle, knowing what to ask, and what kinds of words or technologies they may have. You really need to read up on it and understand where each other [are] coming from. I think that’s really the biggest hurdle. At the University of Wisconsin here they have a genome sciences training program that involves all departments like math and computer sciences and physics and biochemistry and genetics. I think that’s where biology is going. I think a lot of schools now are creating programs like that, where you have the grad students all integrated into all the different departments. There is just so much out there that biologists could access, but you don’t know about it. I would have never known [about proteomics] if I hadn’t done a PubMed search, and I had to have the key word, “organelle proteomics”. […] [Only when] I put those two terms together, John Yates popped up. I think there will be a lot more crossing over in the future. It’s just beginning.