Advertisements
Feeds:
Posts
Comments

Posts Tagged ‘cullin ubiquitin ligase family’


Ubiquitin researchers win Nobel

Larry H. Bernstein, MD, FCAP, Curator

 

Ciechanover, Hershko, and Rose awarded for discovery of ubiquitin-mediated proteolysis

http://www.the-scientist.com/?articles.view/articleNo/23111/title/Ubiquitin-researchers-win-Nobel/

Nature Cell Biology 2, E171 (2000) http://dx.doi.org:/10.1038/35036412

The Royal Swedish Academy of Sciences has decided to award the Nobel Prize in Chemistry for 2004 “for the discovery of ubiquitin-mediated protein degradation” jointly to

Aaron Ciechanover
Technion – Israel Institute of Technology, Haifa, Israel,

Avram Hershko
Technion – Israel Institute of Technology, Haifa, Israel and

Irwin Rose
University of California, Irvine, USA

 

Proteins labelled for destruction

Proteins build up all living things: plants, animals and therefore us humans. In the past few decades biochemistry has come a long way towards explaining how the cell produces all its various proteins. But as to thebreaking down of proteins, not so many researchers were interested. Aaron Ciechanover, Avram Hershko and Irwin Rose went against the stream and at the beginning of the 1980s discovered one of the cell’s most important cyclical processes, regulated protein degradation. For this, they are being rewarded with this year’s Nobel Prize in Chemistry.

Aaron Ciechanover, Avram Hershko and Irwin Rose have brought us to realise that the cell functions as a highly-efficient checking station where proteins are built up and broken down at a furious rate. The degradation is not indiscriminate but takes place through a process that is controlled in detail so that the proteins to be broken down at any given moment are given a molecular label, a ‘kiss of death’, to be dramatic. The labelled proteins are then fed into the cells’ “waste disposers”, the so called proteasomes, where they are chopped into small pieces and destroyed.

 

Avram Hershko is an Israeli biochemist and winner of the 2004 Nobel Prize for Chemistry.

Hershko (born December 31, 1937) was born as Hersko Ferenc in Karcag, Hungary. In 1950, Hershko and his family emigrated from Hungary to Israel, where he adopted the name Avram. Hershko received his M.D. and Ph.D. from the Hadassah Medical School of the Hebrew University. In 1965-67, Hershko worked as a physician in the Israel Defense Forces.

In 1969-72, Hershko was a postdoctoral fellow with the late Dr. Gordon Tomkins at the University of California, San Francisco.

In 1987, Hershko was awarded the Weizmann Prize for Sciences, an honor given to top Israeli scientists. In 1994, he won the Israeli Prize for his contributions to Israeli society through biochemistry and medicine.

In 2004, Hershko was awarded the Nobel Prize in Chemistry “for the discovery of ubiquitin-mediated protein degradation.”

Ciechanover was born in Haifa, a year before the establishment of Israel. He is the son of Bluma (Lubashevsky), a teacher of English, and Yitzhak Ciechanover, an office worker.[1] His family were Jewish immigrants from Poland before World War II.

He earned a master’s degree in science in 1971 and graduated from Hadassah Medical School in Jerusalem in 1974. He received his doctorate in biochemistry in 1981 from the Technion – Israel Institute of Technology in Haifa before conducting postdoctoral research in the laboratory of Harvey Lodish at the Whitehead Instituteat MIT from 1981-1984. He is currently a Technion Distinguished Research Professor in the Ruth and Bruce Rappaport Faculty of Medicine and Research Institute at the Technion.

Ciechanover is a member of the Israel Academy of Sciences and Humanities, the Pontifical Academy of Sciences, and is a foreign associate of the United States National Academy of Sciences.

As one of Israel’s first Nobel Laureates in Science, he is honored in playing a central role in the history of Israel and in the history of the Technion – Israel Institute of Technology

 

  • Ciechanover, A., Hod, Y. and Hershko, A. (1978). A Heat-stable Polypeptide Component of an ATP-dependent Proteolytic System from Reticulocytes. Biochem. Biophys. Res. Commun. 81, 1100–1105.
  • Ciechanover, A., Heller, H., Elias, S., Haas, A.L. and Hershko, A. (1980). ATP-dependent Conjugation of Reticulocyte Proteins with the Polypeptide Required for Protein Degradation. Proc. Natl. Acad. Sci. USA 77, 1365–1368.
  • Hershko, A. and Ciechanover, A. (1982). Mechanisms of intracellular protein breakdown. Annu. Rev. Biochem. 51, 335–364.

Interview Transcript

Transcript from an interview with the 2004 Nobel Laureates in Chemistry Aaron Ciechanover, Avram Hershko and Irwin Rose, on 9 December 2004. Interviewer is Joanna Rose, science writer.

Aaron Ciechanover, Avram Hershko and Irwin Rose during the interview

Dr Ciechanover, Dr Hershko and Dr Rose, my congratulations to the Nobel Prize and welcome to this interview. I know that you two started as medical doctors but you are in science now, and you get the prize for scientific research. How come you left medicine?

Avram Hershko: Well, I started out as a medical student, I wanted to be a doctor. And during my medical studies I studied biochemistry. That was one of the subjects that every medical student studies, so I liked it very much. I liked, you know, the whole concept of biochemistry, of looking for chemical processes in cells, so we had, we could take off one year from the studies to spend in research in the lab. I also found a very good teacher, Jacob Mager, and I wanted to spend it with him, so I did. That’s how I got involved in biochemistry. Afterwards, I finished my medical studies but already, I, after that one year, I knew that I will go to biochemistry and not to practical medicine. That’s how I started. So, it’s, it’s, like all things in life, it starts by some kind of accident or so, that was the accident, I met a subject during my studies that I liked.

And a good teacher.

Avram Hershko: And a very good teacher.

Was it also a topic, an issue that you were interested in?

Avram Hershko: No, no, not yet, not yet. Mager was interested in many subjects so that was … Actually, I continued with him after my army service as a doctor, and during the course of a couple of years I evolved in four completely different subjects, protein, synthesis, purine metabolism, and a certain disease called glucose-6-phosphate dehydrogenase deficiency, because he was interested in many things, so that gave me a very good background, a very, very, you know, very good basic background.

What about you, Dr Ciechanover?

I fell in love with biochemistry …

Aaron Ciechanover: Surely you can repeat the story verbatim. The same very story, I started in the same medical school, and after four years I decided to try and taste, I fell in love with biochemistry, too.

Like ten years later.

Aaron Ciechanover: Exactly ten years later, and I also decided to taste it, and at that time at medical school they let students take one year off for medical studies, try some research, so I went into biochemistry, same very story, different mentor. And, a wonderful mentor, and I studied lipids.

Also.

Aaron Ciechanover: Not proteins at all, and then exactly, made a decision, that that’s it. But I had, because of obligations to serve in Israel in the military as a physician. I completed my medical studies, went to serve in the army, but meanwhile, in between, I was looking already for a future mentor, in biochemistry, and Avram was at the time abroad, in the University of California in San Francisco, and I got rave recommendation, that he is a great teacher and a great biochemist, and I wrote him, and he was ready to accept me, and there started this story. More or less.

So did you go to the States?

Aaron Ciechanover: No, no, he came here. He returned to /- – -/ fellow, he started a new department in Haifa, which was a new medical school, I joined him, not initially on this project, on a different one because I still had to serve in the army. It’s a little bit complicated date-wise, but basically it’s the same very story, mentorship, the same footstep, without knowing where I am going.

You will never know.

Aaron Ciechanover: I never know, but it’s basically, ten years later the same very footsteps.

Oh, that’s funny. What about you, Dr Rose? How did you get …

Irwin Rose: I have an anomalist’s story. It doesn’t, there is no precedent for this. We moved from the east coast to the town of Spokane, Washington, when I was about 13 years old, and I did not adapt very well to the, to the style of the place, and I spent most of my time in the public library. And I enjoyed the company of the Journal of Biological Chemistry, because it was the book shaped thing, in those days, you know, it was the small journal …

Avram Hershko: At the age of 13?

Irwin Rose: No, you know, like a couple of years, you know, I was very unpopular with the other students, and so I read the Journal of Biological … the small, the small Journal of Biological Chemistry, and I found an article I thought I understood. And I read it and I thought I understood it to the point where I could make some suggestions as to how it would be, the experiment might work, and then I was very satisfied with that, and then I … I didn’t spend much time in science at that point. Went into the navy, got out of the navy, tried to go to the University of California at Berkeley, but due to the failure to find the bulletin board announcing the laboratory time of organic chemistry, I couldn’t do my organic chemistry there.

So I said OK, I’ll be a biochemist …

So I went back to the State College of Washington and there I was influenced, I would say, by the embryology teacher, who was a very strong personality in terms of academic research, he tried to encourage his students. Then I went to the University of Chicago and there was a big shock to learn all the new kinds of things that they were teaching there, in organic chemistry and that sort of stuff, and very attractive concepts, and things began to come together in my mind as to how chemistry worked and how I might be able to exploit some of the early kinds of techniques that were being used in organic chemistry into biochemistry, which was something I was attracted to, due to my reading of the Journal of Biological Chemistry. So at that point I signed up, there was a big gymnasium, and people were signing people up for which major you were going to go into. So I said OK, I’ll be a biochemist.

So I entered into the department of biochemistry, never saw the chairman of biochemistry because he was the appointed ambassador to Britain for the United States. So I floated around in the department of biochemistry and learned some interesting things, and then I began to … I never wanted to work with a mentor, because I always wanted to have my own reputation and be free to do what I wanted to do. So I worked with the weakest people in the department. Don’t make that public. No, I don’t mention the names, but … so I did that sort of thing and that way I came to learn some more independence, and once in a while I did a good experiment, and so I had more confidence that I could do research, and so that’s how it got started.

Avram Hershko: Can I mention the story that you did your PhD or eight counts per minute or …

Irwin Rose: Oh yes, well, in those days people weren’t counting, people counted on planchettes. And you …

Avram Hershko: Puckered.

Irwin Rose: Well, it could be, depends on they were flat.

Avram Hershko: You dried them, didn’t you?

Irwin Rose: Yes, you dried them out, depending … yes, that’s right. You had to dry them out, it depends on what the compound was, but if it was trillium you had to get an infinitely thin layer so that you wouldn’t get self-absorption.

Avram Hershko: It’s common, self-absorption on a planchette.

Irwin Rose: Did you guys do that, too?

Aaron Ciechanover: Yeah, yeah, yeah.

Avram Hershko: We had a counter with only three /- – -/ so we moved it like that …

Irwin Rose: Oh yeah, yeah.

Avram Hershko: … it was a big excitement.

Irwin Rose: So I wasn’t that primitive. You were doing these things in Israel, an advanced state.

Aaron Ciechanover: You came to our country.

Irwin Rose: I did. I came to Israel. But anyway, yes, so we did those things. And even if you had eight counts above background, if there were eight, there were eight. That’s right. So you could do some experiments. That’s how it worked out.

So how did you meet together?

Avram Hershko: Well, that’s another story. I got interested in protein degradation during my post-doc fellowship in San Francisco, and when I came back to Israel I continued with that, and at that time it was a very obscure field, you know. People, there were all kinds of, not too many people were interested in it. Those that were interested were not very good. So I looked for somebody, and so my first time I think I came up and I looked for somebody to spend a sabbatical with. I couldn’t find anybody that attracted me. So then I met Ernie at a meeting in 1976, one year before, before my sabbatical was due. And do you remember, we met in the breakfast, so I said can I, just began to talk …

Irwin Rose: It’s alright, I forgot.

… it turned out that he was interested in protein degradation. And that was a secret …

Avram Hershko: … breakfast table, so I knew who he was, he was very well known for his work on enzyme mechanism. That I knew, but then I asked him what are you interested in, in other things? So it turned out that he was interested in protein degradation. And that was a secret, it was a secret because he never published anything on it, and I asked him how come you never published anything, and so he said there is nothing worth publishing on protein degradation. So that’s what he said.

Irwin Rose: Yeah, that was my opinion. Well, because I hadn’t done anything, you don’t say it right.

Avram Hershko: OK. Well, that’s how I remember it. And anyhow, I liked that attitude very much, and asked, I asked him can I spend my sabbatical with you? And he said yes, so that’s how it started, and then Aaron, the same year he started his PhD with me, and after my sabbatical the following, the summer after my sabbatical, Aaron joined us, and then he joined us for a couple of summers afterwards, so that’s how, that’s how the whole connection started.

But how come you pick up an obscure field in science, to work on?

Irwin Rose: Well, I’ll tell you, because when I first worked at Yale, the guy who had a lab next to me had made the original observation that there was a protein, there was an energy dependent on protein breakdown. Now, nobody believed him, but he had made some pretty strong observations that if you …

Avram Hershko: Here, we could mention names.

Irwin Rose: Yes, Melvin Simpson. He made these important observations.

Aaron Ciechanover: He hardly believed himself, because when you go into discussion on the paper, you kind of come to a convoluted argument whether it’s a direct requirement or indirect. We can do the conclusion that it’s indirect.

When was it?

Avram Hershko: 1953, so …

Irwin Rose: So I didn’t read the paper, but I had this man in the laboratory next to me and he said, he made this observation and I got very interested in it. And worked on it for, on sabbatical, and when I went to England and when I went to Israel I got mice from Mager, it turned out the same guy, but he wasn’t there at the time, and … but I never found an energy dependence on the protein breakdown. And it turns out later on that a fellow named Art Haas who had been a post doc with me, made the observation that if you’re not careful when you break cells, there’s a lysosomal enzyme that degrades the ubiquitin. So I never would have found it, you know. Somebody else had to make the observation that you could make a self-resistent that … that would show an ATP dependence on protein breakdown. It was not for me, but I did work on it earlier, and that’s the, that’s why I told you that I’d never made any important observations.

But you three work together. How does it work, to do things together?

Irwin Rose: I don’t do anything.

You do nothing? Who is the worker?

Avram Hershko: Well, that’s, first of all, that’s not true. I remember that you made some ubiquitin preparation …

Irwin Rose: I did.

Avram Hershko: Yes, and it fell on the floor, and then you collected it up from the floor … yeah, yeah. That first step is to boil the extra, because ubiquitin is heat stable, so you boiled it but then it fell on the floor, but you picked it up and it was good, yeah.

Irwin Rose: It was good, nothing could destroy it.

Irwin Rose: It was a licence only enzyme.

Aaron Ciechanover: The /- – -/ can take it, but not the floor.

Avram Hershko: But, yeah, but when I came to his lab we already had his first step, which was the fractionation, well, the reticulocyte cell-free system system was actually established in the laboratory of somebody else, Alfred Goldberg in Harvard, but they didn’t …

Aaron Ciechanover: /Inaudible./

Avram Hershko: No, no, but, yeah, but he made it first, he made it first.

Aaron Ciechanover: The first publication was from Harvard, no doubt.

Avram Hershko: But then he didn’t progress, but then he didn’t do what he should have done, which is fractionation. It’s hard to purify right away, but ATP dependent enzyme, he never found it. And what we did was fractionation and constitution, so we already had this first step of separating it into two, two fractions, fraction one and fraction two.

… we didn’t really understand that it’s binding …

So during these two years between the beginning of ’77 when I write to your lab and December of ’79, when we made the breakthrough in your lab, we purified the component from fraction one, we found it a heat stable protein, and then you had a part in that, you also boiled ubiquitin, and then in Haifa we found that it gets … when we labelled it with iodine and we found it gets bound to proteins and ATP dependent reaction, but we didn’t really understand that it’s binding, its co-herent binding the substate until that summer in 1971 in the laboratory of Rose where you invited me, together with Aaron who was then my graduate student in /- – -/ who was there. 1979, 1979. So that is when, when the discovery that ubiquitin …

Irwin Rose: Shall I tell the story about the ubiquitin?

Avram Hershko: Yes. I think I have finished. So then, that’s how I remember it, and how …

Irwin Rose: OK, well, here they had a heat stable factor that was required, and they made the observation that the ubiquitin went on to proteins. And so one of my post docs went to a post doc of another student, of another faculty member at the Fox Chase Cancer Centre, and said, there was a conversation, and do you know of any examples of a protein covalently linked to a protein? And this post doctoral fellow said yes, there is in the nucleus, a protein called ubiquitin that’s covalently linked to histone. And so they rushed to look at the amino acid composition of that so-called ubiquitin, and they compared it to the amino acid composition which you had published, I guess …

Aaron Ciechanover: No, not yet.

Irwin Rose: Not yet published.

Aaron Ciechanover: But in the end it was published back to back with JBC.

Irwin Rose: No, no, no. But how did they know the conversation …

Aaron Ciechanover: No, because they knew, the end story is that the Wilkinson paper came back to back with ours on the /- – -/.

Avram Hershko: OK. Let’s not go into the detail.

Irwin Rose: Well, for some reason or other, they found confidence…

Avram Hershko: They knew that I published that.

Irwin Rose: Really, and I was not a leak.

Avram Hershko: No, no, you were not.

Aaron Ciechanover: No, he was in the lab, he was free and did this. We didn’t hide anything.

Irwin Rose: OK, you’re getting the inside story here. Now, wait a second.

I have a statement from your colleague. “At first nobody cared about your work, and those that knew something about it, they didn’t believe it.” Was it so …?

Irwin Rose: Who said that?

Avram Hershko: That was, that was Fred Goldberg, yeah.

Aaron Ciechanover: Let’s not mention names.

Avram Hershko: Oh! No, we didn’t mention names.

That citation is right.

Aaron Ciechanover: I’ll tell you, I’ll tell you a funny story. I left the lab in ’81, basically after my PhD was completed I submitted it and I went to Harvard, I went to MIT to do a post doc fellow, and Harvard carried out weekly seminars. And in this weekly seminar, one of the founders in the field of proteolysis, one of the originally, not the founder, but it doesn’t matter. A famous scientist in the field presented the weekly seminar at Harvard. I knew of him because he was our competitor for many years, and I went to hear the seminar, so I crossed the river by the bus, I took the shuttle bus that goes /- – -/ and I was sitting in the very back bench. And this was probably about two weeks before you came to visit, it was the very beginning of my, do you remember when I met you, I came to the airport to pick you up.

Avram Hershko: Yeah, yeah.

Aaron Ciechanover: And then, near me, was sitting a very famous scientist that I only later realised that his name is Arthur Dee, a very famous scientist, and after this presentation of the professor, this was only ’81 when we had like eight or nine papers already in the literature with a huge amount of information there. And he was a protein researcher and he raised his hand, I remember very well, and the other guy, when we were both  /- – -/ he said, you know, I have a question to ask you. There is a fellow in Haifa by the name of Hershko, and another one with a very complicated Polish name that I cannot even pronounce, that published a series of papers on a small protein that is attached to other proteins and marks them for degradation, can you comment on it? And he basically dismissed it as an artefact.

… it adds to our benefit, because they left us alone for seven successive years …

And I don’t, I don’t criticise him, all I’m telling you it was symbolic for me enough for after eight papers in the literature, this was the spirit in the field from people who worked in the field, and there were very few. As a matter of fact, it adds to our benefit, because they left us alone for seven successive years, even after I left the lab to work out basically the entire system. The next scientist to join the field was a scientist at MIT, Alex Varshavsky, who joined in ’84, ’83, but published in ’84, and given I was there and collaborated, so for seven successive years they let us lay the entire stone down in the literature so I don’t criticise him, actually I appreciate him tremendously for letting us do it. You know, in retrospect.

But I wonder, how do you survive as a scientist when nobody believes you somehow? Nobody’s interested. You become kind of non-visible.

Irwin Rose: You’re making observations, and the observations get published, so the observations are true. Whether anybody will say that belongs to a big story like it turns out to be is not predictable, but so you don’t make claims like that. You say that this is very interesting and so on and so on and so on, and you keep following it up, and it doesn’t necessarily become the centre of attention yet, until you build a big enough story. I think that’s the way it works.

We all survive because funding for research was generous in those days, you know. It’s been less generous now, and we have a peer review system which is more critical and so I think you have to, you have to add successively to the picture you’re trying to portray. It’s not sufficient to just provide data. So I think that’s part of it. But I agree that it’s important to be left alone for a sufficient amount of time in order to be able to do it, and not feel that you’re in the middle of a big activity already, so you know, you need to do that sort of thing.

So do you think you would get support today for such work, which was kind of apart?

Avram Hershko: Well, I hope the fund /- – -/ look up your website and will hear these things. Because it’s … yeah, Joe Goldstein, you know, a Nobel Laureate and a good one, wrote a nice article about this year’s Lasker Award, in which he compared science to a sculpture by this British sculptor who had his stone, it was a huge stone of two and a half ton, on which another stone, and another stone, and another stone, and at the end is a little stone, so he said that in science there are big stones and small stones. The important science is the opposite. When you have a little stone, and on top of it you put a bigger stone and then a bigger stone. If you throw out a big stone at the beginning so there’s a lot of publicity sometimes nothing comes out of it, and the scientist, to find his little stone, on which the other stones can be built. So I recommend to read his article.

Now you find the small stones, Dr Rose, in your kitchen, as I understand it. You have a small laboratory there?

Irwin Rose: You want to talk about my kitchen?

Yeah. Your laboratory, I would say.

Irwin Rose: Well, when I retired from Fox Chase I took my spectrophotometer and a lot of my chemicals, based on a sort of suggestion of Dr … his recommendation. So I took all my chemicals and my spectrophotometer and my constant temperature bath and so forth with me to Irvine, and when the person whose laboratory I was sitting decided to retire, I had to do something with the spectrophotometer and so I found a place in my kitchen for it. And this was very convenient because it saved me a lot of time. I didn’t have to go to work every day and if I had a little experiment to do I could do it in my kitchen. So that was very good, although I’ve got a lot of chemicals that I have no use for and I’d like to take them back.

Aaron Ciechanover: Send them over, send them over.

Irwin Rose: I’ll send them over. I’ll get a box.

Avram Hershko: But I worry that you don’t have an ice machine. You need an ice machine.

Irwin Rose: No, I don’t have an ice machine. But I have a freezer and I can make ice cubes and I can break them up.

It’s kind of worrying, in science. So you can work when everything’s /- – -/ ?

Irwin Rose: Yeah, that’s right, exactly.

So are you the kind of scientists that work all day and all night long, kind of nerd scientists?

So that’s my recommendation, do not retire. Do not retire fellas. …

Irwin Rose: I think we all work all day and all night long. I do. I don’t have any hobbies, you know, I’m very embarrassed when people ask me what are my hobbies, I don’t have any hobbies. I mean, it’s just enough to keep up with the things I’m trying to solve. You know, I used to work on little puzzles and so on and so forth. Each puzzle requires attention and, so you get an idea. You get your ideas at different times. Sometimes your wife makes a statement and you say: aha, maybe you’re right. And so you go off to your kitchen, and do a little experiment, so you try to, that’s the way you make progress, if you continue these things. So that’s my recommendation, do not retire. Do not retire fellas.

Avram Hershko: I won’t.

Aaron Ciechanover: I’m never going to.

You worked together in the beginning, you were the graduate student of Dr Hershko, how was it to separate from each other?

Aaron Ciechanover: Well, it’s the nature of science, I think, because you know, you graduate, you go your post doctorate fellowship, and Avram was gracious enough to bring me back, but now is independent and that’s the entire idea, if you bring a young scientist back, you give him a bench, start up funds, and then you tell him now in five years, come back in five years, and show the committees that you worked for something. So actually, you know, it would be unnatural if we would have continued to work together. So, each of us is independent. Now we’re in the same institute and that’s the whole idea of children that grow up, students that become their own, scientists on their own, I think that’s the way.

Do you compete with each other?

Avram Hershko: No, there is enough to do in the ubiquitin field, we don’t feel that we had to compete. There are different aspects of the ubiquitin field. I am working on cell cycle and he works on …

Aaron Ciechanover: /- – -/. Completely different.

How is it to live in a small country with big problems and to get funds for science?

Avram Hershko: It is not easy, it is not easy. You have to know the daily tension which is of course distractive. The funds are small, some funds for science are small. Graduate students have to go to serve in the army and things like that, so it’s more difficult than elsewhere, but it’s possible, it’s possible.

And now everybody’s happy. About the Nobel Prize. So thank you very much for sharing your thoughts with us, and being with us.

 

See a Video of the Interview
26 min.

 

Share this:

To cite this page
MLA style: “Transcript from an interview with the 2004 Nobel Laureates in Chemistry Aaron Ciechanover, Avram Hershko and Irwin Rose, on 9 December 2004”. Nobelprize.org. Nobel Media AB 2014. Web. 5 Sep 2015. <http://www.nobelprize.org/nobel_prizes/chemistry/laureates/2004/ciechanover-hershko-rose-interview-transcript.html>

 

Other related articles published in this Open Access Online Scientific Journal include the following:

 

Innovations in Israel – Nobel Prize in Chemistry 2004, 2011

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2012/08/18/innovations-in-israel-nobel-prize-in-chemistry-2004-2011/

 

Ubiquitin Pathway Involved in Neurodegenerative Diseases

Larry H Bernstein, MD,  FCAP

https://pharmaceuticalintelligence.com/2013/02/15/ubiquitin-pathway-involved-in-neurodegenerative-diseases/

 

Ubiquitin-Proteosome pathway, Autophagy, the Mitochondrion, Proteolysis and Cell Apoptosis: Part III

Curator: Larry H Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2013/02/14/ubiquinin-proteosome-pathway-autophagy-the-mitochondrion-proteolysis-and-cell-apoptosis-reconsidered/

 

Recurrent somatic mutations in chromatin-remodeling and ubiquitin ligase complex genes in serous endometrial tumors

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

https://pharmaceuticalintelligence.com/2012/11/19/recurrent-somatic-mutations-in-chromatin-remodeling-and-ubiquitin-ligase-complex-genes-in-serous-endometrial-tumors/

 

BRCA1 a tumour suppressor in breast and ovarian cancer – functions in transcription, ubiquitination and DNA repair

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

https://pharmaceuticalintelligence.com/2012/12/04/brca1-a-tumour-suppressor-in-breast-and-ovarian-cancer-functions-in-transcription-ubiquitination-and-dna-repair/

 

Exome sequencing of serous endometrial tumors shows recurrent somatic mutations in chromatin-remodeling and ubiquitin ligase complex genes

Reporter and Curator: Dr. Sudipta Saha, Ph.D

https://pharmaceuticalintelligence.com/2012/12/18/exome-sequencing-of-serous-endometrial-tumors-shows-recurrent-somatic-mutations-in-chromatin-remodeling-and-ubiquitin-ligase-complex-genes/

 

Advertisements

Read Full Post »


Metabolomic analysis of two leukemia cell lines. II.

Larry H. Bernstein, MD, FCAP, Reviewer and Curator

Leaders in Pharmaceutical Intelligence

 

In Part I of metabolomics of two leukemia cell lines, we have established a major premise for the study, an insight into the use of an experimental model, and some insight into questions raised.

I here return to examine these before pursuing more detail in the study.

Q1. What strong metabolic pathways come into focus in this study?

Answer – The aerobic and anaerobic glycolytic pathways, with a difference measured in the extent of participation of mitochondrial oxidative phosphorylation.

Q2. Would we expect to also gain insight into the effect, on balance, played by a suppressed ubiquitin pathway?

Answer – lets look into this in Part II.

Q3. Would the synthesis of phospholipid and the maintenance of membrane structures requires availability of NADPH, which would be a reversal of the TCA cycle at the cost of delta G in catabolic energy, be consistent with increased dependence of anaerobic glycolysis  with unchecked replication?

Answer: Part II might show this, as the direction and the difference between the cell lines is consistent with a Warburg (Pasteur) effect.

Recall the observation that the model is based on experimental results from  lymphocytic leukemia cell lines in cell culture.  The internal metabolic state is inferred from measurement of external metabolites.

The classification of the lymphocytic leukemias in humans is based on T-cell and B-cell lineages, but actually uses cell differentiation (CD) markers on the cytoskeleton for recognition.  It is only a conjecture that if the cells line were highly anaplastic, they might not be sustainable in cell culture in perpetuity.
The analogue of these cells to what I would expect to see in humans is the SLL having the characteristic marking: CD5, see http://www.pathologyoutlines.com/topic/lymphomaSLL.html

Micro description
=======================================================

● Effacement of nodal architecture by pale staining pseudofollicles or proliferation centers with ill-defined borders, containing small round mature lymphocytes, prolymphocytes (larger than small lymphocytes, abundant basophilic cytoplasm, prominent nucleoli), paraimmunoblasts (larger cells with distinct nucleoli) and many smudge cells
● Pseudofollicular centers are highlighted by decreasing light through the condenser at low power; cells have pale cytoplasm but resemble soccer balls or smudge cells on peripheral smear (cytoplasm is bubbly in mantle cell lymphoma); may have plasmacytoid features
● May have marginal zone, perifollicular or interfollicular patterns, but these cases also have proliferation centers (Mod Pathol 2000;13:1161)
● Interfollicular pattern: large, reactive germinal centers; resembles follicular lymphoma but germinal centers are bcl2 negative and tumor cells resemble SLL by morphology and immunostains
(Am J Clin Path 2000;114:41)
● Paraimmunoblastic variant: diffuse proliferation of paraimmunoblasts (normally just in pseudoproliferation centers); rare, <30 reported cases; usually multiple lymphadenopathies and rapid disease progression; case report in 69 year old man (Hum Pathol 2002;33:1145); consider as mantile cell lymphoma if t(11;14)(q13;q32) is present; may also represent CD5+ diffuse large B cell lymphoma
Bone marrow: small focal aggregates of variable size with irregular, poorly circumscribed outlines; lymphocytes are well differentiated, small, round with minimal atypia; may have foci of transformation; rarely has granulomas (J Clin Pathol 2005;58:815)
● Marrow infiltrative patterns are also described as diffuse (unmutated IgH genes, ZAP-70+, more aggressive), nodular (associated with IgH hypermutation, ZAP-70 negative) or mixed (variable mutation of IgH, variable ZAP-70, Hum Pathol 2006;37:1153)

 

Positive stains
=======================================================

● CD5, CD19, CD20 (dim), CD23, surface Ig light chain, surface IgM (dim)
● Also CD43, CD79a, CD79b (dim in 20%, Arch Pathol Lab Med 2003;127:561), bcl2
● Variable CD11c, FMC7 (42%)
Negative stains
=======================================================

● CD10, cyclin D1
Molecular
=======================================================

● Trisomy 12 (30%, associated with atypical CLL and CD79b), deletion 13q14 (25-50%),
deletion of 11q23 (worse prognosis, 10-20%)

 

Results

We set up a pipeline that could be used to

  • infer intracellular metabolic states from semi-quantitative data
  • regarding metabolites exchanged between cells and their environment.

Our pipeline combined the following four steps:

  1. data acquisition,
  2. data analysis,
  3. metabolic modeling and
  4.  experimental validation of
  • the model predictions (Fig. 1A).

We demonstrated the pipeline and the predictive potential

  • to predict metabolic alternations in diseases such as cancer
  • based on two lymphoblastic leukemia cell lines.

The resulting Molt-4 and CCRF-CEM condition-specific cell line models were able

  • to explain metabolite uptake and secretion
  •  by predicting the distinct utilization of central metabolic pathways by the two cell lines.

Whereas the CCRF-CEM model

  • resembled more a glycolytic, commonly referred to as ‘Warburg’ phenotype,
  • our predictions suggested  a more respiratory phenotype for the Molt-4  model.

We found these predictions to be in agreement with measured gene expression differences

  • at key regulatory steps in the central metabolic pathways, and
  • they were also consistent with  data regarding the energy and redox states of the cells.

After a brief discussion of the data generation and analysis steps, the results

  • derived from model generation and analysis will be described in detail.

 

2.1 Pipeline for generation of condition-specific metabolic cell line models

2.1.1 Generation of experimental data

We monitored the growth and viability of lymphoblastic leukemia cell lines in
serum- free medium (File S2, Fig. S1). Multiple omics  data sets  were derived  from these cells.

Extracellular metabolomics (exo-metabolomic) data,

  • comprising measurements of the metabolites in the spent medium of the cell cultures
    (Paglia et al. 2012a),
  • were collected along with transcriptomic data, and
  • these data sets were used to construct the models.

 

2.1.4 Condition-specific models for CCRF-CEM and Molt-4 cells

To determine whether we had obtained two distinct models,

  • we evaluated the reactions, metabolites, and genes of the two models.

Both the Molt-4 and CCRF-CEM models contained approximately

  • half of the reactions and metabolites present in the global model (Fig. 1C).

They were very similar to each other in terms of their

  • reactions,
  • metabolites, and
  • genes (File S1, Table S5A–C).

The Molt– 4 model contained

  • seven reactions that were not present in the CCRF-CEM model
    (Co-A biosynthesis pathway and exchange reactions).

In contrast, the CCRF-CEM  contained

31 unique reactions

  • arginine and proline metabolism,
  • vitamin B6  metabolism,
  • fatty acid activation,
  • transport, and exchange reaction.
  • There  were 2 and 15 unique metabolites in the Molt-4 and CCRF-CEM models,  respectively
    (File S1, Table S5B).
    Approximately three quarters of the global  model  genesremained in the condition-specific cell line models  (Fig. 1C).

The Molt-4 model contained

  • 15 unique genes, and

the CCRF-CEM model had

  • 4 unique genes (File S1, Table S5C).

Both models lacked NADH dehydrogenase
(complex I of the electron transport chain—ETC),

  •  determined by  the  absence of expression of a mandatory subunit
    (NDUFB3, Entrez gene ID 4709).

The ETC was fueled by FADH2 originating from

  1. succinate dehydrogenase and
  2. from fatty acid oxidation, which
  • through flavoprotein electron transfer
  • could contribute to the same ubiquinone pool as
  • complex I and complex II (succinate dehydrogenase).

Despite their different in vitro growth rates
(which differed by 11 %, see File S2, Fig. S1) and

  • differences in exo-metabolomic data (Fig. 1B) and
  • transcriptomic data,
  • the internal networks were largely conserved
  • in the two condition-specific cell line models.

 

2.1.5 Condition-specific cell line models predict distinct metabolic strategies

Despite the overall similarity of the metabolic models,

  • differences in their cellular uptake and secretion patterns suggested
  • distinct metabolic states in the two cell lines
    (Fig. 1B and see “Materials and methods” section for more detail).

To interrogate the metabolic differences, we sampled the solution space

  • of each model  using an Artificial Centering Hit-and-Run (ACHR) sampler (Thiele et al. 2005).

For this  analysis, additional constraints were applied, emphasizing

  • the  quantitative differences in commonly uptaken and secreted metabolites.

The  maximum possible uptake and maximum possible secretion flux rates were

  • reduced according to the measured relative differences between the cell lines
    (Fig. 1D, see “Materials and methods” section).

We plotted the number of sample points containing a particular flux rate for each reaction. The resulting

  • binned histograms can be understood as representing the probability that
  • a particular reaction can have a certain flux value.

A comparison of the sample points obtained for the Molt-4 and CCRF-CEM models revealed

  • a  considerable shift in the distributions, suggesting
  • a higher utilization of  glycolysis by the CCRF-CEM model (File S2, Fig. S2).

This result  was further  supported by differences

  • in medians calculated from sampling points (File S1,  Table S6).

The shift persisted throughout all reactions of the pathway and

  • was  induced by the higher glucose uptake (35 %) from
  • the extracellular medium in CCRF-CEM cells.

The sampling median for glucose uptake was 34 % higher

  • in the  CCRF-CEM model than in Molt-4 model (File S2, Fig. S2).

The usage of the  TCA cycle was also distinct in the two condition-specific cell-line models (Fig. 2).

  • the models used succinate dehydrogenase differently (Figs. 23).

The Molt-4 model utilized an associated reaction to generate FADH2, whereas

  • in  the CCRF-CEM model, the histogram was shifted in the opposite direction,
  • toward  the generation of succinate.

Additionally, there was a higher efflux of  citrate toward

  • amino acid and lipid metabolism in the CCRF-CEM model (Fig. 2).

There was higher flux through anaplerotic and cataplerotic reactions

  • in the CCRF-CEM model than in the Molt-4 model (Fig. 2);
  • these reactions include the efflux  of citrate through

 

  1. ATP-citrate lyase,
  2. uptake of glutamine,
  3. generation of  glutamate from glutamine,
  4. transamination of pyruvate and
  5.  glutamate to alanine  and to 2-oxoglutarate,
  6. secretion of nitrogen, and
  7. secretion of alanine.

The Molt-4 model showed higher utilization of oxidative phosphorylation (Fig. 3),

  • supported by elevated median flux through ATP synthase (36 %) and other  enzymes,
  • which contributed to higher oxidative metabolism.

The sampling  analysis therefore revealed different usage of

  • central metabolic pathways by the condition-specific models.

 

Fig. 2

Differences in the use of the TCA cycle by the CCRF-CEM

Differences in the use of the TCA cycle by the CCRF-CEM

Differences in the use of the TCA cycle by the CCRF-CEM model (red) and the Molt-4 model (blue).
The table provides the median values of the sampling results. Negative values in histograms and Table

  • describe reversible  reactions with flux in the reverse direction.

There are multiple reversible  reactions for the transformation of

  1. isocitrate and α-ketoglutarate,
  2. malate and  fumarate, and
  3. succinyl-CoA and succinate.

These reactions are  unbounded,  and therefore histograms are not shown.
The details of participating cofactors  have been removed.

Atp ATP, cit citrate, adp ADP, pi phosphate, oaa oxaloacetate, accoa acetyl-CoAcoa coenzyme-A,
icit isocitrate, αkg α-ketoglutarate, succcoa succinyl-CoAsucc succinate, fumfumarate, mal malate,
oxa oxaloacetate,  pyr pyruvate, lac lactate, ala alanine, gln glutamine, ETC electron transport  chain.

 

Electronic supplementary material The online version of this article
http://dx.doi.org:/10.1007/s11306-014-0721-3 
contains supplementary material,  which  is available to authorized users.

  1.  K. Aurich _ G. Paglia _ O ´ . Rolfsson _ S. Hrafnsdo´ ttir _
  2. Magnu´sdo´ ttir _ B. Ø. Palsson _ R. M. T. Fleming _ I. Thiele. Center for Systems Biology,
    University of Iceland, Reykjavik, Iceland
  3.  K. Aurich _ R. M. T. Fleming _ I. Thiele (&). Luxembourg Centre for Systems Biomedicine,
    University of Luxembourg, Campus Belval, Esch-Sur-Alzette, Luxembourg
    e-mail: ines.thiele@uni.lu
  4. M. Stefaniak. School of Health Science, Faculty of Food Science and Nutrition,
    University of Iceland, Reykjavik, Iceland
  5. Ø. Palsson. Department of Bioengineering, University of California San Diego, La Jolla, CA, USA

http://link.springer.com/static-content/images/404/art%253A10.1007%252
Fs11306-014-0721-3/MediaObjects/11306_2014_721_Fig3_HTML.gif

 

Fig. 3

Fatty acid oxidation and ETC _Fig3

Fatty acid oxidation and ETC _Fig3

 

Sampling reveals different utilization of oxidative phosphorylation by the

  • generated models.

Different distributions are observed for the CCRF-CEM model (red) and the Molt-4 model (blue).

  • Molt-4 has higher  median  flux through ETC reactions II–IV.

The table provides the median values  of the sampling results. Negative values in the histograms and in the table describe

  • reversible reactions with flux in the reverse direction.

Both models lack Complex I of the ETC because of constraints

  • arising from the mapping of transcriptomic data.

Electron transfer flavoprotein and

  • electron transfer flavoprotein–ubiquinone oxidoreductase
  •  both also carry higher flux in the Molt-4 model

 

2.1.6 Experimental validation of energy and redox status of CCRF-CEM and Molt-4 cells

Cancer cells have to balance their needs

  •  for energy and biosynthetic precursors, and they have
  • to maintain redox homeostasis to proliferate (Cairns et al. 2011).

We conducted enzymatic assays of cell lysates to measure levels and/or ratios of

  • ATP,
  • NADPH + NADP,
  • NADH + NAD, and
  • glutathione.

These measurements were used to provide support for

  • the in silico predicted metabolic differences (Fig. 4).

Additionally, an Oxygen Radical Absorbance Capacity (ORAC) assay was used

  • to evaluate the cellular antioxidant status (Fig. 4B).

Total concentrations of NADH + NAD, GSH + GSSG, NADPH + NADP and ATP, were higher in Molt-4 cells  (Fig. 4A).

The higher ATP concentration in Molt-4 cells could either result from

  • high production rates, or intracellular  accumulation connected to high or
  • low reactions fluxes (Fig. 4A).

Our simplified view that oxidative Molt-4 produces less ATP and was contradicted by

  • the higher ATP concentrations measured (Fig. 4L).

Yet we want to emphasize that concentrations

  • cannot be compared to flux values,
  • since we are modeling at steady-state.

NADH/NAD+ ratios for both cell lines were shifted toward NADH (Fig. 4D, E), but

  • the shift toward NADH was more pronounced in CCRF-CEM (Fig. 4E),
  • which matched  our expectation based on the higher utilization of
  • glycolysis and 2-oxoglutarate  dehydrogenase in the CCRF-CEM model (Fig. 4L).

 

Fig. 4 (not shown)

A–K  Experimentally determined ATP, NADH + NAD, NADPH + NADP, and GSH + GSSG concentrations, and ROS detoxification in the CCRF-CEM and Molt-4 cells.

L Expectations for cellular energy and redox states. Expectations are based on predicted metabolic differences of the Molt-4 and CCRF-CEM models

2.1.7 Comparison of network utilization and alteration in gene expression

With the assumption that

  • differential expression of particular genes would cause reaction flux changes,

we determined how the differences in gene expression (between CCRF-CEM and Molt-4)

  • compared to the flux differences observed in the  models.

Specifically, we checked whether the reactions associated with genes upregulated
(significantly more expressed in CCRF-CEM cells compared to Molt-4  cells)

  • were indeed more utilized by the CCRF-CEM model,

and we  checked  whether downregulated genes

  • were associated with reactions more utilized by the Molt-4 model.

The set of downregulated genes was associated with 15 reactions, and

  • the set of 49 upregulated genes was associated with 113 reactions in the models.

Reactions were defined as differently utilized

  • if the difference in flux exceeded 10 % (considering only non-loop reactions).

Of the reactions associated with upregulated genes,

  • 72.57 % were more utilized by the CCRF-CEM model, and
  • 2.65 % were more utilized by the Molt-4 model (File S1, Table S7).

In contrast, all 15 reactions associated with the 12 downregulated genes

  • were more utilized in the CCRF-CEM model (File S1, Table S8).

After this initial analysis, we approached the question from a different angle, asking

  • whether the majority of the reactions associated with each individual gene
  • upregulated in CCRF-CEM were more utilized by the CCRF-CEM model.
  •  this was the case for 77.55 % of the upregulated genes.

The majority of reactions associated with two (16.67 %) downregulated genes

  • were more utilized by the Molt-4 model.

Taken together, our comparisons of the

  • direction of gene expression with the fluxes of the two cancer cell-line models
  • confirmed that reactions associated with upregulated genes in the CCRF-CEM
    cells were generally more utilized by the CCRF-CEM model.

2.1.8 Accumulation of DEGs and AS genes at key metabolic steps

After we confirmed that most reactions associated with upregulated genes

  • were more utilized by the CCRF-CEM model,

we checked the locations of DEGs within the network. In this analysis, we paid special attention to

  • the central metabolic pathways that we had found
  • to be distinctively utilized by the two models.

Several DEGs and AS events were associated with

  • glycolysis,
  • the ETC,
  • pyruvate metabolism, and
  • the PPP (Table 1).

 

Table 1

DEGs and AS events of central metabolic and cancer-related pathways

Full lists of DEGs and AS are provided in the supplementary material.

Upregulated significantly more expressed in CCRF-CEM compared to Molt-4 cells

PPP pentose phosphate pathway, OxPhos oxidative phosphorylation, Glycolysis/gluconglycolysis/gluconeogenesis, Pyruvate met. pyruvate metabolism

Moreover, in glycolysis, the DEGs and/or AS genes

  • were associated with all three rate-limiting steps, i.e., the steps mediated by
  1. hexokinase,
  2. pyruvate kinase, and
  3. phosphofructokinase.

Of these key enzymes,

  • hexokinase 1 (Entrez Gene ID: 3098) was alternatively spliced,
  • pyruvate kinase (PKM, Entrez gene ID: 5315) was significantly more
    expressed in the CCRF-CEM cells (Table 1),

in agreement with the higher in silico predicted flux.

However, in contrast to the observed

  • higher utilization of glycolysis in the CCRF-CEM model,
  • the gene associated with the rate-limiting glycolysis step, phosphofructokinase (Entrez Gene ID: 5213),
  • was significantly upregulated in Molt-4 cells relative to CCRF-CEM cells.

This higher expression was detected for only a single isozyme, however. Two of
the three genes associated with phosphofructokinase were also subject to
alternative splicing (Table 1). In addition to the key enzymes, fructose
bisphosphate aldolase (Entrez Gene ID: 230) was also significantly

  • upregulated in Molt-4 cells relative to CCRF-CEM cells,
  • in contrast to the predicted higher utilization of glycolysis in the CCRF-CEM model.

Additionally, glucose-6P-dehydrogenase (G6PD), which catalyzes

  • the first reaction and committed step of the PPP,
  • was an AS gene (Table 1).

A second AS gene associated with

  •  the PPP reaction of the deoxyribokinase
  • was RBKS (Entrez Gene ID: 64080).

This gene is also associated with ribokinase, but ribokinase was removed

  • because of the lack of ribose uptake or secretion.

Single AS genes were associated with different complexes of the ETC (Table 1).

Literature query revealed that at least 13 genes associated with alternative

  • splicing events were mentioned previously in connection with both alternative
    splicing and cancer (File S1, Table S14), and
  • 37 genes were associated with cancer, e.g., upregulated, downregulated at the
    level of mRNA or protein, or otherwise
  • connected to cancer metabolism and signaling.

One general observation was that there was a surprising

  • accumulation of metabolite transporters among the AS.

Overall, the high incidence of

  • differential gene expression events at metabolic control points
  • increases the plausibility of the in silico predictions.

 

2.1.9 Single gene deletion

Analyses of essential genes in metabolic models have been used

  • to predict candidate drug targets for cancer cells (Folger et al. 2011).

Here, we conducted an in silico gene deletion study for all model genes to identify

  • a unique set of knock-out (KO) genes
  • for each condition-specific cell line model.

The analysis yielded 63 shared lethal KO genes and

  • distinct sets of KO genes for the CCRF-CEM model (11 genes) and the Molt-4 model (3 genes).

For three of the unique CCRF-CEM KO genes,

  • the genes were only present in the CCRF-CEM model (File S1, Table S9).

 

The essential genes for both models were then

  • related to the cell-line-specific differences in metabolite uptake and secretion (Fig. 1B).

The CCRF-CEM model

  1. needed to generate putrescine from ornithine
    (ORNDC, Entrez Gene ID: 4953)
  2. to subsequently produce 5-methylthioadenosine for secretion (Fig. 1B).
  3. S-adenosylmethioninamine produced by adenosylmethionine decarboxylase
    (arginine and proline metabolism, associated with Entrez Gene ID: 262)
  • is a substrate required for generation of 5-methylthioadenosine.

Another example of a KO gene connected to an enforced exchange reaction was

  • glutamic-oxaloacetic transaminase 1 (GOT1, Entrez Gene ID: 2805).

Without GOT1, the CCRF-CEM model was forced to secrete

  • 4-hydroxyphenylpyruvate (Fig. 1B),
  • the second product of tyrosine transaminase,
  • which is produced only by that enzyme.

 

One KO gene in the Molt-4 model (Entrez Gene ID: 26227) was associated with

  • phosphoglycerate dehydrogenase (PGDH),
  • which catalyzes the conversion of 3-phospho-d-glycerate to 3-phosphohydroxypyruvate
  • while generating NADH from NAD+.

This KO gene is particularly interesting, given

  • the involvement of this reaction in a novel pathway for ATP generation in rapidly proliferating cells
    (Locasale et al. 2011; Vander Heiden 2011; Vazquez et al. 2011).

Reactions associated with unique KO genes were in many cases utilized more by the model, in which

  • the gene KO was lethal,
  • underlining the potential importance of these reactions for the models.

Thus, single gene deletion provided unique sets of lethal genes that could be

  • specifically targeted to kill these cells.

 

3 Discussion

In the current study, we explored the possibility of

  • semi-quantitatively integrating metabolomic data with
  • the human genome-scale reconstruction to facilitate analysis.

By constructing condition-specific cell line models

  • to provide a structured framework,
  • we derived insights that could not have been obtained from data analysis alone.

We derived condition-specific cell line models

  • for CCRF-CEM and
  • Molt-4 cells

that were able to explain the observed exo-metabolomic differences (Fig. 1B).

Despite the overall similarities between the models, the analysis revealed

  • distinct usage of central metabolic pathways (Figs. 234),
  • which we validated based on experimental data and
  • differential gene expression.

The additional data sufficiently supported

  • metabolic differences in the cell lines,
  • providing confidence in the generated models and the model-based predictions.

We used the validated models

  • to predict unique sets of lethal genes
  • to identify weak links in each model.

These weak links may represent potential drug targets.

Integrating omics data with the human genome-scale reconstruction

  • provides a structured framework (i.e., pathways)
  • that is based on careful consideration of the available biochemical literature
    (Thiele and Palsson2010).

This network context can simplify omics data analysis, and

  • it allows even non-biochemical experts
  • to gain fast and comprehensive insights
  • into the metabolic aspects of omics data sets.

Compared to transcriptomic data,

  • methods for the integration and analysis of metabolomic data
  • in the context of metabolic models are less well established,

although it is an active field of research (Li et al. 2013; Paglia et al. 2012b).
In contrast to other studies, our approach emphasizes

  • the representation of experimental conditions rather than
  • the reconstruction of a generic, cell-line-specific network,
  • which would require the combination of data sets from
  • many experimental conditions and extensive manual curation.

Rather, our way of model construction allowed us to efficiently

  • assess the metabolic characteristics of cells.

Despite the fact, that only a limited number of exchanged metabolites can be

  • measured by available metabolomics platforms and
  • at reasonable time-scale,

and that pathways of measured metabolites might still be unknown to date
(File S1, Tables S2–S3), our methods have the potential

  • to reveal metabolic characteristics of cells
  • which could be useful for biomedicine and personalized health.

The reasons why some cancers respond to certain treatments and not others
remain unclear, and choosing a treatment for a specific patient is often difficult
(Vander Heiden 2011). One potential application of our approach could be the
characterization of cancer phenotypes to explore how cancer cells or other cell
types

  • with particular metabolic characteristics respond to drugs.

The generation of our condition-specific cell line models involved

  • only limited manual curation,
  • making this approach a fast way to place metabolomic data
  • into a network context.

Model building mainly involves

  • the rigid reduction of metabolite exchanges
  • to match the observed metabolite exchange pattern
  • with as few additional metabolite exchanges as possible.

It should be noted that this reduction determines,

  • which pathways can be utilized by the model.

Our approach mostly conserved the internal network redundancy. However, a

  • more significant reduction may be achieved using different data.

Generally, a trade-off exists between the reduction of the internal network and

  • the increasing number of network gaps that need to be curated
  • by using additional omics data, such as transcriptomics and proteomics.

One way to prevent the emergence of network gaps would be

  • to use mapping algorithms that conserve network functionality,
    such as GIMME (Becker and Palsson 2008).

However, several additional methods exist for the integration of
transcriptomic data (Blazier and Papin 2012), and

  • which model-building method is best depends on the available data.

Interestingly, the lack of a significant contribution of our

  • gene expression data to the reduction of network size
  • suggests that the use of transcriptomic data is not necessary
  • to identify distinct metabolic strategies;
  • rather, the integration of exo-metabolomic data alone
    may provide sufficient insight.

However, sampling of the cell line models constrained

  • according to the exo-metabolomic profiles only, or
  • increasing the cutoff for the generation of absent and present calls (p < 0.01),
  • did not yield the same insights as presented herein (File S1, Table S18).

Only recently Gene Inactivation Moderated by Metabolism, Metabolomics and
Expression (GIM(3)E) became available, which

  • enforces minimum turnover of detected metabolites
  • based on intracellular metabolomics data as well as
  • gene expression microarray data (Schmidt et al. 2013).

In contrast to this approach, we emphasized our analysis on the

  • relative differences in the exo-metabolomic data of two cell lines.

GIM(3)E constitutes another integration method when the analysis should be

  • emphasized on intracellular metabolomics data (Schmidt et al. 2013).

The metabolic differences predicted by the models are generally plausible.
Cancers are known to be heterogeneous (Cairns et al. 2011), and

  • the contribution of oxidative phosphorylation to cellular ATP production
    may vary (Zu and Guppy 2004).

Moreover, leukemia cell lines have been shown

  • to depend on glucose, glutamine, and fatty acids to varying extents
  • to support proliferation.

Such dependence may cause the cells to adapt their metabolism

  • to the environmental conditions (Suganuma et al. 2010).

In addition to identifying supporting data in the literature, we performed

  • several analyses to validate the models and model predictions.

Our expectations regarding the levels and ratios of metabolites

  • relevant to energy and redox state were largely met (Fig. 4L).

The more pronounced shift of the NADH/NAD+ ratio

  • toward NADH in the CCRF-CEM cells
  • was in agreement with the predicted Warburg phenotype (Fig. 4),
  • and the higher lactate secretion in the CCRF-CEM cells (File S2, Fig. S2)
  • implies an increase in NADH relative to NAD+
    (Chiarugi et al. 2012; Nikiforov et al. 2011), again
  • matching the known Warburg phenotype.

ROS production is enhanced in certain types of cancer (Droge 2002; Ha et al. 2000), and

  • the generation of ROS is thought to contribute to
  1. mutagenesis,
  2. tumor promotion, and
  3. tumor progression (Dreher and Junod1996; Ha et al. 2000).

However, decreased mitochondrial glucose oxidation and

  • a transition to aerobic glycolysis
  • protect cells against ROS damage during biosynthesis and cell division
    (Brand and Hermfisse1997).

The higher ROS detoxification capability in Molt-4 cells, in combination with

  • higher spermidine dismutase utilization by the Molt-4 model (Fig. 4),
  • provided a consistent picture of the predicted respiratory phenotype (Fig. 4L).

Control of NADPH maintains the redox potential through GSH and

  • protects against oxidative stress, yet
  • changes in the NADPH ratio in response to oxidative damage
  • are not well understood (Ogasawara et al.2009).

Under stress conditions, as assumed for Molt-4 cells,

  • the NADPH/NADP+ ratio is expected to decrease because of
  • the continuous reduction of GSSG (Fig. 4L), and
  • this was confirmed in the Molt-4 cells (Fig. 4).

The higher amounts of GSH found in Molt-4 cells in vitro may demonstrate

  • an additional need for ROS scavengers because of
  • a greater reliance on oxidative metabolism.

Cancer is related to metabolic reprogramming, which results from

  • alterations of gene expression and
  • the expression of specific isoforms or
  • splice forms to support proliferation
    (Cortes-Cros et al. 2013; Marin-Hernandez et al. 2009).

The gene expression differences detected between the two cell lines in this study
supported the existence of

  • metabolic differences in these cell lines, particularly because
  • key steps of the metabolic pathways central to cancer metabolism
  • seemed to be differentially regulated (Table 1).

The detailed analysis of the respective

  • differences on the pathway fluxes exceeds the scope of this study, which was to
  • demonstrate the potential of the integration of exo-metabolomic data into the network context.

We found discrepancies between differential gene regulation and

  • the flux differences between the two models as well as
  • the utilization AS gene-associated reaction.

This is not surprising, since analysis of the detailed system is required

  • to make any further assumptions on the impact that
  • the differential regulation or splicing might have on the reaction flux,
  • given that for many of the concerned enzymes isozymes exist, or
  • only one of multiple subunits of a protein complex was concerned.

Additionally, reaction fluxes are regulated by numerous post-translational factors, e.g.,

  • protein modification,
  • inhibition through proteins or metabolites,
  • alter reaction fluxes (Lenzen 2014),

which are out of the scope of constraint-based steady-state modeling.

Rather, the results of the presented  approach

  • demonstrate how the models can be used to generate
  • informed hypothesis that can guide experimental work.

The combination of our tailored metabolic models and

  • differential gene expression analysis seems well-suited
  • to determine the potential drivers
  • involved in metabolic differences between cells.

Such information could be valuable for drug discovery, especially when more

  • peripheral metabolic pathways are considered.

Statistical comparisons of gene expression data with sampling-derived flux data

  • could be useful in future studies (Mardinoglu et al. 2013).

A single-gene-deletion analysis revealed that PGDH was

  • a lethal KO gene for the Molt-4 model only.

Differences in PGDH protein levels

  • correspond to the amount of glycolytic carbon
  • diverted into glycine biosynthesis.

Rapidly proliferating cells may use an

  • alternative glycolytic pathway for ATP generation,
  • which may provide an advantage in the case of
  • extensive oxidative phosphorylation and proliferation
    (Locasale et al.2011; Vander Heiden 2011; Vazquez et al. 2011).

For breast cancer cell lines, variable dependency on

  • the expression of PGDH has already been demonstrated
    (Locasale et al. 2011).

This example of a unique KO gene demonstrates how

  • in silico gene deletion in metabolomics-driven models
  • can identify the metabolic pathways used by cancer cells.

This approach can provide valuable information for drug discovery.

In conclusion, our contextualization method produced

  • metabolic models that agreed in many ways with the validation data sets.

The analyses described in this study have great potential to reveal

  • the mechanisms of metabolic reprogramming,
  • not only in cancer cells but also in other cells affected by diseases, and
  • for drug discovery in general.

 

4.3 Analysis of the extracellular metabolome

Mass spectrometry analysis of the exo-metabolome was performed by
Metabolon®, Inc. (Durham, NC, USA) using a standardized analytical platform.
In total, 75 extracellular metabolites were detected in the initial data set for at
least 1 of the 2 cell lines (Paglia et al. 2012a). Of these metabolites, 15 were not
part of our global model and were discarded. Apart from being absent in our
global model, an independent search in HMDB (Wishart et al. 2013) revealed no
pathway information was available for most of these metabolites (File S1, Tables S2–S3).
It should be noted that metabolites e.g.,

  • N-acetylisoleucine,
  • N-acetyl-methionine or pseudouridine,

constitute protein and RNA degradation products, which were out of the scope
of the metabolic network.

Thiamin (Vitamin B1) was part of the minimal medium of essential compounds
supplied to both models.Riboflavin (Vitamin B2) and Trehalose were excluded
since these compounds cannot be produced by human cells. Erythrose and
fructose were also excluded. In contrast 46 metabolites that were part of the
global model. The data set included two different time points, which allowed us
to treat the increase/decrease of a metabolite signal between time points as

  • evidence for uptake or secretion when the change was greater than 5 %
    from what was observed in the control (File S1, Tables S2–S3).

We found 12 metabolites that were taken up by both cell lines and
10 metabolites that were commonly secreted by both cell lines over
the course of the experiment.

Molt-4 cells took up three metabolites not taken up by CCRF-CEM cells, and
secreted one metabolite not secreted by CCRF-CEM cells. Two of the three
uniquely uptaken metabolites were essential amino acids:

  1. valine and
  2. methionine.

It is unlikely that these metabolites were not taken up by the CCRF-CEM cells,
and the CCRF-CEM model was allowed to take up this metabolite. Therefore,
no quantitative constraints were applied for the sampling analysis either.
CCRF-CEM cells had

  • four unique uptaken
  • and seven unique secreted metabolites
    (exchange not detected in Molt-4 cells).

 

4.4 Network refinement based on exo-metabolic data

Despite its comprehensiveness, the human metabolic reconstruction is

  • not complete with respect to extracellular metabolite transporters
    (Sahoo et al. 2014; Thiele et al. 2013).

Accordingly, we identified metabolite transport systems

  • from the literature for metabolites that were already part of the global model,
  • but whose extracellular transport was not yet accounted for.

Diffusion reactions were included whenever a respective transporter could not be identified.

In total, 34 reactions [11 exchange reactions, 16 transport reactions and 7 demand reactions
(File S1, Table S11)] were added to Recon 2 (Thiele et al. 2013), and 2 additional reactions
were added to the global model (File S1, Table S10).

4.5 Expression profiling

Molt-4 and CCRF-CEM cells were grown in advanced RPMI 1640 and 2 mM
GlutaMax, and the cells were resuspended in medium containing DMSO
(0.67 %) at a concentration of 5 × 105 cells/mL. The cell suspension (2 mL)
was seeded in 12-well plates in triplicate. After 48 h of growth, the cells
were collected by centrifugation at 201×g for 5 min. Cell pellets were snap-frozen
in liquid N2 and kept frozen until RNA extraction and analysis by Aros
(Aarhus, Denmark).

4.6 Analysis of transcriptomic data

We used the Affymetrix GeneChip Human Exon 1.0 ST Array to measure whole
genome exon expression. We generated detection above background (DABG) calls
using ROOT (version 22) and the XPS package for R (version 11.1), with Robust
Multi-array Analysis summarization. Calls for data mapping were assigned based
on p < 0.05 as the cutoff probability to distinguish presence versus absence for
the 1,278 model genes (File S1, Table S12).

Differential gene expression and alternative splicing analyses were performed by
using AltAnalyse software (v2.02beta) with default options on the raw data files
(CEL files). The Homo sapiens Ensemble 65 database was used, probe set filtering
was kept as DABG p < 0.05, and non-log expression < 70 was used for
constitutive probe sets to determine gene expression levels. For the comparison,
CCRF-CEM was the experimental group and Molt-4 was the baseline group. The
set of DEGs between cell lines was identified based on a p < 0.05 FDR cutoff
(File S1, Table S13A–B). Alternative splicing analysis was performed on core probe sets
with a minimum alternative exon score of 2 and a maximum absolute gene
expression change of 3 because alternative splicing is a less critical factor among
highly DEGs (File S1, Table S14). Gene expression data, complete lists of DABG p-values,
DEGs and alternative splicing events have been deposited in the Gene
Expression Omnibus
 (GEO) database (Accession number: GSE53123).

 

4.7 Deriving cell-type-specific subnetworks

Transcriptomic data were mapped to the model in a manual fashion (COBRA
function: deleteModelGenes). Specifically, reactions dependent on gene products
that were called as “absent” were constrained to zero, such that fluxes through
these reactions were disabled. Submodels were extracted based on the set of
reactions carrying flux (network pruning) by running fastFVA
(Gudmundsson and Thiele 2010) after mapping the metabolomic and
transcriptomic data using the COBRA toolbox (Schellenberger et al. 2011).

 

…..

 

Electronic supplementary material

Below is the link to the electronic supplementary material.

File S1. Supplementary material 1 (XLSX 915 kb)

File S2. Supplementary material 2 (DOCX 448 kb)

References

Antonucci, R., Pilloni, M. D., Atzori, L., & Fanos, V. (2012). Pharmaceutical research and metabolomics in the newborn. Journal of Maternal-Fetal and Neonatal Medicine, 25, 22–26.PubMedCrossRef

Barrett, T., Troup, D. B., Wilhite, S. E., Ledoux, P., Evangelista, C., Kim, I. F., et al. (2011). NCBI GEO: archive for functional genomics data sets—10 years on. Nucleic Acids Research, 39, D1005–D1010.PubMedCentralPubMedCrossRef

Beck, M., Schmidt, A., Malmstroem, J., Claassen, M., Ori, A., Szymborska, A., et al. (2011). The quantitative proteome of a human cell line.Molecular Systems Biology, 7, 549.PubMedCentralPubMedCrossRef

Becker, S. A., & Palsson, B. O. (2008). Context-specific metabolic networks are consistent with experiments. PLoS Computational Biology, 4, e1000082.PubMedCentralPubMedCrossRef

Blazier, A. S., & Papin, J. A. (2012). Integration of expression data in genome-scale metabolic network reconstructions. Frontiers in Physiology, 3, 299.PubMedCentralPubMedCrossRef

Bordbar, A., Lewis, N. E., Schellenberger, J., Palsson, B. O., & Jamshidi, N. (2010). Insight into human alveolar macrophage and M. tuberculosisinteractions via metabolic reconstructions. Molecular Systems Biology, 6, 422.PubMedCentralPubMedCrossRef

Bordbar, A., & Palsson, B. O. (2012). Using the reconstructed genome-scale human metabolic network to study physiology and pathology. Journal of Internal Medicine, 271, 131–141.PubMedCentralPubMedCrossRef

Brand, K. A., & Hermfisse, U. (1997). Aerobic glycolysis by proliferating cells: a protective strategy against reactive oxygen species. FASEB Journal, 11, 388–395.PubMed

Cairns, R. A., Harris, I. S., & Mak, T. W. (2011). Regulation of cancer cell metabolism. Nature Reviews Cancer, 11, 85–95.PubMedCrossRef

Chance, B., Sies, H., & Boveris, A. (1979). Hydroperoxide metabolism in mammalian organs. Physiological Reviews, 59, 527–605.PubMed

Chapman, E. H., Kurec, A. S., & Davey, F. R. (1981). Cell volumes of normal and malignant mononuclear cells. Journal of Clinical Pathology, 34, 1083–1090.PubMedCentralPubMedCrossRef

Chiarugi, A., Dolle, C., Felici, R., & Ziegler, M. (2012). The NAD metabolome—a key determinant of cancer cell biology. Nature Reviews Cancer, 12, 741–752.PubMedCrossRef

Cortes-Cros, M., Hemmerlin, C., Ferretti, S., Zhang, J., Gounarides, J. S., Yin, H., et al. (2013). M2 isoform of pyruvate kinase is dispensable for tumor maintenance and growth. Proceedings of the National Academy of Sciences of the United States of America, 110, 489–494.PubMedCentralPubMedCrossRef

Dreher, D., & Junod, A. F. (1996). Role of oxygen free radicals in cancer development. European Journal of Cancer, 32a, 30–38.PubMedCrossRef

Droge, W. (2002). Free radicals in the physiological control of cell function. Physiological Reviews, 82, 47–95.PubMed

Duarte, N. C., Becker, S. A., Jamshidi, N., Thiele, I., Mo, M. L., Vo, T. D., et al. (2007). Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proceedings of the National Academy of Sciences of the United States of America, 104, 1777–1782.PubMedCentralPubMedCrossRef

Durot, M., Bourguignon, P. Y., & Schachter, V. (2009). Genome-scale models of bacterial metabolism: Reconstruction and applications. FEMS Microbiology Reviews, 33, 164–190.PubMedCentralPubMedCrossRef

Fleming, R. M., Thiele, I., & Nasheuer, H. P. (2009). Quantitative assignment of reaction directionality in constraint-based models of metabolism: Application to Escherichia coliBiophysical Chemistry, 145, 47–56.PubMedCentralPubMedCrossRef

Folger, O., Jerby, L., Frezza, C., Gottlieb, E., Ruppin, E., & Shlomi, T. (2011). Predicting selective drug targets in cancer through metabolic networks. Molecular Systems Biology, 7, 501.PubMedCentralPubMedCrossRef

Frezza, C., Zheng, L., Folger, O., Rajagopalan, K. N., MacKenzie, E. D., Jerby, L., et al. (2011). Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase. Nature, 477, 225–228.PubMedCrossRef

Ganske, F., & Dell, E. J. (2006). ORAC assay on the FLUOstar OPTIMA to determine antioxidant capacity. BMG LABTECH.

Gudmundsson, S., & Thiele, I. (2010). Computationally efficient flux variability analysis. BMC Bioinformatics, 11, 489.PubMedCentralPubMedCrossRef

Ha, H. C., Thiagalingam, A., Nelkin, B. D., & Casero, R. A, Jr. (2000). Reactive oxygen species are critical for the growth and differentiation of medullary thyroid carcinoma cells. Clinical Cancer Research, 6, 3783–3787.PubMed

Hyduke, D. R., Lewis, N. E., & Palsson, B. O. (2013). Analysis of omics data with genome-scale models of metabolism. Molecular BioSystems, 9, 167–174.PubMedCentralPubMedCrossRef

Jerby, L., & Ruppin, E. (2012). Predicting drug targets and biomarkers of cancer via genome-scale metabolic modeling. Clinical Cancer Research,18, 5572–5584.PubMedCrossRef

Jerby, L., Shlomi, T., & Ruppin, E. (2010). Computational reconstruction of tissue-specific metabolic models: Application to human liver metabolism.Molecular Systems Biology, 6, 401.PubMedCentralPubMedCrossRef

Jerby, L., Wolf, L., Denkert, C., Stein, G. Y., Hilvo, M., Oresic, M., et al. (2012). Metabolic associations of reduced proliferation and oxidative stress in advanced breast cancer. Cancer Research, 72, 5712–5720.PubMedCrossRef

Lenzen, S. (2014). A fresh view of glycolysis and glucokinase regulation: History and current status. Journal of Biological Chemistry, 289, 12189–12194.PubMedCrossRef

Lewis, N. E., Nagarajan, H., & Palsson, B. O. (2012). Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods. Nature Reviews Microbiology, 10, 291–305.PubMedCentralPubMed

Lewis, N. E., Schramm, G., Bordbar, A., Schellenberger, J., Andersen, M. P., Cheng, J. K., et al. (2010). Large-scale in silico modeling of metabolic interactions between cell types in the human brain. Nature Biotechnology, 28, 1279–1285.PubMedCentralPubMedCrossRef

Li, S., Park, Y., Duraisingham, S., Strobel, F. H., Khan, N., Soltow, Q. A., et al. (2013). Predicting network activity from high throughput metabolomics. PLoS Computational Biology, 9, e1003123.PubMedCentralPubMedCrossRef

Locasale, J. W., Grassian, A. R., Melman, T., Lyssiotis, C. A., Mattaini, K. R., Bass, A. J., et al. (2011). Phosphoglycerate dehydrogenase diverts glycolytic flux and contributes to oncogenesis. Nature Genetics, 43, 869–874.PubMedCentralPubMedCrossRef

Mardinoglu, A., Agren, R., Kampf, C., Asplund, A., Nookaew, I., Jacobson, P., et al. (2013). Integration of clinical data with a genome-scale metabolic model of the human adipocyte. Molecular Systems Biology, 9, 649.PubMedCentralPubMedCrossRef

Marin-Hernandez, A., Gallardo-Perez, J. C., Ralph, S. J., Rodriguez-Enriquez, S., & Moreno-Sanchez, R. (2009). HIF-1alpha modulates energy metabolism in cancer cells by inducing over-expression of specific glycolytic isoforms. Mini Reviews in Medicinal Chemistry, 9, 1084–1101.PubMedCrossRef

Mir, M., Wang, Z., Shen, Z., Bednarz, M., Bashir, R., Golding, I., et al. (2011). Optical measurement of cycle-dependent cell growth. Proceedings of the National Academy of Sciences of the United States of America, 108, 13124–13129.PubMedCentralPubMedCrossRef

Mo, M. L., Palsson, B. O., & Herrgard, M. J. (2009). Connecting extracellular metabolomic measurements to intracellular flux states in yeast. BMC Systems Biology, 3, 37.PubMedCentralPubMedCrossRef

Nikiforov, A., Dolle, C., Niere, M., & Ziegler, M. (2011). Pathways and subcellular compartmentation of NAD biosynthesis in human cells: From entry of extracellular precursors to mitochondrial NAD generation. The Journal of biological chemistry, 286, 21767–21778.PubMedCentralPubMedCrossRef

Ogasawara, Y., Funakoshi, M., & Ishii, K. (2009). Determination of reduced nicotinamide adenine dinucleotide phosphate concentration using high-performance liquid chromatography with fluorescence detection: Ratio of the reduced form as a biomarker of oxidative stress. Biological & Pharmaceutical Bulletin, 32, 1819–1823.CrossRef

Paglia, G., Hrafnsdottir, S., Magnusdottir, M., Fleming, R. M., Thorlacius, S., Palsson, B. O., et al. (2012a). Monitoring metabolites consumption and secretion in cultured cells using ultra-performance liquid chromatography quadrupole-time of flight mass spectrometry (UPLC-Q-ToF-MS).Analytical and Bioanalytical Chemistry, 402, 1183–1198.PubMedCrossRef

Paglia, G., Palsson, B. O., & Sigurjonsson, O. E. (2012b). Systems biology of stored blood cells: Can it help to extend the expiration date? Journal of Proteomics, 76, 163–167.PubMedCrossRef

Price, N. D., Schellenberger, J., & Palsson, B. O. (2004). Uniform sampling of steady-state flux spaces: Means to design experiments and to interpret enzymopathies. Biophysical Journal, 87, 2172–2186.PubMedCentralPubMedCrossRef

Reed, J. L., Famili, I., Thiele, I., & Palsson, B. O. (2006). Towards multidimensional genome annotation. Nature Reviews Genetics, 7, 130–141.PubMedCrossRef

Sahoo, S., Aurich, M. K., Jonsson, J. J., & Thiele, I. (2014). Membrane transporters in a human genome-scale metabolic knowledgebase and their implications for disease. Frontiers in Physiology, 5, 91.PubMedCentralPubMedCrossRef

Sahoo, S., & Thiele, I. (2013). Predicting the impact of diet and enzymopathies on human small intestinal epithelial cells. Human Molecular Genetics, 22, 2705–2722.PubMedCentralPubMedCrossRef

Schellenberger, J., & Palsson, B. O. (2009). Use of randomized sampling for analysis of metabolic networks. The Journal of biological chemistry,284, 5457–5461.PubMedCrossRef

Schellenberger, J., Que, R., Fleming, R. M., Thiele, I., Orth, J. D., Feist, A. M., et al. (2011). Quantitative prediction of cellular metabolism with constraint-based models: The COBRA Toolbox v2.0. Nature Protocols, 6, 1290–1307.PubMedCentralPubMedCrossRef

Schmidt, B. J., Ebrahim, A., Metz, T. O., Adkins, J. N., Palsson, B. O., & Hyduke, D. R. (2013). GIM3E: Condition-specific models of cellular metabolism developed from metabolomics and expression data. Bioinformatics (Oxford, England), 29, 2900–2908.CrossRef

Suganuma, K., Miwa, H., Imai, N., Shikami, M., Gotou, M., Goto, M., et al. (2010). Energy metabolism of leukemia cells: Glycolysis versus oxidative phosphorylation. Leukemia & Lymphoma, 51, 2112–2119.CrossRef

Thiele, I., & Palsson, B. O. (2010). A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature Protocols, 5, 93–121.PubMedCentralPubMedCrossRef

Thiele, I., Price, N. D., Vo, T. D., & Palsson, B. O. (2005). Candidate metabolic network states in human mitochondria. Impact of diabetes, ischemia, and diet. The Journal of biological chemistry, 280, 11683–11695.PubMedCrossRef

Thiele, I., Swainston, N., Fleming, R. M., Hoppe, A., Sahoo, S., Aurich, M. K., et al. (2013). A community-driven global reconstruction of human metabolism. Nature Biotechnology, 31, 419–425.PubMedCrossRef

Uhlen, M., Oksvold, P., Fagerberg, L., Lundberg, E., Jonasson, K., Forsberg, M., et al. (2010). Towards a knowledge-based human protein Atlas.Nature Biotechnology, 28, 1248–1250.PubMedCrossRef

Vander Heiden, M. G. (2011). Targeting cancer metabolism: A therapeutic window opens. Nature Reviews Drug Discovery, 10, 671–684.PubMedCrossRef

Vazquez, A., Markert, E. K., & Oltvai, Z. N. (2011). Serine biosynthesis with one carbon catabolism and the glycine cleavage system represents a novel pathway for ATP generation. PLoS ONE, 6, e25881.PubMedCentralPubMedCrossRef

Wishart, D. S., Jewison, T., Guo, A. C., Wilson, M., Knox, C., Liu, Y., et al. (2013). HMDB 3.0—The human metabolome database in 2013. Nucleic Acids Research, 41, D801–D807.PubMedCentralPubMedCrossRef

Zu, X. L., & Guppy, M. (2004). Cancer metabolism: Facts, fantasy, and fiction. Biochemical and Biophysical Research Communications, 313, 459–465.PubMedCrossRef

 

Read Full Post »


CRACKING THE CODE OF HUMAN LIFE: Milestones along the Way – Part IIA

Curator: Larry H Bernstein, MD, FCAP

Introduction and purpose

This material goes beyond the Initiation Phase of Molecular Biology, Part I.

https://pharmaceuticalintelligence.com/2013/02/08/the-initiation-and-growth-of-molecular-biology-and-genomics/
Part II reviews the Human Genome Project and the decade beyond.

In a three part series:
Part IIA.  CRACKING THE CODE OF HUMAN LIFE: Milestones along the Way
Part IIB.  CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics & Computational Genomics
Part IIC.  CRACKING THE CODE OF HUMAN LIFE: Recent Advances in Genomic Analysis and Disease

Part III will conclude with Ubiquitin, it’s Role in Signaling and Regulatory Control.
Part I reviewed the huge expansion of the biological research enterprise after the Second World War. It concentrated on the

  • discovery of cellular structures,
  • metabolic function, and
  • creation of a new science of Molecular Biology.

Part II follows the race to delineation of the Human Genome, discovery methods and fundamental genomic patterns that are ancient in both animal and plant speciation. But it explores both the complexity and the systems view of the architecture that underlies and understanding of the genome.

These articles review a web-like connectivity between inter-connected scientific discoveries, as significant findings have led to novel hypotheses and many expectations over the last 75 years. This largely post WWII revolution has driven our understanding of biological and medical processes at an exponential pace owing to successive discoveries of

  • chemical structure,
  • the basic building blocks of DNA  and proteins,
  • nucleotide and protein-protein interactions,
  • protein folding, allostericity,
  • genomic structure,
  • DNA replication,
  • nuclear polyribosome interaction, and
  • metabolic control.

In addition, the emergence of methods for

  • copying,
  • removal,
  • insertion,
  • improvements in structural analysis
  • developments in applied mathematics that have transformed the research framework.

Part IIA:

CRACKING THE CODE OF HUMAN LIFE:

Milestones along the Way

A NOVA interview with Francis Collins (NHGRI) (FC), J. Craig Venter (CELERA)(JCV), and Eric Lander (EL).
RK: For the past ten years, scientists all over the world have been painstakingly trying to read the tiny instructions buried inside our DNA. And now, finally, the “Human Genome” has been decoded.
EL: The genome is a storybook that’s been edited for a couple billion years.
The following will address the odd similarity of genes between man and yeast

EL: In the nucleus of your cell the DNA molecule resides that is about 10 angstroms wide curled up, but the amount of curling is limited by the negative charges that repel one another, but there are folds upon folds. If the DNA is stretched the length of the DNA would be thousands of feet.
EL: We have known for 2000 years that your kids look a lot like you. Well it’s because you must pass them instructions that give them the eyes, the hair color, and the nose shape they have. RK: Cracking the code of those minuscule differences in DNA that influence health and illness is what the Human Genome Project is all about. Since 1990, scientists all over the world have been involved in the effort to read all three billion As, Ts, Gs, and Cs of human DNA.  It took 10 years to find the one genetic mistake that causes cystic fibrosis. Another 10 years to find the gene for Huntington’s disease. Fifteen years to find one of the genes that increase the risk for breast cancer. One letter at a time, painfully slowly…     And then came the revolution. In the last ten years the entire process has been computerized. The computations can do a thousand every second and that has made all the difference. EL: This is basically a parts list with a lot of parts. If you take an airplane, a Boeing 777, I think it has like 100,000 parts. If I gave you a parts list for the Boeing 777 in one sense you’d know 100,000 components, screws and wires and rudders and things like that.  But you wouldn’t know how to put it together, or why it flies. We now have a parts list, and that’s not enough to understand why it flies.

The Human Genome

The Human Genome (Photo credit: dullhunk)

A Quest For Clarity

Tracy Vence is a senior editor of Genome Technology
Tracy Vence @GenomeTechMag
Projects supported by the US National Institutes of Health will have produced 68,000 total human genomes — around 18,000 of those whole human genomes — through the end of this year, National Human Genome Research Institute estimates indicate. And in his book, The Creative Destruction of Medicine, the Scripps Research Institute’s Eric Topol projects that 1 million human genomes will have been sequenced by 2013 and 5 million by 2014.
Daniel MacArthur, a group leader in Massachusetts General Hospital’s Analytic and Translational Genetics Unit estimates that “From a capacity perspective … millions of genomes are not that far off. If you look at the rate that we’re scaling, we can certainly achieve that.”    The prospect of so many genomes has brought clinical interpretation into focus. But there is an important distinction to be made between the interpretation of an apparently healthy person’s genome and that of an individual who is already affected by a disease.
In an April Science Translational Medicine paper, Johns Hopkins University School of Medicine‘s Nicholas Roberts and his colleagues reported that personal genome sequences for healthy monozygotic twin pairs are not predictive of significant risk for 24 different diseases in those individuals. The researchers concluded that whole-genome sequencing was not likely to be clinically useful. Ambiguities have clouded even the most targeted interpretation efforts.

  • Technological challenges,
  • meager sample sizes,
  • a need for increased,
  • fail-safe automation and most important
  • a lack of community-wide standards for the task.

have hampered researchers’ attempts to reliably interpret the clinical significance of genomic variation.

How signals from the cell surface affect transcription of genes in the nucleus.

James Darnell, Jr., MD, Astor Professor, Rockefeller
After graduation from Washington University School of Medicine he worked with Francois Jacob at the Pasteur Institute in Paris and served as Vice President for Academic Affairs at Rockefeller in 1990-91. He is the coauthor with S.E. Luria of General Virology and the founding author with Harvey Lodish and David Baltimore of Molecular Cell Biology, now in its sixth edition. His book RNA, Life’s Indispensable Molecule was published in July 2011 by Cold Spring Harbor Laboratory Press. A member of the National Academy of Sciences since 1973, recipient of  numerous awards, including the 2003 National Medal of Science, the 2002 Albert Lasker Award.
Using interferon as a model cytokine, the Darnell group discovered that cell transcription was quickly changed by binding of cytokines to the cell surface. The bound interferon led to the tyrosine phosphorylation of latent cytoplasmic proteins now called STATs (signal transducers and activators of transcription) that dimerize by

  • reciprocal phosphotyrosine-SH2 interchange.
  • accumulate in the nucleus,
  • bind DNA and drive transcription.

This pathway has proved to be of wide importance with seven STATs now known in mammals that take part in a wide variety of developmental and homeostatic events in all multicellular animals. Crystallographic analysis defined functional domains in the STATs, and current attention is focused on two areas:

  • how the STATs complete their cycle of  activation and inactivation, which requires regulated tyrosine dephosphorylation; and how
  • persistent activation of STAT3 that occurs in a high proportion of many human cancers contributes to blocking apoptosis in cancer cells.

Current efforts are devoted to inhibiting STAT3 with modified peptides that can enter cells.

Cell cycle regulation and the cellular response to genotoxic stress

Stephen J Elledge, PhD, Gregor Mendel Professor of Genetics and Medicine, Investigator, Howard Hughes Medical Institute, Harvard Medical School
As a postdoctoral fellow at Stanford working on eukaryotic homologous recombination, he serendipitously found a family of genes known as ribonucleotide reductases. He subsequently showed that

  • these genes are activated by DNA damage and
  • could serve as tools to help scientists dissect the signaling pathways
  • through which cells sense and respond to DNA damage and replication stress.

At Baylor College of Medicine he made a second major breakthrough with the discovery of the cyclin-dependent kinase 2 gene (Cdk2), which

  • controls the G1-to-S cell cycle transition,
  • an entry checkpoint for the cell proliferation cycle and
  • a critical regulatory step in tumorigenesis.

From there, using a novel “two-hybrid” cloning method he developed, Elledge and Wade Harper, PhD, proceeded to

  • isolate several members of the Cdk2-inhibitory family.

Their discoveries included the p21 and p57 genes, mutations in the latter (responsible for Beckwith-Wiedemann syndrome), characterized by somatic overgrowth and increased cancer risk. Elledge is also recognized for his work in understanding

  • proteome remodeling through ubiquitin-mediated proteolysis.
  • they identified F-box proteins that regulate protein degradation in the cell by
  1. binding to specific target protein sequences and then
  2. marking them with ubiquitin for destruction by the cell’s proteasome machinery.

This breakthrough resulted in

  • the elucidation of the cullin ubiquitin ligase family,
  • which controls regulated protein stability in eukaryotes.

nature10774-f5.2  nature10774-f3.2   ubiquitin structures  Rn1  Rn2

Elledge’s recent research has focused on the cellular mechanisms underlying DNA damage detection and cancer using genetic technologies. In collaboration with Cold Spring Harbor Laboratory researcher Gregory Hannon, PhD, Elledge has generated complete human and mouse short hairpin RNA (shRNA) libraries for genome-wide loss-of-function studies. Their efforts have led to

  • the identification of a number of tumor suppressor proteins
  • genes upon which cancer cells uniquely depend for survival.

This work led to the development of the “non-oncogene addiction” concept. This is noted as follows:

  • proteome remodeling through ubiquitin-mediated proteolysis
  • F-box proteins regulate protein degradation in the cell by binding to specific target protein sequences
  • and then marking them with ubiquitin for destruction by the cell’s proteasome machinery
  • elucidation of the cullin ubiquitin ligase family, which controls regulated protein stability in eukaryotes

Playing the dual roles of inventor and investigator, Elledge developed original techniques to define

  • what drives the cell cycle and
  • how cells respond to DNA damage.

By using these tools, he and his colleagues have identified multiple genes involved in cell-cycle regulation.

Elledge’s work has earned him many awards, including a 2001 Paul Marks Prize for Cancer Research and a 2003 election to the National Academy of Sciences. In his Inaugural Article (1), published in this issue of PNAS, Elledge and his colleagues describe the function of Fbw7, a protein involved in controlling cell proliferation (see below). Elledge studied the error-prone DNA repair mechanism in E-Coli (Escherichia coli) called SOS mutagenesis for his PhD thesis at MIT. His work identified  and described

  • the regulation of a group of enzymes now known as error-prone polymerases,
  • the first members of which were the umuCD genes in E. coli.

It was then that he developed a new cloning tool. Elledge invented a technique that allowed him to approach future cloning problems of this type with great rapidity. With the new technique, “you could make large libraries in lambda that behave like plasmids. We called them `phasmid’ vectors, like plasmid and phage together”. The phasmid cloning method was an early cornerstone for molecular biology research.

Elledge began working on homologous recombination in postdoctoral fellowship at Stanford University, an important niche in the field of eukaryotic genetics. Working with the yeast genome, Elledge searched for rec A, a gene that allows DNA to recombine homologously. Although he never located rec A, he discovered a family of genes known as ribonucleotide reductases (RNRs), which are involved in DNA production. Rec A and RNRs share the same last 4 amino acids, which caused an antibody crossreaction in one of Elledge’s experiments. Initially disappointed with the false positives in his hunt for rec A, Elledge was later delighted with his luck. He found that

  • RNRs are turned  on by DNA damage, and
  • these genes are regulated by the cell cycle.

Prior to leaving Stanford, Elledge attended a talk at the University of California, San Francisco, by Paul Nurse, a leader in cell-cycle research who would later win the 2001 Nobel Prize in medicine. Nurse described his success in isolating the homolog of a key human cell-cycle kinase gene, Cdc2, by using a mutant strain of yeast (8). Although Nurse’s methods were primitive, Elledge was struck by the message he carried: that

  • cell-cycle regulation was functionally conserved, and
  • many human genes could be isolated by looking for complimentary genes in yeast.

Elledge then took advantage of his past successes in building phasmid vectors to build a versatile human cDNA library that could be expressed in yeast. After setting up a laboratory at Baylor, he introduced this library into yeast, screening for complimentary cell-cycle genes.  He quickly identified the same Cdc2 gene isolated by Nurse. However, Elledge also discovered a related gene known as Cdk2. Elledge subsequently found that

  • Cdk2 controlled the G1 to S cell-cycle transition, a step that often goes awry in cancer. These results were published in the EMBO Journal in 1991.

He then continued to use

  • RNRs to perform genetic screens to
  • identify genes involved in sensing and responding to DNA damage.

He subsequently worked out the

  • signal transduction pathways in both yeast and humans that recognize damaged DNA and replication problems.

These “checkpoint” pathways are central to the

  • prevention of genomic instability and a key to understanding tumorigenesis.

This contribution is part of the special series of Inaugural Articles by members of the National Academy of Sciences elected on April 29, 2003.

Defective cardiovascular development and elevated cyclin E and Notch proteins in mice lacking the Fbw7 F-box protein.

Tetzlaff MT, Yu W, Li M, Zhang P, Finegold M, Mahon K , Harper JW, Schwartz RJ, and SJ Elledge. PNAS 2004; 101(10): 3338-3345. cgi doi 10.1073.  pnas.0307875101

The mammalian F-box protein Fbw7 and its Caenorhabditis elegans counterpart Sel-10 have been implicated in

  • the ubiquitin-mediated turnover of cyclin E
  • as well as the Notch Lin-12 family of transcriptional activators. Both unregulated
  1. Notch and cyclin E
  2. promote tumorigenesis, and
  3. inactivate mutations in human

Fbw7 studies suggest that it may be a tumor suppressor. To generate an in vivo system to assess the consequences of such unregulated signaling, we generated mice deficient for Fbw7.  Fbw7-null mice die around 10.5 days post coitus because of a combination of deficiencies in hematopoietic and vascular development and heart chamber mutations. The absence of Fbw7 results in elevated levels of cyclin E, concurrent with inappropriate DNA replication in placental giant trophoblast cells. Moreover, the levels of both Notch 1 and Notch 4 intracellular domains were elevated, leading to stimulation of downstream transcriptional pathways involving Hes1, Herp1, and Herp2. These data suggest essential functions for Fbw7 in controlling cyclin E and Notch signaling pathways in the mouse.

Science as an Adventure

Ubiquitins

Prof. Avram Hershko – Science as an Adventure
Prof. Avram Hershko shared the 2004 Nobel Prize in Chemistry with Aaron Ciechanover and Irwin Rose for “for the discovery of ubiquitin-mediated protein degradation.”

http://www.youtube.com/watch?v=lGJvsmG3mhw&feature=player_detailpage&list=EC8814C902ACB98559

Gene Switches

Nipam Patel is a professor in the Departments of Molecular and Cell Biology and Integrative Biology at UC Berkeley and runs a research laboratory that studies the role, during embryonic development, of homeotic genes (the genetic switches described in this feature). “Ghost in Your Genes” focuses on epigenetic “switches” that turn genes “on” or “off.” But not all switches are epigenetic; some are genetic. That is, other genes within the chromosome turn genes on or off. In an animal’s embryonic stage, these gene switches play a predominant role in laying out the animal’s basic body plan and perform other early functions;

  • the epigenome begins to take over during the later stages of embryogenesis.

Beginning as a fertilized single egg that egg becomes many different kinds of cells.  Altogether, multicellular organisms like humans have thousands of differentiated cells. Each is optimized for use in the brain, the liver, the skin, and so on. Remarkably, the DNA inside all these cells is exactly the same. What makes the cells differ from one another is that different genes in that DNA are either turned on or off in each type of cell.

Take a typical cell, such as a red blood cell. Each gene within that cell has a coding region that encodes the information used to make a particular protein. (Hemoglobin shuttles oxygen to the tissues and carbon dioxide back out to the lungs—or gills, if you’re a fish.) But another region of the gene, called “regulatory DNA,” determines whether and when the gene will be expressed, or turned on, in a particular kind of cell. This precise transcribing of genes is handled by proteins known as transcription factors, which bind to the regulatory DNA, thereby generating instructions for the coding region.

One important class of transcription factors is encoded by the so called homeotic, or Hox, genes. Found in all animals, Hox genes act to “regionalize” the body along the embryo’s anterior-to-posterior (head-to-tail) axis. In a fruit fly, for example, Hox genes lay out the various main body segments—the head, thorax, and abdomen. Amazingly, all animals, from fruit flies to mice to people, rely on the same basic Hox-gene complex. Using different-colored antibody stains, we can see exactly where and to what degree Hox genes are expressed. Each Hox gene is expressed in a specific region along the anterior-to-posterior axis of the embryo.

A fly’s body has three main divisions: head, thorax, and abdomen. We’ll focus on the thorax, which itself has three main segments. In a normal adult fly, the second thoracic segment features a pair of wings, while the third thoracic segment has a pair of small, balloon-shaped structures called halteres. A modified second wing, the haltere serves as a flight stabilizer. In order for the pair of wings and the pair of halteres (as well as all other parts of the fly) to develop properly, the fly’s suite of

  • Hox genes must be expressed in a precise way and at precise times.

During development, the fly’s two wings grow from a structure in the larva known as the wing imaginal disk. (An imago is an insect in its final, adult state.) The haltere grows from the larval haltere imaginal disk. Remember the Ubx Hox gene? Using staining again, we can detect the gene product of Ubx. This reveals that

  • the Ubx gene is naturally “off” in the wing disk—
  • and is “on” in the haltere disk.
  • Now you’ll see what happens when the Ubx gene—just one of a large number of Hox genes—is turned off in the haltere disk. What if a genetic mutation caused the Ubx gene to be turned off, during the larval stage, in the third thoracic segment, the segment that normally produces the haltere? Instead of a pair of halteres, the fly has a second set of wings. With the switch of that single Hox gene, Ubx, from on to off, the third thoracic segment becomes an additional second thoracic segment and the pair of halteres became a second pair of wings. This illustrates the remarkable ability of transcription factors like Ubx to control patterning as well as cell type during development.

ENCODE

A. Data Suggests “Gene” Redefinition

As part of a huge collaborative effort called ENCODE (Encyclopedia of DNA Elements), a research team led by Cold Spring Harbor Laboratory (CSHL) Professor Thomas Gingeras, PhD, publishes a genome-wide analysis of RNA messages, called transcripts, produced within human cells.
Their analysis—one component of a massive release of research results by ENCODE teams from 32 institutes in 5 countries, with 30 papers appearing in 3 different high-level scientific journals—shows that three-quarters of the genome is capable of being transcribed.  This indicates that nearly all of our genome is dynamic and active.  It stands in marked contrast to consensus views prior to ENCODE’s comprehensive research efforts, which suggested that

  • only the small protein-encoding fraction of the genome was transcribed.

The vast amount of data generated with advanced technologies by Gingeras’ group and others in the ENCODE project changes the prevailing understanding of what defines a gene. The current outstanding question concerns

  • the nature and range of those functions.  It is thought that these
  • “non-coding” RNA transcripts act something like components of a giant, complex switchboard, controlling a network of  many events in the cell by
  1. regulating the processes of
  2. replication,
  3. transcription
  4. and translation

– that is, the copying of DNA and the making of proteins is based on information carried by messenger RNAs.  With the understanding that so much of our DNA can be transcribed into RNA comes the realization that there is much less space between what we previously thought of as genes, Gingeras points out.

The full ENCODE Consortium data sets can be freely accessed through

  • the ENCODE project portal as well as at the University of California at Santa Cruz genome browser,
  • the National Center for Biotechnology Information, and
  • the European Bioinformatics Institute.

Topic threads that run through several different papers can be explored via the ENCODE microsite page at http://Nature.com/encode.    Date: September 5, 2012   Source: Cold Spring Harbor Laboratory

1000 Genomes Project Team Reports on Variation Patterns

(from Phase I Data) October 31, 2012 GenomeWeb

In a study appearing online today in Nature, members of the 1000 Genomes Project Consortium presented an integrated haplotype map representing the genomic variation present in more than 1,000 individuals from 14 human populations.  Using data on 1,092 individuals tested by

  • low-coverage whole-genome sequencing,
  • deep exome sequencing, and/or
  • dense genotyping,

the team looked at the nature and extent of the rare and common variation present in the genomes of individuals within these populations. In addition to population-specific differences in common variant profiles, for example, the researchers found distinct rare variant patterns within populations from different parts of the world — information that is expected to be important in interpreting future disease studies. They also encountered a surprising number of the variants that are expected to impact gene function, such as

  • non-synonymous changes,
  • loss-of-function variants, and, in some cases,
  • potentially damaging mutations.

ENCODE was designed to pick up where the Human Genome Project left off.
Although that massive effort revealed the blue­print of human biology, it quickly became clear that the instruction manual for reading the blueprint was sketchy at best. Researchers could identify in its 3 billion letters many of the regions that code for proteins, but they make up little more than 1% of the genome, contained in around 20,000 genes. ENCODE, which started in 2003, is a massive data-collection effort designed to catalogue the

  • ‘functional’ DNA sequences,
  • learn when and in which cells they are active and
  • trace their effects on how the genome is
  1. packaged,
  2. regulated and
  3. read.

After an initial pilot phase, ENCODE scientists started applying their methods to the entire genome in 2007. That phase came to a close with the publication of 30 papers, in Nature, Genome Research and Genome Biology. The consortium has assigned some sort of function to roughly 80% of the genome, including

  • more than 70,000 ‘promoter’ regions — the sites, just upstream of genes, where proteins bind to control gene expression —
  • and nearly 400,000 ‘enhancer’ regions that regulate expression of  distant genes (see page 57)1. But the job is far from done.

Junk DNA? What Junk DNA?

New data reveals that at least 80% of the human genome encodes elements that have some sort of biological function. [© Gernot Krautberger – Fotolia.com] Far from containing vast amounts of junk DNA between its protein-coding genes, at least 80% of the human genome encodes elements that have some sort of biological function, according to newly released data from the Encyclopedia of DNA Elements (Encode) project, a five-year initiative that aims to delineate all functional elements within human DNA. The massive international project, data from which are published in 30 different papers in Nature, Genome Research, Genome Biology, the Journal of Biological Chemistry, Science, and Cell, has identified four million gene switches, effectively

  • regulatory regions in the genome where
  • proteins interact with the DNA to control gene expression.

Overall, the Encode data define regulatory switches that are scattered all over the three billion nucleotides of the genome. In fact, the data suggests,

  • the regions that lie between gene-coding sequences contain a wealth of previously unrecognized functional elements,Including
  • nonprotein-coding RNA transcribed sequences,
  • transcription factor binding sites,
  • chromatin structural elements, and
  • DNA methylation sites.

The combined results suggest that 95% of the genome lies within 8 kb of a DNA-protein interaction, and 99% lies within 1.7 kb of at least one of the biochemical events, the researchers say. Importantly, given the complex three-dimensional nature of DNA, it’s also apparent that

  • a regulatory element for one gene may be located quite some ‘linear’ distance from the gene itself.

“The information processing and the intelligence of the genome reside in the regulatory elements,” explains Jim Kent, director of the University of California, Santa Cruz Genome Browser project and head of the Encode Data Coordination Center. “With this project, we probably went from understanding less than 5% to now around 75% of them.”
The ENCODE results also identified SNPs within regulatory regions that are associated with a range of diseases, providing new insights into the roles that

  • noncoding DNA plays in disease development.

“As much as nine out of 10 times, disease-linked genetic variants are not in protein-coding regions,” comments Mike Pazin, Encode program director at the National Human Genome Research Institute.  “Far from being junk DNA, this regulatory DNA clearly makes important contributions to human disease.”

Other Related Articles on this Open Access Online Scientific Journal, include the following: 

Big Data in Genomic Medicine LHB

https://pharmaceuticalintelligence.com/2012/12/17/big-data-in-genomic-medicine/

BRCA1 a tumour suppressor in breast and ovarian cancer – functions in transcription, ubiquitination and DNA repair S Saha
http://pharmaceuticalintelligence.com/2012/12/04/brca1-a-tumour-suppressor-in-breast-and-ovarian-cancer-functions-in-transcription-ubiquitination-and-dna-repair/

Computational Genomics Center: New Unification of Computational Technologies at Stanford A Lev-Ari
http://pharmaceuticalintelligence.com/2012/12/03/computational-genomics-center-new-unification-of-computational-technologies-at-stanford/

Personalized medicine gearing up to tackle cancer ritu saxena
http://pharmaceuticalintelligence.com/2013/01/07/personalized-medicine-gearing-up-to-tackle-cancer/

Differentiation Therapy – Epigenetics Tackles Solid Tumors sj Williams
http://pharmaceuticalintelligence.com/2013/01/03/differentiation-therapy-epigenetics-tackles-solid-tumors/

Mechanism involved in Breast Cancer Cell Growth: Function in Early Detection & Treatment A Lev-Ari
http://pharmaceuticalintelligence.com/2013/01/17/mechanism-involved-in-breast-cancer-cell-growth-function-in-early-detection-treatment/

The Molecular pathology of Breast Cancer Progression tilde barliya`
http://pharmaceuticalintelligence.com/2013/01/10/the-molecular-pathology-of-breast-cancer-progression/

Paradigm Shift in Human Genomics – Predictive Biomarkers and Personalized Medicine – Part 1 (pharmaceuticalintelligence.com) A Lev-Ari

http://pharmaceuticalintelligence.com/2013/01/13/paradigm-shift-in-human-genomics-predictive-biomarkers-and-personalized-medicine-part-1/

LEADERS in Genome Sequencing of Genetic Mutations for Therapeutic Drug Selection in Cancer Personalized Treatment: Part 2 A Lev-Ari
http://pharmaceuticalintelligence.com/2013/01/13/leaders-in-genome-sequencing-of-genetic-mutations-for-therapeutic-drug-selection-in-cancer-personalized-treatment-part-2/

Personalized Medicine: An Institute Profile – Coriell Institute for Medical Research: Part 3 A Lev-Ari
http://pharmaceuticalintelligence.com/2013/01/13/personalized-medicine-an-institute-profile-coriell-institute-for-medical-research-part-3/

Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders @ http://pharmaceuticalintelligence.com ALA
http://pharmaceuticalintelligence.com/2013/01/13/7000/Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders/

GSK for Personalized Medicine using Cancer Drugs needs Alacris systems biology model to determine the in silico effect of the inhibitor in its “virtual clinical trial” A Lev-Ari
http://pharmaceuticalintelligence.com/2012/11/14/gsk-for-personalized-medicine-using-cancer-drugs-needs-alacris-systems-biology-model-to-determine-the-in-silico-effect-of-the-inhibitor-in-its-virtual-clinical-trial/

Recurrent somatic mutations in chromatin-remodeling and ubiquitin ligase complex genes in serous endometrial tumors S Saha
http://pharmaceuticalintelligence.com/2012/11/19/recurrent-somatic-mutations-in-chromatin-remodeling-and-ubiquitin-ligase-complex-genes-in-serous-endometrial-tumors/

Personalized medicine-based cure for cancer might not be far away ritu saxena
http://pharmaceuticalintelligence.com/2012/11/20/personalized-medicine-based-cure-for-cancer-might-not-be-far-away/

Human Variome Project: encyclopedic catalog of sequence variants indexed to the human genome sequence A Lev-Ari
http://pharmaceuticalintelligence.com/2012/11/24/human-variome-project-encyclopedic-catalog-of-sequence-variants-indexed-to-the-human-genome-sequence/

Prostate Cancer Cells: Histone Deacetylase Inhibitors Induce Epithelial-to-Mesenchymal Transition sjwilliams
http://pharmaceuticalintelligence.com/2012/11/30/histone-deacetylase-inhibitors-induce-epithelial-to-mesenchymal-transition-in-prostate-cancer-cells/

Inspiration From Dr. Maureen Cronin’s Achievements in Applying Genomic Sequencing to Cancer Diagnostics A Lev-Ari
http://pharmaceuticalintelligence.com/2013/01/10/inspiration-from-dr-maureen-cronins-achievements-in-applying-genomic-sequencing-to-cancer-diagnostics/

The “Cancer establishments” examined by James Watson, co-discoverer of DNA w/Crick, 4/1953 A Lev-Ari
http://pharmaceuticalintelligence.com/2013/01/09/the-cancer-establishments-examined-by-james-watson-co-discover-of-dna-wcrick-41953/

Directions for genomics in personalized medicine lhb
http://pharmaceuticalintelligence.com/2013/01/27/directions-for-genomics-in-personalized-medicine/

How mobile elements in “Junk” DNA promote cancer. Part 1: Transposon-mediated tumorigenesis. SJwilliams
http://pharmaceuticalintelligence.com/2012/10/31/how-mobile-elements-in-junk-dna-prote-cancer-part1-transposon-mediated-tumorigenesis/

Mitochondria: More than just the “powerhouse of the cell” eritu saxena
http://pharmaceuticalintelligence.com/2012/07/09/mitochondria-more-than-just-the-powerhouse-of-the-cell/

Mitochondrial fission and fusion: potential therapeutic targets? Ritu saxena
http://pharmaceuticalintelligence.com/2012/10/31/mitochondrial-fission-and-fusion-potential-therapeutic-target/

Mitochondrial mutation analysis might be “1-step” away ritu saxena
http://pharmaceuticalintelligence.com/2012/08/14/mitochondrial-mutation-analysis-might-be-1-step-away/

mRNA interference with cancer expression lhb
http://pharmaceuticalintelligence.com/2012/10/26/mrna-interference-with-cancer-expression/

Expanding the Genetic Alphabet and linking the genome to the metabolome LHB
http://pharmaceuticalintelligence.com/2012/09/24/expanding-the-genetic-alphabet-and-linking-the-genome-to-the-metabolome/

Breast Cancer, drug resistance, and biopharmaceutical targets lhb
http://pharmaceuticalintelligence.com/2012/09/18/breast-cancer-drug-resistance-and-biopharmaceutical-targets/

Breast Cancer: Genomic profiling to predict Survival: Combination of Histopathology and Gene Expression Analysis A Lev-Ari
http://pharmaceuticalintelligence.com/2012/12/24/breast-cancer-genomic-profiling-to-predict-survival-combination-of-histopathology-and-gene-expression-analysis/

Gastric Cancer: Whole-genome reconstruction and mutational signatures A Lev-Ari
http://pharmaceuticalintelligence.com/2012/12/24/gastric-cancer-whole-genome-reconstruction-and-mutational-signatures-2/

Ubiquinin-Proteosome pathway, autophagy, the mitochondrion, proteolysis and cell apoptosis lhb
http://pharmaceuticalintelligence.com/2012/10/30/ubiquinin-proteosome-pathway-autophagy-the-mitochondrion-proteolysis-and-cell-apoptosis/

Genomic Analysis: FLUIDIGM Technology in the Life Science and Agricultural Biotechnology A Lev-Ari
http://pharmaceuticalintelligence.com/2012/08/22/genomic-analysis-fluidigm-technology-in-the-life-science-and-agricultural-biotechnology/

Reveals from ENCODE project will invite high synergistic collaborations to discover specific targets A. Sarkar

https://pharmaceuticalintelligence.com/2012/09/30/reveals-from-encode-project-will-lead-to-confusion-or-specific-target/

ENCODE: the key to unlocking the secrets of complex genetic diseases R. Saxena

https://pharmaceuticalintelligence.com/2012/09/26/encode-the-key-to-unlocking-the-secrets-of-complex-genetic-diseases/

Impact of evolutionary selection on functional regions: The imprint of evolutionary selection on ENCODE regulatory elements is manifested between species and within human populations s Saha

https://pharmaceuticalintelligence.com/2012/09/20/impact-of-evolutionary-selection-on-functional-regions-the-imprint-of-evolutionary-selection-on-encode-regulatory-elements-is-manifested-between-species-and-within-human-populations/

ENCODE Findings as Consortium A Lev-Ari

https://pharmaceuticalintelligence.com/2012/09/10/encode-findings-as-consortium/

Genomics Orientations for Personalized Medicine SJH, ALA, LHB

https://pharmaceuticalintelligence.com/biomed-e-books/genomics-orientations-for-personalized-medicine/

2013 Genomics: The Era Beyond the Sequencing of the Human Genome: Francis Collins, Craig Venter, Eric Lander, et al.

https://pharmaceuticalintelligence.com/2013/02/11/2013-genomics-the-era-beyond-the-sequencing-human-genome-francis-collins-craig-venter-eric-lander-et-al/

 Related Articles

Read Full Post »