Transcript microRNA Research Design Strategies from the ... › sites › default › files... ·...
Transcript of Transcript microRNA Research Design Strategies from the ... › sites › default › files... ·...
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microRNA Research Design: Strategies from the Experts
[0:00:00] Sean Sanders: Hello and welcome to the Science/AAAS webinar. My name is Sean
Sanders and I’m the commercial editor at Science. Slide 1 The topic of today’s discussion is, “microRNA Research Design.”
MicroRNAs are increasingly being accepted as playing a crucial regulatory role in normal and dysfunctional cellular processes and are thought to be involved in almost every human pathology currently under study. From tumor progression and viral host interactions, to immune response, and cell fate determination, microRNAs are quickly growing in importance as the "master regulators" in cell cycle processes. In this webinar, our experts will share their knowledge and expertise to help you determine the optimal path to a successful miRNA research project.
With me in the studio today, I have three fantastic panelists who will help
us understand this topic. Just to my left, Dr. Peter Nelson from the University of Kentucky in Lexington. Next to him, Dr. Kai Wang from the Institute for Systems Biology in Seattle, Washington. And finally, Dr. Neil Kubica from Harvard Medical School in Boston, Massachusetts.
A warm welcome to all of you. Thanks for being here. Dr. Kai Wang: Thank you. Dr. Peter Nelson: Thank you very much for having us, Sean. Sean Sanders: A reminder to everybody watching that you can see an enlarged version
of any of the slides by just clicking the enlarge slides button located underneath the slide window of your web console. You can also download a PDF copy of all the slides by using the download slides button. If you’re joining us live, you can submit a question to the panel at any time by typing it into the ask‐a‐question box on the bottom left of your viewing console below the video screen and clicking the submit button. As always, please do keep your questions short and to the point. I’ll get to as many of them as possible in the Q&A session following the talks.
Last but not least, thank you Exiqon for their sponsorship of today’s
webinar. Slide 2
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Now, I’d like to introduce our first speaker for this webinar, Dr. Peter Nelson. Dr. Nelson obtained his undergraduate degree in biology and economics from Rice University in Houston, Texas, followed by a joint M.D. and Ph.D. at the University of Chicago where his graduate work looked at animal models of Alzheimer’s disease. After completing his medical residency and clinical fellowship training at the University of Pennsylvania, Dr. Nelson worked as a postdoctoral fellow and research associate studying miRNAs. In 2006, Dr. Nelson moved to the University of Kentucky, where he is currently an associate professor in the Department of Pathology and Laboratory Medicine. He runs a laboratory in the Sanders‐Brown Center on Aging and serves as an attending physician in neuropathology. Dr. Nelson invented new techniques to analyze and manipulate microRNAs, and his current research interests involve studying how microRNA biology is altered in neurodegenerative diseases, in order to better understand pathogenesis and to obtain clues relevant to potential therapies.
Dr. Nelson, welcome. Dr. Peter Nelson: Thank you very much, Sean, and thank you very much for having me here
today. Slide 3 My discussion is going to focus on microRNA research design strategies in
the context of in situ hybridization in the brain, and this is an overview of it. I’ve been asked to give a little bit of introduction of what microRNAs are and what they do. And then I’m going to talk about the topic at hand very briefly.
Slide 4 So, the things that we’re talking about are these microRNAs, which are
born in the nucleus. They are transcribed and exist first in this long stem loop structures that are serially processed and exported into the cytoplasm, where they interact with various proteins that are not the really the topic of our conversation today. They are eventually cleaved to a mature form about 22 nucleotides in length. And these go on to regulate some of the translational processing of miRNAs and probably do other things as well.
Slide 5 As this sort of cartoon depiction shows what translation is, it shows a
cartoon miRNA. And translation is a process by which a polypeptide is formed from the miRNA. And it’s increasingly clear that miRNA translation is a key focal point in gene expression regulation. One manifestation of that is the fact that especially in humans whenever they’ve used high throughput means to assess it, the correlation between levels of miRNAs and levels of protein is extremely weak.
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Slide 6 One of the reasons for that presumably is microRNAs, which use very
ancient mechanisms of regulation at the level of miRNA translation. The canonical manner in which this occurs is that the microRNA pairs in a complementary fashion ‐‐ partial complementary to the 3’ UTR of the miRNA and suppresses the translation of that particular miRNA.
Slide 7 This is a complicated slide and the point really is just to say it’s from a
review of which Guiliang Tang was the main author. And the main point is just to say that there are many different mechanisms by which microRNAs work. We only know some of them and particularly, the focus has been upon how the microRNAs inhibit translation from cognate miRNAs.
Slide 8 [0:04:58] So, one of the important things that are to be learned about microRNAs is
where they’re expressed and how their expression changes in disease. And this cartoon shows one way that this is studied using microarrays or many other different platforms. On the left, you can see a brain where the hippocampus is nice and robust and juicy, and that’s a normal control. On the right, you can see a brain where the same area is shriveled away and dying, and that is an Alzheimer’s disease hippocampus. And tissue level microRNA profiling can show the differences between these two conditions.
Slide 9 However, it’s very important to note that tissue level microRNA profiling
is confounded by all sorts of heterogeneity in the tissue. And this is very relevant to human brain studies in which tissue level profiling is performed.
Slide 10 Just one somewhat, I guess, trivial way that you could look at it is that
when people say that profiling of cortex is used and this has a change at profiling. Well, cortex comprises two very different types of cells. Those that are in the gray matter and those that are in the white matter. The cells that are in the gray matter are totally different from those there in the white matter. And we found that the gray matter and white matter have very different microRNA profiles as you might expect. And if you don’t dissect out the gray matter and the white matter then you can be just showing differences in the relative ratios of gray matter to white matter in your experiment.
Slide 11 Another thing that’s very important is to compare apples to apples. These
are quick photomicrographs to show what can happen in a disease. On the left, you see a normal cerebral cortex. And I can’t use my pointer
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here to show it, but you have very healthy neurons and quiescent astrocytes, the normal constituents of the brain.
In Alzheimer’s disease and other diseases where they have a devastating
impact, they can have a complete replacement such as the normal cell constituents are not there. And if you do a comparative tissue level profiling, all you’re doing is really showing the difference between one group of cells and another group of cells that has nothing to do with the disease pathogenesis itself.
Slide 12 So one of the theme ‐‐ the main theme, if I can just make one point
during this brief discussion, is that it’s important to complement tissue level microRNA profiling with cellular and subcellular profiling that you can do with in situ hybridization.
Slide 13 So, in studying the human brain disease, there are some technical notes.
It is important to compare apples to apples, and so you need to try to make it so you’re not having any massive switching in the types of cells that are in your material.
You have to dissect carefully by people who understand some degree of
the neural anatomy and the diseases at hand. And, I guess, it’s intrinsic to in situ hybridization that you’re extending tissue level profiling to the cellular level. And, of course, it’s very complex in humans because there are agonal events that can, I think, be confounders.
But with all that being said, that human brain is a very important source
both for testing hypotheses and generating new hypotheses, as long as we acknowledge that there are huge gaps in our current understanding in this context.
Slide 14 So, here are the people that make up most of the ‐‐ do most of the work
in our lab. And I’d like to send a shout‐out to Jim Dimayuga, Willa Huang or Dada Huang, Wang‐Xia Wang. But the person that does most of this work with in situ hybridization is shown here, Bernard Wilfred who has done a magnificent job and he gets great results.
Slide 15 And so, I’m just going to give a quick technical overview of how we do in
situ hybridization in the brain. So, we have a rapid autopsy team so we get very short postmortem interval brain tissue. And it’s been shown that microRNAs can degrade relatively quickly and more important than quickly is that they do differentially such that different microRNAs degrade at different rates.
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So, we fix in paraformaldehyde and we cut the brain sections at 20 microns on a freezing microtome, which still enables us to get great results in terms of morphology. Then we store the sections at ‐80 in a freezer until we’re ready to use them.
Slide 23 So, I will say after the last slide, there’s another slide that anybody who
wants to can look at some of the technical tips that Bernard has given us in terms of things you may not necessarily quickly think of that I’m not including on this talk. But for those of you that are going back afterwards that want to look at the slides, you can look at the very last slide and there are some technical tips for brain in situ hybridization.
Slide 16 What we do is a three‐day protocol. And on the one hand, a three‐day
protocol is kind of a slug. It’s kind of a pain to go through all that much time. But on the other hand, we get just really, really nice, pretty results. And I can recommend not trying to take too many shortcuts.
[0:09:56] Day 1 goes from the freezer to the hyb chamber. Hyb chamber to the
antibodies, day 2. And then antibody to the stain. Slide 17 So, I’m not being a show for anybody, but I have to say that the Exiqon
probes that we use have given us really very nice results. And then, there’s another product here from EM Sciences, this Hyb chamber, that is very effective in terms of allowing you to manipulate your slides and get good hyb results in a hyb chamber. And this is shown here on the right ‐‐ how it work on the actual physical slide.
Slide 18 Slide 19 So, here are some of the steps ‐‐ I’m not going to go through them
exhaustively ‐‐ that you do on your first day. Then you get into hybridization. You need to humidify your hyb oven and you then put it into the antibody overnight. And then the next day, you develop it with your NBT/BCIP. This is both in situ hybridization and the antibody reaction because you’re doing anti‐Dig, and these are Dig‐labeled LNA Exiqon probes. So, this is something like what ‐‐ some of the results that you might get.
Slide 20 And these are some of our published results. I’m not going to go into
them in any great detail, other than to say that they can really find very interesting and unexpected results. And you can correlate these to pathology, to other microRNAs. And we’re working with Exiqon to develop a technology to get double‐labeled fluorescent probes so that
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we can get double and triple labeled relationships of different markers in the brain.
Here, you can see the six layers of cerebral cortex and you can see that
one microRNA probe clearly labels different from another one. Slide 21 Here’s an In‐Press paper on the transentorhinal cortex relating the in situ
hybridization to the pathology in this area. And we had some interesting results.
So, I don’t want to take too much more time. I just want to give an
overview of what we do, why you do it, and some of our results. Slide 22 I want to say thanks to the people that enabled us to do our lab work.
And then for anybody who’s interested in technical tips, we can either talk about them later or you can look in the next slide after this one. But for now, I’ll pass it on over.
Sean Sanders: Great. Thank you, Dr. Nelson. Slide 23 So, we’re going to put that slide up for you right now. You can take a
quick look at it. And also, you can download the slides using the download slides button, and that’ll get you a PDF, which will include that slide.
Slide 24 Great. So, we’re going to move right on to our next speaker and that is
Dr. Kai Wang. Dr. Wang completed his undergraduate training in Taiwan before moving to the United States for the study of his Ph.D. at Oregon State University. Following an assistant professorship at the University of Washington in Seattle, Dr. Wang moved to industry and in 2001 co‐founded PhenoGenomics Corporation. In 2008, he took a senior research scientist position at the Institute for Systems Biology in Seattle, Washington, where he currently conducts research into the application and biological function of microRNAs. Besides his over 20 years of experience as a molecular biologist in academia and industry, Dr. Wang is also a diplomate of the American Board of Toxicology and is named on three US patents.
Dr. Wang, welcome. Dr. Kai Wang: Thank you, Sean. It’s a pleasure to be here to share some of our
experience in microRNA with you. Slide 25
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So, our research focus is using systems approach to study various diseases, especially disease in lung, brain, and liver, so that we can get a better understanding of the biology through modeling and network analysis.
Slide 26 But a fundamental of a systems approach is based on two. One is be able
to accurately measure the molecule of interest. So, in this context, you have to ask yourself, do we actually know how to measure microRNA correctly. And the second is be able to integrate, interpret, and test those results you have. So, again, here you have to ask about yourself, do we actually understand how microRNA is actually being expressed and how microarray is being processed, and also, do we know how to interpret and integrate the result from microRNA.
Slide 27 So, let’s look at microRNA measurement. Currently, there are three major
platforms for microRNA measurement. They are microarray based or qPCR based or sequencing. The sequencing here is mainly talking about next generation sequencing based technology.
People did interplatform and intraplatform comparisons. The result
usually is between platforms, the consistency of the result usually are low. Within a platform, it all depends on the technology you use. Sometimes the reproducibility also can be an issue.
[0:14:56] So, I think, all of this is probably caused by interesting property of some
microRNAs. As Pete mentioned earlier, the microRNAs are a very short stretch of nucleotide sequences. They’re usually 20 to 22 nucleotides long. So, that gives you a very small room to manipulate, to design good primer or probes for microRNA profiling.
Slide 28 On top of that, a lot of microRNAs actually show very high sequence
similarities. People usually use the let‐7 as an example to show how similar they are. But that’s not the only case. There are plenty of other cases. For instance in here, we show this family, 302 microRNA family. It has four members, a, b, c, and d. This particular family plays a very important role in stem cell development ‐‐ embryonic stem cell development.
And you can see the sequence conservation among these four members
is extremely high. There’s only one nucleotide difference among all of them. And when you look at the human models today, they are exactly the same sequence except 302c. So, that creates some problems on how we actually measure specific microRNA.
Slide 29
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The next problem is when we look at result from next generation sequencing, we and many other people actually realized that a lot of microRNas actually posses end region diversity.
Here, I’ll just show you one example, miR‐451. This particular example,
they have fairly consistent 5’ end, but at their 3’ end they show great variabilities. The one marked in red actually is the sequence to this in the database. So, this end region diversity can also contribute to issues associated with measurement and potentially may also affect the function of microRNAs.
Slide 30 So, if you compare different platforms, microarray and qPCR platforms
have the advantages on cost as well as its throughput. However, next generation sequencing platform actually provides unprecedented depths of coverage on microRNA as well as provides you an opportunity to identify new microRNAs. However, next generation sequencing suffers from high cost, low throughput, lengthy sample preparation procedure, and also, you have to deal with huge data file.
Slide 31 There are two types of microRNAs based on its genomic location. One
type of them actually is located in the intronic region of a messenger RNA. We call it intronic microRNA. The other type is located between genes so we called it intergenic microRNAs.
So, if you look at intronic microRNAs, you would expect the expression
profile of the intronic microRNA and its hosting gene should be very similar. And, indeed, in the case on your left‐hand side, these two are almost identical. But in the same model system, we are looking at on your right‐hand side, this would be miR‐30e, they are totally opposite from its hosting genes.
Slide 32 For intergenic microRNAs, if you look at individual microRNAs in these
microRNA clusters, again, you would expect their expression profile should be similar. And, indeed, in the same model system we looked at, the miR‐144 and the miR‐451 on your left‐hand side, they show identical expression profile. The distance between these two microRNAs is about 500 base pair.
But if you take the same example and look on the right‐hand side, these
are two different microRNA in the same model system. Again, their distance is about 500 nucleotides. They show totally opposite expression profile. So, that tells us there is a complex regulatory mechanism involved in microRNA maturation process.
Slide 33
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So, how to identify microRNA’s function? There are two major processes you can ‐‐ people usually do. One is through its interacting targets. People can clone ‐‐ you can clone your gene of interest, usually the 3’ UTR region, into a reporter. So then, you can use the reporter’s activity to determine or as a measurement of microRNA and messenger RNA interaction.
The problem for this is you assume that a microRNA and a messenger
RNA interaction happened at its 3’ end. The second problem is there are too many predicted targets to choose from. So, you have to just pick one or two or a bunch of them. You won’t be able to do all of them.
[0:20:13] The other approach is to use pull‐down. You can pull down microRNA and
the messenger RNA to look at all possible interacting partners. But, again, here you will get a huge list of messenger RNA for any particular microRNA.
The second approach to assess microRNA function is through a functional
screen. By manipulating specific microRNA levels, you can over express a particular microRNA or you can knock down a particular microRNA in a biological system. The advantage for this is you have a very good readout and you can screen a particular phenotype. But, again, if you look at the gene expression level when you manipulate just one single microRNA, you’d probably find many, many different genes being affected by a particular microRNA.
Slide 34 So, in summary, it’s very difficult for us to accurately measure the level of
microRNA. And also, it is a challenge to integrate microRNA data to existing dataset. But nevertheless, this also provides us a great opportunity for us to understand biology.
Slide 35 At the end, I want to thank people doing the work, my collaborators at
different places, and also generous funding from Battelle, University of Luxembourg, and Department of Defense. Thank you.
Sean Sanders: Great. Thank you so much, Dr. Wang. Slide 36 Our final speaker for today is Dr. Neil Kubica. Dr. Kubica completed his
undergraduate degree in biology at James Madison University in Virginia. Next, he pursued his graduate training at The Pennsylvania State University, first at the University Park main campus and then at the College of Medicine in Hershey. Dr. Kubica then joined the Department of Cell Biology at Harvard Medical School where the focus of his research program is on the reciprocal relationship between the mammalian target
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of rapamycin complex 1 and cell signaling pathway and microRNAs in models of human cancer. Dr. Kubica has co‐founded several organizations related to microRNA research including the New England RNA Data Club ‐‐ the mission of which is to allow for the dissemination of recent developments in RNA biology and facilitate interactions between academia and industry scientists in the field. More recently, Dr. Kubica co‐founded the miRNA Screeners Consortium, a group of academic laboratories and industry partners, which has established high throughput miRNA functional screening capabilities at Harvard Medical School.
Welcome, Dr. Kubica. Dr. Neil Kubica: Thank you very much, Sean. I’d like to thank Science AAAS and our
sponsor, Exiqon for giving us the opportunity to speak about our microRNA research programs.
Kai set me up very nicely and introduced the concept of high throughput
microRNA functional screening, and that’s what I’ll be talking about today.
Slide 37 So, I’m actually a postdoctoral fellow in John Blenis’ Laboratory at
Harvard Medical School. In the Blenis Lab, what we’re interested in is understanding how oncogenic cell signaling pathways contribute to malignancy. And the question that we want to answer with our screening is can microRNAs repress oncogenic cell signaling?
We can imagine at least two ways in which this might happen. First, you
can imagine transfecting a microRNA mimic or a microRNA gain‐of‐function reagent that targets positive regulators of the pathway, repressing their expression, and shutting the pathway off. You can also imagine a scenario in which a microRNA that’s highly expressed in the tumor cell is targeting negative regulators of the pathway. So, if we inhibit this microRNA, again, the end result would be a repression in tumorigenic signaling.
Slide 38 We thought a functional screen would be a good way to identify some of
these candidates, but the only problem was that we didn’t have local access to these libraries. We didn’t know which libraries worked the best. And to purchase many of the libraries is quite expensive for an individual laboratory.
So, to overcome some of these barriers, we worked with our local
screening facility, The Institute of Chemistry and Cell Biology at
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Longwood, or the ICCB‐L. So, the ICCB‐L does small molecule siRNA screening that facilitated many high throughput genome‐wide siRNA screens both for members of the Harvard community and external laboratories.
So, working with the ICCB‐L and the director Caroline Shamu, we
assembled the microRNA Screeners Consortium. So, the ICCB is the central player here providing the infrastructure and expertise for screening, but we also have seven independent academic laboratories. Each of these labs has their own biological interests, but we all share the common desire to screen microRNA gain‐of‐function and loss‐of‐function libraries.
So, as a group, we did a shared purchase of these libraries, which made
the barrier for entry low for every individual lab relative to a lab purchasing all of these libraries by themselves.
[0:24:59] Slide 39 So, we’re currently evaluating several libraries. We have two gain‐of‐
function libraries, one from Ambion and one from Qiagen. And we have two loss‐of‐function libraries, one from our sponsor Exiqon and one that’s also from Qiagen. All of these libraries are based on the miRBase Release 13 that came out in March of 2009. So, we’re pretty current; although, the database moves quickly. [Laughs]
Slide 40 So, now that we had access to the libraries, we had to think about how to
optimize our screen. And I’ve just listed in this slide a number of things that you might want to consider in your optimization process and things that we considered in ours.
Because of the time constraints today, I’m only going to talk about a few
of these things. And the first one I’d like to talk about is assay endpoint validation. So, you may have an assay that works well for your biological question of interest, but you really need to scale that down into the screening platform, in our case 384‐well plates, that performs the same way. So, I’ll show data in a moment where we’ve used a well‐characterized small molecule inhibitor of our pathway of interest to validate our assay.
Secondly, to screen microRNA libraries, we need to be able to transfect
these reagents into the cells. And so, I’m going to talk a little bit about transfection reagent optimization, and I’ll show you some data. We’ve used a panel of ten lipid and amine based transfection reagents to see what works best in our cell line. And, I think, the main take‐home point
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here is that every cell line is different and you really have to optimize both the endpoint assay and the transfection in your cell line of interest.
This also goes for the selection of appropriate positive controls and
negative controls. I’m going to focus on positive controls on my talk today because we ran into a problem when we screened because there weren’t any known microRNAs that affected the pathway. And so, to overcome this problem, what we did was we used siRNA reagents as surrogates for microRNA reagents in our library. And we think this is a very reasonable strategy because these reagents have many similar properties including their size and their chemical charge properties, etc.
Finally, I’ll show you some data where we’ve integrated all of these
optimization parameters and screened a couple of the plates from one of our libraries.
Slide 41 Okay. So, this is a typical workflow for that assay validation experiment
that I referred to in the last slide where we’ve used a small molecule inhibitor of our pathway. So, typically, the way this works is we plate cells in a 384‐well plate using a liquid handling robot available to us in the ICCB‐L. We allowed the cells to bind to the plate overnight and then we treat them with either a vehicle control or this drug inhibitor that I have referred to before. We then fixed, permeabilized, and blocked the cells three hours after drug treatment, and this is very similar to a standard immunofluorescence assay except that it occurs in a 384‐well plate. We then incubate with a phospho‐specific primary antibody that is a bona fide biomarker of our pathway of interest. We incubate overnight with a primary antibody and then we stain with an Alexa‐488 fluorescently labeled secondary antibody. In the final wash steps, we also counterstained the DNA with propidium iodide, and then we imaged and quantified the plates using the Acumen eX3 Microplate Cytometer from TTP LabTech.
Slide 42 Okay. So, this is an example of the results of these experiments. And
what the Acumen does is it scans the entire well. And then using a predetermined threshold, it scores each of the cells as active or inactive. So, the pseudocolor in green and the vehicle, well you can see a lot of the cells are active. And when we treat with a drug, you can see the cells in red and those are below the threshold, they’re inactive. We can then express this is a percent of the number of cells in the well active and get a single value for each well.
Okay. So I’ve shown a heat map on the bottom left and a scatter plot on
the right. And what we did to validate our assay was calculate a Z
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statistic. And so, this considers the difference in the mean between your positive and negative control, and also the standard deviations within those observations.
So, for those who are unfamiliar, a value of 1 would be a theoretically
perfect assay. And usually, something that has a value greater than 0.5 is used in high throughput screening and indicates a robust endpoint assay.
So, we’re doing pretty well here with our small molecule, what about
with our siRNA and microRNA reagents? Slide 43 So, here the protocol is just a little different. We’ve performed a reverse
transfection of the cells. Meaning that we make our transfection complexes, put them into the 384‐well plates and then plate the cells on top of that transfection mix so that the siRNA or microRNA reagent enters the cell. We typically change out the media 24 hours later to reduce toxicity. And then, typically 72 hours after transfection, we perform our endpoint assay just as I have described it to you before.
So, what I’m going to show you is using this workflow, we have decided
which lipid or amine transfection reagent works best for us by looking at a panel of 10 potential reagents. And in these experiments, we’ve used, as I said, an siRNA positive control that knocks down the kinase immediately upstream of our biomarker of interest.
Slide 44 Okay. So, the first thing we wanted to look at was transfection efficiency
and there are many different ways to look at efficiency of transfection. But ultimately, what we care about is a detectable change in our endpoint assay of interest. So, I’m showing on the left‐hand, a graph, the percent active value from the Acumen. So, as this goes down, our pathway is being shut off. And we compared a non‐targeting control negative siRNA to the positive siRNA control that I described to you before. And I think what you can see right away is that the reagent 1 and reagent 2, as I’ve listed them here on the x axis, give us the best knockdown in terms of turning off of our pathway of interest.
[0:30:16] In addition to looking at efficiency, we also wanted to look at reagent
toxicity. And even though reagent 2 gives us the best knockdown, we also see that it causes a great reduction in cell viability on the graph on the right. So, here, we’re looking at cell number. We’re looking at an untransfected versus a mock transfected well. So, reagent 2 is quite toxic and so we eliminated that from further consideration. But reagent 1, which also had a good efficiency, also resulted in no discernible cell
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toxicity and so we selected that for our screen and that just happened to be Lipofectamine RNAiMAX from Invitrogen. For your cell line of interest, you’ll have to see what works best for you.
Slide 45 So, next we wanted to consider our siRNA positive control a little bit
more even though it was a nice rational strategy to knock down that kinase that was immediately upstream of our biomarker. We wanted to ensure that that was the best out of a large number of potential possibilities. And so, one really nice thing about the pathway driven approach is we know many things about this pathway.
And so, what I’m showing here is non‐targeting controls and negative
versus 21 potential positive controls targeting knockdown of positive players in the pathway. So, you can see that treatment P and U give us the best knockdown both in serum‐containing and serum‐starved conditions.
Next, we went on to do a follow‐up experiment where we played at
various densities of cells and used siRNA treatment U alone, siRNA treatment P alone, or the combination of those reagents and created a matrix of Z scores. And what we’ve selected is a plating density of 800 cells per well. And we’re going to use the treatment U, which is our immediate upstream kinase, and also the combination of the two reagents as our positive controls on the plate. And you can see that very similar to when we used the small molecule inhibitor, we’re getting Z scores in the high 0.7 to low 0.8 range, which indicate a very robust assay.
Slide 46 So, now we’re going to put all of those parameters together and I
thought it might be useful to show you how we lay out our library plates. The first thing you’ll notice is that the outer two rows and columns are completely empty, and this is to avoid the very well described edge effects that we often see in high throughput functional screening. The microRNA library elements that are in each library are in yellow here and in columns 11 through 14, we show our various siRNA or microRNA negative and positive controls.
And you see that we have an N of eight. So, we have a lot of replication
for our controls on these plates. And we can get away with that because there aren’t so many microRNAs relative to say a whole genome siRNA screen. So, we have a lot of room on these plates.
So, we can screen an entire library in five 384‐well plates and with three
libraries, we can do that in triplicate and two conditions, which we’re
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going to use in about 90 384‐well plates. And that’ll take us about eight weeks of screening, it’s not too bad.
Slide 47 Okay. So, these are the real results from the first two library plates from
one of our gain‐of‐function libraries. What you can see is our percent active ‐‐ the average percent active between the triplicates on the y axis. And you can see the plates and the well positions on the x axis.
So, in the dark blue, you have the non‐targeting siRNa. In the yellow, you
have the mimic negative control. It doesn’t affect the assay very much. In red, you have our siRNA positive controls. They really inhibit the pathway. And in light blue, you see the various microRNA elements. And we’re really encouraged in this first run to see that there are four reagents here that give us almost as penetrant of a knockdown of the pathway as our positive controls. So, it’s a good sign for the future.
Slide 48 So, just quickly, take‐home messages. I think the Consortium Model is a
really useful one for academic laboratories to gain access to these reagents, sharing cost and expertise. I’ve given you a couple of optimization parameters that you might want to consider for your own assays. And finally, if you’re already up and running doing high throughput siRNA screening, it’s a pretty easy transition to microRNAs.
Slide 49 So, just very quickly, I want to thank my mentor John Blenis for
supporting these projects, Greg Hoffman, our post doc in the lab, and Janie Zhang, a very talented undergraduate student from Harvard University who helped me execute the projects. Caroline Shamu who helped assemble the microRNA Screeners Consortium. Sean Johnston who did all the library reformatting and really helps us with every transfection. So, thanks to Sean and all of our industry partners. I want to point out Ben Schenker particularly because we actually don’t have the Acumen instrument locally and Ben’s been a saint in letting us come and use his instrument over in Cambridge. So, thanks to him.
And that’s going to be all. Thanks, Sean. Sean Sanders: Great. Thanks Dr. Kubica. And thank you all for the excellent
presentations. Slide 50 And we’re now going to move on to the Q&A portion of the webinar. Just
a reminder to everyone that you can submit your questions by simply typing them into the ask‐a‐question box and clicking the submit button.
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So, our first question ‐‐ actually, I had one all lined up and Dr. Wang answered it for me so…
[Laughter] So, I’m going to go on to the next one. I’ve actually had a lot of questions
about RNA isolation so I’m going to put this out to you. One of the questions asked, in your experience what is the best total RNA isolation method or protocol for retaining the small RNA fraction and also for obtaining high levels of purity (without organic contamination). So, why don’t we start with Dr. Kubica at the end.
[0:35:07] Dr. Neil Kubica: Yeah, sure. Well, I can tell you what we use. There are obviously many
available strategies on the market and I think many of them are successful. So, there’s no magic here. We use Ambion’s mirVna RNA isolation kit. It does have a phenol step, but then there’s a column that we clean samples over, and we think it results in relatively high purity.
I think no matter what method you use, what you want to consider most
carefully is that you’re actually precipitating a small RNA fraction. So, one thing that people may not be aware of is that using kind of a traditional, say TRIzol or phenol‐chloroform extraction, you’re typically using like 70% ethanol concentration. And those ethanol concentrations are great for miRNAs and larger RNAs, but they lose a lot of the small RNA fraction. And so, the solution is just to increase the concentration of ethanol to say 80% or more percent and then you really get very efficacious precipitation of the small RNAs. And you can see that by running a 1% agarose gel or some other QC tool.
Sean Sanders: Great. Dr. Wang? Dr. Kai Wang: I just want to call on Neil’s comment. Most of the commercial kits
actually works very well. The key is just follow the protocol. Don’t make any shortcuts.
[Laughter] Sean Sanders: Okay. Dr. Nelson? Dr. Peter Nelson: We’d looked at this in the brain comparing a number of different kits in a
paper we published in BBA a couple of years ago. And we found that there actually were differences that were surprising to us between the different kits and the different methods. And the one that was most reproducible and the one that seemed to most correlate with the total
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RNA was the TRIzol LS kit. That was the one that worked best in our hands.
And the extra, when we did an extra overnight freezing ‘cause we
thought that that would actually enrich our ‐‐ there was a mild slight increased yield for the small RNAs, but it wasn’t that much. But it did seem that there was one better and the one that was better in our hands was the TRIzol LS kit.
Dr. Neil Kubica: I mean, I think one theme we’ll see in a lot of the answers to these
questions is that you need a solution that works for your model system and there isn’t one answer out there.
Dr. Peter Nelson: That’s right. Dr. Neil Kubica: So, you know, Peter works with the brain, I work with tumor cells in
culture and those are very different systems. And, you know, there may be very different requirements in terms of what you need to do. So, you need to optimize everything that you do. We’d love to give you a magic bullet, but it doesn’t exist just like in other areas of research.
Dr. Peter Nelson: But it would be great if more people published this type of technical data
because there needs to be more out there to give people an idea of what is ‐‐ and these are things that ‐‐ particularly the negative data does not get published. But we need to see those data. I think as a field, there needs to be more acknowledgement of both the pitfalls, but also just how these different technical kits and methods work.
Sean Sanders: Uh‐hum. Okay. So, another technical question then, asking for the best
method for both in vivo and in vitro transfection using microRNAs. Dr. Neil Kubica: Yes. So, maybe I talked the most about that. And, you know, again, the
answer is maybe painfully disappointing to some in our audience. But, you know, the answer is that just like in siRNA or transfecting a plasmid or any other reagent, really you need to optimize for your cell line of interest.
And, you know, we’ve done these transfection panels like the one I
showed you for high throughput screening. And if we look at say a HeLa cell versus the cells we’re working in, we get completely different answers. And so, you really need to optimize for your particular model system of interest.
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Dr. Peter Nelson: And the in vivo system, I think, is a very open and interesting question that has been not addressed very much.
Dr. Kai Wang: Well, people can follow siRNA model and people did in vivo experiment
with that. But I don’t know how much of that can be translated into microRNA directly.
Sean Sanders: Uh‐hum. Dr. Kai Wang: That’s still an open question. People did microRNA knockdown, for
instance 122 knockdown in vivo. Sean Sanders: Right. Dr. Kai Wang: But that’s a particular case, because that’s liver and the most of
microRNA goes into liver in that case. Dr. Neil Kubica: Yes. It seems like the liver is the best model for in vivo transfection now,
but when you want to work in other areas it becomes more difficult. And maybe some viral approaches that have been used in the brain before for siRNA may also be useful in addressing other tissue types besides the liver.
Sean Sanders: Okay. This question has just come in. Can you please address the problem of
microRNA knockdown experiments that we were just talking about in vitro where a whole “family” of microRNAs might potentially need to be knocked down to see a phenotype. How are investigators best dealing with this? [Laughs]
Dr. Neil Kubica: That’s a great question. I don’t know. I mean, my opinion is that we don’t
really have the degree of sophistication to partition between some of the microRNA families, in some cases, I think. In certain instances, we do better than others. But the more similar the sequence is, the more crossovers there are going to be between reagents.
And so, this kind of brings up an interesting point because, you know,
many people have viewed these families as functionally redundant and I think that that’s not always the case. And a lot more work needs to be done in the field. So, I think you’re ‐‐ you have a great potential research project there.
[Laughter]
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[0:40:05] Dr. Peter Nelson: But these issues pertain both to knocking in and knocking out. And
basically, I think, you have to bite the bullet and do all the controls with regard to both in terms making ‐‐ putting all the pair logs in. Because you’d be surprised, they sometimes do very different things and you may expect given the canonical things that we think about how the binding occurs.
But let me make a special point about the inhibitors. In our hands, I’ve
seen a lot of papers come through and they rarely have a lot of controls with the inhibitors. And we’ve done a lot of controls with inhibitors. We’ve used from different companies. And I can say that the things that inhibitors do are different than that which is currently known and they’re not as clean. And not only are they not as specific ‐‐ you’d mentioned the point of specificity.
Sean Sanders: Uh‐hum. Dr. Peter Nelson: But they don’t actually work all that well. And so, that is something that, I
think, needs to be acknowledged at some point. It’s not something that is sexy to put in your paper or your grant or anything else, but it’s a very important point that the microRNA inhibitors from our functional assays when we do the reporter constructs are much more disappointing. And I’m sure that there are many that work great, but there are definitely some that just basically don’t do the work at hand so…
Dr. Neil Kubica: We had the same exact experience that Peter has had. The mimics or
gain‐a‐function reagents seem to give very clean results. And the inhibitors give very different results based on the inhibitor that you’re using and it’s a much noisier, more variable assay.
Sean Sanders: Great. I have a couple of questions for you Dr. Nelson. One is you mentioned
microRNAs are degraded. What is the half‐life in the cell and… Actually, we’ll stay with that one for the moment and I’ll move into the next one once you’ve answered.
Dr. Peter Nelson: Okay. This is a question that is essentially important to many types of
research. And it’s one that surprisingly little has been done considering how important it is both to the life of a microRNA, but also the function of a microRNA. A function of anything is very closely related to how it
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gets made, yes. How it works, yes. But also, how and when it gets degraded.
And so that is ‐‐ and for terms of microRNAs, this has been extremely
minimally studied. There’s a really nice study recently in this year from Walter Lukiw’s group. He’s one of the people who’s done some of the mostly, I think, important work both in Alzheimer’s disease and in the brain in general with microRNAs. He’s down in Louisiana. And he found that important to note that different microRNAs degrade at different rates in the brain after death. And that is based upon his studies looking at different PMI intervals.
And so, it’s one thing to say that they degrade at this time, it’s another
thing to say, hey, this one degrades at this time, this one degrades ‐‐ it takes a different time. Maybe it’s different compartments, maybe it’s different pathways they’re involved in, and maybe it’s different types of cells that are being degraded at different rates in the brain. But the point is that ‐‐ like many of these other things, I think, it’s a recurrent theme here. [Laughs] So, we’re in early days and we don’t know.
Sean Sanders: Uh‐hum. Dr. Peter Nelson: It’s worse though to act like you know when you don’t know. Sean Sanders: Right. Dr. Peter Nelson: And I think that it’s important to recognize that, you know, it may happen
in different rates. Dr. Kai Wang: Yeah. But that’s different on live cell. You know, live cell how they turn
over we actually don’t know. Dr. Peter Nelson: That’s right. Dr. Kai Wang: Yeah. Dr. Neil Kubica: It’s a very open area, I think, in terms of turnover and degradation of
microRNAs and living cells, I agree. Sean Sanders: Okay. So, the other question for you Dr. Nelson was how do you establish
the causative mechanisms in the pathogenesis of Alzheimer’s using microRNA profiling?
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Dr. Peter Nelson: The causative mechanisms of Alzheimer’s disease are something that everybody has given a lot of thought to and we still don’t know what are. So, I don’t know. The first thing you can do though is you can say what microRNAs are perturbed and what cell types at what stages of the disease and what parts of the brain. ‘Cause all of those things are very important in terms of understanding the disease pathogenesis. And so, we’re in early days and we’re describing the microRNAs that are perturbed in the various stages of the degeneration of the cell, and then trying to develop testable hypotheses to say how can you manipulate those microRNAs to possibly help attenuate the development of the pathology. And, I think, we’re coming along a little bit in that way.
Sean Sanders: Okay. Excellent. Next question, is it possible that the same microRNA can regulate two
different mRNAs having opposite function, or microRNA target genes in related pathways having the same function?
Dr. Neil Kubica: So, I’m not sure if I completely understand the question, but, you know,
certainly, when we’ve done things like in silico predictions of targets, which are of course we don’t know for sure whether those are bona fide targets in our model system of interest. That requires experimentation. But often what we’ll see is genes within a particular cellular process, let’s say, cell proliferation, we’ll see some things that get knocked down that are positive regulators of that process and some that are negative regulators. And the way we view it is that the important outcome is the net balance of those things.
[0:45:15] And so, maybe you’re knocking down eight positive regulators and only
two negative regulators, so the net outcome of that process could potentially be repression of the cell cycle. So, that’s kind of a very simple way to think and we need to go in and interrogate those types of ideas with experimentation so…
Dr. Kai Wang: Yeah, I agree. It is possible, we just don’t know. [Laughs] Sean Sanders: At this stage, anything is possible. Dr. Neil Kubica: Well, so it’s difficult. I mean, microRNAs are targeting, you know,
hundreds of transcripts and so there’s a kind of systems level analysis that has to occur. And, I think, Kai is working on a lot of that out in Washington. So, there are a lot of people thinking about it. [Laughs] But it’s challenging.
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Sean Sanders: Okay. Dr. Peter Nelson: I mean, all that it has to have is one system where it happens that way
and clearly just in different systems, they’d behave in different ways. Sean Sanders: Yeah. Dr. Peter Nelson: There’s no single dogmatic way that a microRNA can work. It has a very
flexible versatile system perfecting not just translation but probably transcription and other processes of the cell.
Sean Sanders: Okay. The next question comes back to controls that we were just talking
about. For miRNA experimental controls, we find scrambled sequences or irrelevant siRNAs just sometimes have unexpected results. Would mutating the seed region by a couple of bases be a good solution? Maybe we’ll start with ‐‐
Dr. Neil Kubica: I think Peter has done the most work with making controls for these
assays. [Laughter] Dr. Peter Nelson: I like your control. Sean Sanders: Dr. Wang? Dr. Kai Wang: Well, I think, we can look at this at several different levels. If you look at
control at measurement, then there isn’t any good control. If you look at cells, doing PCR or array, people will use housekeeping genes for messenger RNA sometimes even for microRNAs. But a lot of the housekeeping gene actually changes in the experimental conditions.
Sean Sanders: Uh‐hum. Dr. Kai Wang: People use small non‐coding RNA for control also, but those also change.
So, on the measurement side, it’s very difficult. People will do spiking. Spiking won’t control the biology. It only controls the isolation process. So, on the measurement side, there isn’t any good control for microRNA.
For a functional side, yes, I presume the question probably more deals
with functional side, when you change the seed region, what will happen. Yes, you can try that. But remember, when you compare all the
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microRNA sequences, sometimes the seed region only changed endogenously. They have different microRNA species. They show very similar sequences, but the seed region is slightly different.
So, you might want to look into those and also look at different species.
Because the seed region actually is not conserved, it’s throughout evolution. So, that might give you some clue. So, in summary, I actually don’t know what’s the best control for both measurement as well as the functional side of microRNA.
Dr. Peter Nelson: I can sort of take that just from the perspective of in situ hybridization.
It’s very important to have some controls when you’re doing an in situ labeling reaction. That is pretty well worked out how you can do that for antibodies, immunohistochemistry. It’s okay in terms of mRNA related in situ hybridization, but it’s very, very difficult for microRNA in situ hybridization to have control sequences. If you have a negative control, that doesn’t necessarily indicate that your sequence is actually real. And it’s biologically important what you’re seeing.
And so, my strategy is to basically try to get as many positive controls for
each tissue type that stain different known cellular groups in that tissue type. So, for example, you have microRNA that looks at neurons, microRNA b that looks at glial cells, or whatever. So, that now you can maybe use your microRNA c probe to see what it recognizes in the same conditions that works for the other ones. It’s an imperfect system. I don’t think there’s any way you can make it so that you know that your particular microRNA probe is localized specifically and only to your particular target. You can try all sorts of ways of blocking it and absorbing it, you’re still not going to get to that answer when the rubber meets road in your hyb chamber.
Sean Sanders: Uh‐hum. A question here asking if you could please talk a little bit more about
microRNA libraries, for example, production and content. Dr. Kubica? [0:49:55] Dr. Neil Kubica: Right. So, you know, one of the reasons why we’ve kind of waited to this
stage of the game to purchase libraries. And we purchased all of our libraries commercially, we didn’t not fabricate them ourselves; although, in principle, you could synthesize the sequences and make your own library. One of the reasons why we waited is because microRNA identification has been a very rapidly moving field over the past several years. I hope you guys would agree.
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But, you know, the number of microRNAs in the human system has gone
from just a couple of hundred up to about a thousand now. And we’re starting to see with each subsequent release of miRbase kind of diminishing returns in terms of the number of new microRNAs that we’re getting back. And so, we’re kind of getting to a point where not much strange happens that is unexpected. We think we’ve identified the majority of the common microRNAs. And so, purchasing a library at this point, you can, I think, be pretty comfortable that you’re going to get a lot of the content that’s out there. You may miss some things that are very cell type or developmentally specific, but by and large, you’re going to get a lot of the common microRNAs.
And so, if you’re thinking about investing in a library, and it’s an
expensive investment, I think, now the field is getting to a state of maturity where you can start to feel more comfortable with that investment.
Sean Sanders: Okay. Dr. Peter Nelson: I’ll say that ‐‐ interpret it in terms of the brain, especially mature brain. If
you’re talking about a very focused embryological stage, you may want to get your own library. But in terms of the brain, if you just look at the intensity of hybridization, starting with the early miRs let‐7 and miR‐124 down to the last, you know, hundreds and hundreds, there’s a hundredfold average greater expression of the earlier cloned ones simply because those really are the ones that are most represented in those tissues.
Sean Sanders: Okay. The next question is about normalization of microRNA expression in
microRNA qPCR arrays. Another tough one. Dr. Wang, do you like to a stab?
Dr. Kai Wang: Yeah. We’ve been struggling on this issue for almost two years. In the
beginning, we thought, just like many people think, that U6 or one of the snoR would be a good control. And it turns out, they actually changed from experimental condition to experimental conditions.
Sean Sanders: Uh‐hum.
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Dr. Kai Wang: So, I said this earlier, I want to say it again. We actually don’t know what is the best control for any biological system that we encountered. That including some in vivo model as well as in vitro cell works. But nevertheless, I believe there are possible controls for this.
A couple of years ago, I think, just two years ago, Ambion published a
paper. They surveyed a whole bunch of tissue looking at microRNA expressions. And they identified a subset of microRNAs showing similar expression profile among all different tissues and also on several different conditions.
So, those could be potential candidates for internal control. But that
remain to be ‐‐ more work need to worked on that. Sean Sanders: Okay. Dr. Kubica, anything to add? Dr. Neil Kubica: Yeah. And now I just want to reiterate everything that Kai said. I think
that’s right. I mean in our experiments, we’re only looking at a small set of cell lines and a small set of treatment conditions. And so, we can find small nucleolar RNAs that serve as good normalization controls in that very specific model system. But I’ve seen a lot of people who have tried to do things comparing different cell line subtypes or different tissue subtypes and they tried to find one unifying normalization control, and that’s an uphill battle.
Dr. Peter Nelson: I think it’s always an uphill battle. I think, you could have plenty of ‐‐ you
just shouldn’t get married to anything. [Laughs] U6, 5S no matter what it is, I don’t care ‐‐ I promise you that those microRNAs change under some circumstances. It’s not biological sense to have something that’s just expressed at the same rate in all tissues.
And it’s not my experience at all that’s the way it works. You just ‐‐ you
have to use some things, but you have to go in with your eyes wide open because there’s definitely going to be changes and ‐‐ I mean you were talking earlier... Off camera, we were talking about how cancer cells frequently have a low or suppressed overall microRNA. And there has to be a way of figuring that out in the cellular level that there’s a generalized suppression.
On the other hand, I think it is very powerful to just normalize, to just
make everything be a total and then just say that something is now disproportionately expressed. I think it’s an important thing to know even though you’re ignoring the overall expression differences that had
26
happened. ‘Cause I think that those are both independently important to know.
Dr. Kai Wang: Yeah. I just want to make one last comment. Normalization actually is not
very important in certain experiments. So, you probably don’t need to bog down with how am I going to normalize and so on. Because you can look at sample as a whole by itself. So, you can compare different microRNAs in that sample, within that sample. So, you have a ratio of, for instance, two different microRNAs. Then you compare the same ratio on a different sample. So, that actually provides you in itself internal control. So, look at ratio instead of looking at the levels among different samples.
[0:55:27] Sean Sanders: Great. We are almost at the hour, but we’re going to go a little bit over ‘cause
we started late. So, I have a couple more questions I’m going to throw at you. There are several target prediction algorithms for microRNAs that have ‐‐ with very little overlap among their results. How can we find a reliable source of target prediction? Okay, who’s going to take that one? [Laughs]
Dr. Peter Nelson: I’ll start that. Sean Sanders: Okay. Dr. Peter Nelson: I don’t ‐‐ and you, we all have experience with this regard. But anytime
you have three things that are telling you something different, there’s a certain obvious fact that they’re wrong. I mean, one or all of them are somewhat wrong. There’s overlap, which is good. But I think that a lot of the assumptions that have been underlying these computational methods are going to be massively revised over the next decade. So, people at this time, I think, would not be well served to be slavishly dependent upon them.
Sean Sanders: Uh‐hum. Okay. Dr. Wang? Dr. Kai Wang: Well, we tried to attack this question – well, two years ago, we thought
this should be something simple. And we actually constructed a website called miRPortal. So, if you’re interested, you can take a look. We combined different algorithms, tried to find common, trying to find union of all these prediction. But at the end, we basically gave up because there are so many inconsistency as the person mentioned.
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And also, it’s very difficult because if you look at how many targets we can predict, some of them, you have a couple of thousand and some of them you have zero. So, how to justify that, we don’t know. And if you are going to experiment, validate how it is, it is almost impossible. So, we basically gave up at this point on that.
Dr. Neil Kubica: Yeah, it’s very challenging. I mean the one paper I would ‐‐ is coming to
my mind was in Nature and it was a collaboration between Steve Gygi’sLab from our department and Dave Bartel’s Lab from the Whitehead Institute. And what they did was perform a proteomics approach to look at targeting at the protein level and then they compared that back to the predictions that came out of these various algorithms. And perhaps not surprisingly, although, certainly the data is real, TargetScan, which is the Bartel method, holds up quite well as do some of the other target prediction methodologies.
So, I think, there are kind of variability in the quality of the output that
you get from different algorithms, but no algorithm is perfect. And one of the things that Peter and I were talking about before the webinar is that there is a new paper that came out also from the Bartel Lab that showed in tumor cells for example, that you often have shortening of the 3’ UTR. And so, even though the computer is spitting out this predicted target, that may not be experimentally relevant anymore in your model system. And so, we always think about target prediction as a tool that always has to be iterated with experimentation.
Dr. Peter Nelson: I would say that those things worked with the proteomics and at the
miRNA level, but there’s ‐‐ certainly 30% or 40% of the variability is explained by this sort of dogma. And so, any dogma that’s 30% to 40% right, you naturally think also about what’s that 60% or 70% that’s off the camera.
Dr. Neil Kubica: I totally agree. I mean even in the best case scenarios, we’re getting right
20% to 30% agreement. And so, it says that we’re not doing very good, yet.
Sean Sanders: Uh‐hum. Dr. Peter Nelson: That’s not to say that they’re not good and valuable and important. It’s
just that we can’t hang our hat in papers that solely go on the computational method ‐‐ at this point are not state of the art.
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Dr. Neil Kubica: I think that we can’t stop thinking because there’s a tool available on the web to spit out some targets for us. We need to always be thinking about our experiments.
Sean Sanders: Okay. Great. So, we’re almost out of time, but briefly if I could ask you each where you
think the future of microRNA research is headed. Maybe, you know, your favorite area, but where do you think it’s going in the next five to ten years? So, let’s start at the end with Dr. Kubica.
Dr. Neil Kubica: Sure, start with me. [Laughter] That’s a big question, Sean. But actually, I think that ‐‐ the point that I’d
like to emphasize, which has been emphasized throughout the entire webinar, is that this field is really in its infancy. I mean, we’re only less than really a decade of a lot of research being put into this area. And so, I think that future is that there’s going to be a lot of surprises and there’s going to be a lot of changes in what we think of as the dogma, and Peter has pointed it out a number of times. You know, I think that this goes back to what we’re saying, as always be thinking. If you are a post doc or a graduate student out there, or a PI who’s moving into this area, think about something outside of the dogma. A lot of people are working on kind of what is obvious to be predicted, but there’s a lot to learn in the area and I think the future will be a big surprise. [Laughs]
Sean Sanders: Yeah. Dr. Wang? Dr. Kai Wang: Well, I think, we probably will see three different things happen to
microRNA. Of course, I’m probably old around this. One is you probably will see microRNA play a very important role in cell‐cell communication. So, not just within the cell, it’s between cells. Second is you will see microRNA actually play a very important role in diagnostic, replace some of the current protein based diagnostic. Third is you will see microRNA become therapeutic targets or become therapeutic itself. So, that’s three things I envision you will have.
[1:00:31] Dr. Neil Kubica: We hope so. [Laughter]
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Sean Sanders: Dr. Nelson? Dr. Peter Nelson: So, I really agree with, I think, what are very trenchant points. Both of you
‐‐ I think that they’re totally right around the money and you could stop right there. I think, that with regard to the nervous system. The special geometric uniqueness of the nervous system, I think, will lend itself to really interesting new possibilities in terms of localized translation control that will use non‐coding RNAs. And will expand our understanding of the machinery that we now associate with microRNAs working with a whole lot of other different types of non‐coding RNAs, in the context of localized translational control in the nervous system.
Sean Sanders: Yeah. Excellent. Well, thank you all very much. And we’ve unfortunately reached the end
of our broadcast once again. So, please join me in thanking our panelists for being with us today and for generously sharing their experience: Dr. Peter Nelson from the University of Kentucky, Dr. Kai Wang from the Institute for Systems Biology, and Dr. Neil Kubica from Harvard Medical School.
Thank you also to our viewers for your questions that you submitted. As
usual, there were significantly more than we could cover in just an hour so I’m sorry if we didn’t manage to answer yours.
Please go to the URL up in the bottom of your slide viewer now to learn
more about products related to today’s discussion. And look out for more webinars from Science available at www.sciencemag.org/webinar. This webinar will also be made available to view again as an on‐demand video within approximately 48 hours from now.
Please share your thoughts about the webinar with us at any time by
sending an email to the address now up in your slide viewer, [email protected].
Again, thank you to our panel and thank you to Exiqon for their kind
sponsorship of today’s educational seminar. Thank you. Great. Thank you very much. Dr. Kai Wang: Thank you. Sean Sanders: Thank you.
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Dr. Neil Kubica: Thanks, Sean. [1:02:26] End of Audio