New paper in eLife

December 19, 2014

Kinetic competition during the transcription cycle results in stochastic RNA processing

Matthew L FergusonValeria de TurrisMurali PalangatCarson C ChowDaniel R Larson

Abstract

Synthesis of mRNA in eukaryotes involves the coordinated action of many enzymatic processes, including initiation, elongation, splicing, and cleavage. Kinetic competition between these processes has been proposed to determine RNA fate, yet such coupling has never been observed in vivo on single transcripts. In this study, we use dual-color single-molecule RNA imaging in living human cells to construct a complete kinetic profile of transcription and splicing of the β-globin gene. We find that kinetic competition results in multiple competing pathways for pre-mRNA splicing. Splicing of the terminal intron occurs stochastically both before and after transcript release, indicating there is not a strict quality control checkpoint. The majority of pre-mRNAs are spliced after release, while diffusing away from the site of transcription. A single missense point mutation (S34F) in the essential splicing factor U2AF1 which occurs in human cancers perturbs this kinetic balance and defers splicing to occur entirely post-release.

DOI: http://dx.doi.org/10.7554/eLife.03939.001

Selection of the week

December 19, 2014

How about the Hallelujah Chorus from Handel’s Messiah?  Here is the Royal Choral Society.  Happy Holidays!

 

Ideas on CBC radio

December 18, 2014

 

One of the most intellectually stimulating radio shows (and podcasts) is Ideas with Paul Kennedy on CBC radio. It basically covers all topics. Many of the shows span several hour-long segments. One inspiring show I highly recommend is devoted to landscape architect Cornelia Hahn Oberlander. She was a pioneer in green and sustainable architecture. She is also still skiing at age 93!

Selection of the week

December 12, 2014

Adagio in G minor, attributed to the Italian Baroque composer Tomaso Albinoni, arranged and maybe written by twentieth century musicologist Remo Giazotto, and made famous by the film Gallipoli.

NIH Stadtman Investigator

December 7, 2014

The US National Institutes of Health is divided into an Extramural Program (EP), where scientists  in universities and research labs apply for grants, and an Intramural Program (IP), where investigators such as myself are provided with a budget to do research without having to write grants. Intramural Investigators are reviewed fairly rigorously every four years, which affects budgets for the next four years, but this is less stressful than trying to run a lab on NIH grants. This funding model difference is particularly salient in the face of budget cuts because for the IP a 10% cut is 10% cut whereas for the EP, it means that 10% fewer grants are funded. When a  lab cannot renew a grant, people lose their jobs. This problem is further exacerbated by medical schools loading up with “soft money” positions, where researchers must pay their own salaries from grants. The institutions also extract fairly large indirect costs from these grants, so in essence, the investigators write grants to both pay their salaries and fill university coffers. I often nervously joke that since the IP is about 10% of the NIH budget, an easy way to implement a 10% budget cut is to eliminate the IP.

However, I think there is value in having something like the IP where people have the financial security to take some risks. It is the closet thing we have these days to the old Bell Labs, where the transistor, information theory, C, and Unix were invented.  The IP has produced 18 Nobel Prizes and can be credited with breaking the genetic code (Marshall Nirenberg), the discovery of fluoride to prevent tooth decay, lithium for bipolar disorder, and vaccines against multiple diseases (see here for a list of past accomplishments). What the IP needs to ensure its survival is a more a rigorous and transparent procedure for entry into the IP where the EP participates. An IP position should be treated like a lifetime grant to which anyone at any stage in their career can apply. Not everyone may want to be here. Research groups are generally smaller and there are lots of rules and regulations to deal with, particularly for travel. But if someone just wants to close their door and do high risk high reward research, this is a pretty good place to be and they should get a shot at it.

The Stadtman Tenure-track Investigator program is a partial implementation of this idea. For the past five years, the IP has conducted institute-wide searches to identify young talent in a broad set of fields. I am co-chair of the Computational Biology search this year. We have invited five candidates to come to a “Stadtman Symposium”, which will be held tomorrow at NIH.  Details are here along with all the symposia. Candidates that strike the interest of individual scientific directors of the various institutes will be invited back for a more traditional interview. Most of the hires at NIH over the past five years have been through the Stadtman process. I think this has been a good idea and has brought some truly exceptional people to the IP. What I would do to make it even more transparent is to open up the search to people at all stages in the their career and to have EP people participate in the searches and eventual selection of the investigators.

 

 

Selection of the week

December 5, 2014

Joseph Haydn was one of the most prolific and prominent composers of the classical period.  Here is one of his string quartets played by the Casal Quartett.

Selection of the week

November 28, 2014

Since I’ve been kind of biased towards the violin recently, here is an interpretation of Dimitri Shostakovich’s Waltz No. 2 from the Jazz Suite No. 2 by the Sydney Youth Orchestra without strings.

Selection of the week

November 21, 2014

Here is violinist Itzhak Perlman playing Belgian composer Joseph-Hector Fiocco’s Allegro from the late Baroque period.  If you like speed, this is as fast as any rock guitar solo.

Race against the machine

November 11, 2014

One of my favourite museums is the National Palace Museum (Gu Gong) in Taipei, Taiwan. It houses part of the Chinese imperial collection, which was taken to Taiwan in 1948 during the Chinese civil war by Chiang Kai-shek. Beijing has its own version but Chiang took the good stuff. He wasn’t much of a leader or military mind but he did know good art. When I view the incredible objects in that museum and others, I am somewhat saddened that the skill and know-how required to make such beautiful things either no longer exists or is rapidly vanishing. This loss of skill is apparent just walking around American cities much less those of Europe and Asia. The stone masons that carved the wonderful details on the Wrigley Building in Chicago are all gone, which brings me to this moving story about passing the exceedingly stringent test to be a London cabbie (story here).

In order to be an official London black cab driver, you must know how to get between any two points in London in as efficient a manner as possible. Aspiring cabbies often take years to attain the mastery required to pass their test. Neural imaging has found that their hippocampus, where memories are thought to be formed, is larger than normal and it even gets larger as they study. The man profiled in the story quit his job and studied full-time for three years to pass! They’ll ride around London on a scooter memorizing every possible landmark that a person may ask to be dropped off at. Currently, cabbies can outperform GPS and Google Maps (I’ve been led astray many a time by Google Maps) but it’s only a matter of time. I hope that the cabbie tradition lives on after that day just as I hope that stone masons make a comeback.

Crawford Prize

November 8, 2014

The SIAM activity group on dynamical systems is seeking nominations for the J.D. Crawford Prize. J.D. was a marvelous applied mathematician/theoretical physicist who tragically died in his forties the day before I started my job at the University of Pittsburgh. The deadline is November 15. Thus far, we (I’m on the committee) haven’t received many nominations so the odds are good. So please give us more work and send in your nominations. The information for where to send it is here.

Selection of the week

November 7, 2014

Kurt Masur conducting the Leipzig Gewandhaus Orchestra in a rendition of the first movement of Nikolai Rimsky-Korsakov’s Scheherazade (The Sea and Sinbad’s Ship).

How does the cortex compute?

November 5, 2014

Gary Marcus, Adam Marblestone, and Thomas Dean have an opinion piece in Science this week challenging the notion of the “canonical cortical circuit”. They have a longer and open version here. Their claim is that the cortex is probably doing a variety of different computations, which they list in their longer paper. The piece has prompted responses by a number of people including Terry Sejnowski and Stephen Grossberg on the connectionist listserv (Check the November archive here).

What’s wrong with neuroscience

November 2, 2014

Here is a cute parable in Frontiers in Neuroscience from cognitive scientist Joshua Brown at Indiana Univeristy.  It mirrors a lot of what I’ve been saying for the past few years:

 

The tale of the neuroscientists and the computer:  Why mechanistic theory matters

http://journal.frontiersin.org/Journal/10.3389/fnins.2014.00349/full

A little over a decade ago, a biologist asked the question “Can a biologist fix a radio?” (Lazebnik, 2002). That question framed an amusing yet profound discussion of which methods are most appropriate to understand the inner workings of a system, such as a radio. For the engineer, the answer is straightforward: you trace out the transistors, resistors, capacitors etc., and then draw an electrical circuit diagram. At that point you have understood how the radio works and have sufficient information to reproduce its function. For the biologist, as Lazebnik suggests, the answer is more complicated. You first get a hundred radios, snip out one transistor in each, and observe what happens. Perhaps the radio will make a peculiar buzzing noise that is statistically significant across the population of radios, which indicates that the transistor is necessary to make the sound normal. Or perhaps we should snip out a resistor, and then homogenize it to find out the relative composition of silicon, carbon, etc. We might find that certain compositions correlate with louder volumes, for example, or that if we modify the composition, the radio volume decreases. In the end, we might draw a kind of neat box-and-arrow diagram, in which the antenna feeds to the circuit board, and the circuit board feeds to the speaker, and the microphone feeds to the recording circuit, and so on, based on these empirical studies. The only problem is that this does not actually show how the radio works, at least not in any way that would allow us to reproduce the function of the radio given the diagram. As Lazebnik argues, even though we could multiply experiments to add pieces of the diagram, we still won’t really understand how the radio works. To paraphrase Feynmann, if we cannot recreate it, then perhaps we have not understood it (Eliasmith and Trujillo, 2014; Hawking, 2001).

Lazebnik’s argument should not be construed to disparage biological research in general. There are abundant examples of how molecular biology has led to breakthroughs, including many if not all of the pharmaceuticals currently on the market. Likewise, research in psychology has provided countless insights that have led to useful interventions, for instance in cognitive behavioral therapy (Rothbaum et al., 2000). These are valuable ends in and of themselves. Still, are we missing greater breakthroughs by not asking the right questions that would illuminate the larger picture? Within the fields of systems, cognitive, and behavioral neuroscience in particular, I fear we are in danger of losing the meaning of the Question “how does it work”? As the saying goes, if you have a hammer, everything starts to look like a nail. Having been trained in engineering as well as neuroscience and psychology, I find all of the methods of these disciplines useful. Still, many researchers are especially well-trained in psychology, and so the research questions focus predominantly on understanding which brain regions carry out which psychological or cognitive functions, following the established paradigms of psychological research. This has resulted in the question being often reframed as “what brain regions are active during what psychological processes”, or the more sophisticated “what networks are active”, instead of “what mechanisms are necessary to reproduce the essential cognitive functions and activity patterns in the system.” To illustrate the significance of this difference, consider a computer. How does it work?

**The Tale**

Once upon a time, a group of neuroscientists happened upon a computer (Carandini, 2012). Not knowing how it worked, they each decided to find out how it sensed a variety of inputs and generated the sophisticated output seen on its display. The EEG researcher quickly went to work, putting an EEG cap on the motherboard and measuring voltages at various points all over it, including on the outer case for a reference point. She found that when the hard disk was accessed, the disk controller showed higher voltages on average, and especially more power in the higher frequency bands. When there was a lot of computation, a lot of activity was seen around the CPU. Furthermore, the CPU showed increased activity in a way that is time-locked to computational demands. “See here,” the researcher declared, “we now have a fairly temporally precise picture of which regions are active, and with what frequency spectra.” But has she really understood how the computer works?

Next, the enterprising physicist and cognitive neuroscientist came along. “We don’t have enough spatial resolution to see inside the computer,” they said. So they developed a new imaging technique by which activity can be measured, called the Metabolic Radiation Imaging (MRI) camera, which now measures the heat (infrared) given off by each part of the computer in the course of its operations. At first, they found simply that lots of math operations lead to heat given off by certain parts of the CPU, and that memory storage involved the RAM, and that file operations engaged the hard disk. A flurry of papers followed, showing that the CPU and other areas are activated by a variety of applications such as word-processing, speech recognition, game play, display updating, storing new memories, retrieving from memory, etc.

Eventually, the MRI researchers gained a crucial insight, namely that none of these components can be understood properly in isolation; they must understand the network. Now the field shifts, and they begin to look at interactions among regions. Before long, a series of high profile papers emerge showing that file access does not just involve the disks. It involves a network of regions including the CPU, the RAM, the disk controller, and the disk. They know this because when they experimentally increase the file access, all of these regions show correlated increases in activity. Next, they find that the CPU is a kind of hub region, because its activity at various times correlates with activity in other regions, such as the display adapter, the disk controller, the RAM, and the USB ports, depending on what task they require the computer to perform.

Next, one of the MRI researchers has the further insight to study the computer while it is idle. He finds that there is a network involving the CPU, the memory, and the hard disk, as (unbeknownst to them) the idle computer occasionally swaps virtual memory on and off of the disk and monitors its internal temperature. This resting network is slightly different across different computers in a way that correlates with their processor speed, memory capacity, etc., and thus it is possible to predict various capacities and properties of a given computer by measuring its activity pattern when idle. Another flurry of publications results. In this way, the neuroscientists continue to refine their understanding of the network interactions among parts of the computer. They can in fact use these developments to diagnose computer problems. After studying 25 normal computers and comparing them against 25 computers with broken disk controllers, they find that the connectivity between the CPU and the disk controller is reduced in those with broken disk controllers. This allows them to use MRI to diagnose other computers with broken disk controllers. They conclude that the disk controller plays a key role in mediating disk access, and this is confirmed with a statistical mediation analysis. Someone even develops the technique of Directional Trunk Imaging (DTI) to characterize the structure of the ribbon cables (fiber tract) from the disk controller to the hard disk, and the results match the functional correlations between the hard disk and disk controller. But for all this, have they really understood how the computer works?

The neurophysiologist spoke up. “Listen here”, he said. “You have found the larger patterns, but you don’t know what the individual circuits are doing.” He then probes individual circuit points within the computer, measuring the time course of the voltage. After meticulously advancing a very fine electrode in 10 micron increments through the hard material (dura mater) covering the CPU, he finds a voltage. The particular region shows brief “bursts” of positive voltage when the CPU is carrying out math operations. As this is the math co-processor unit (unbeknownst to the neurophysiologist), the particular circuit path is only active when a certain bit of a floating point representation is active. With careful observation, the neurophysiologist identifies this “cell” as responding stochastically when certain numbers are presented for computation. The cell therefore has a relatively broad but weak receptive field for certain numbers. Similar investigations of nearby regions of the CPU yield similar results, while antidromic stimulation reveals inputs from related number-representing regions. In the end, the neurophysiologist concludes that the cells in this particular CPU region have receptive fields that respond to different kinds of numbers, so this must be a number representation area.

Finally the neuropsychologist comes along. She argues (quite reasonably) that despite all of these findings of network interactions and voltage signals, we cannot infer that a given region is necessary without lesion studies. The neuropsychologist then gathers a hundred computers that have had hammer blows to various parts of the motherboard, extension cards, and disks. After testing their abilities extensively, she carefully selects just the few that have a specific problem with the video output. She finds that among computers that don’t display video properly, there is an overlapping area of damage to the video card. This means of course that the video card is necessary for proper video monitor functioning. Other similar discoveries follow regarding the hard disks and the USB ports, and now we have a map of which regions are necessary for various functions. But for all of this, have the neuroscientists really understood how the computer works?

**The Moral**

As the above tale illustrates, despite all of our current sophisticated methods, we in neuroscience are still in a kind of early stage of scientific endeavor; we continue to discover many effects but lack a proportionally strong standard model for understanding how they all derive from mechanistic principles. There are nonetheless many individual mathematical and computational neural models. The Hodgkin-Huxley equations (Hodgkin and Huxley, 1952), Integrate-and-fire model (Izhikevich, 2003), Genesis (Bower and Beeman, 1994), SPAUN (Eliasmith et al., 2012), and Blue Brain project (Markram, 2006) are only a few examples of the models, modeling toolkits, and frameworks available, besides many others more focused on particular phenomena. Still, there are many different kinds of neuroscience models, and even many different frameworks for modeling. This means that there is no one theoretical lingua franca against which to evaluate empirical results, or to generate new predictions. Instead, there is a patchwork of models that treat some phenomena, and large gaps where there are no models relevant to existing phenomena. The moral of the story is not that the brain is a computer. The moral of the story is twofold: first, that we sorely need a foundational mechanistic, computational framework to understand how the elements of the brain work together to form functional units and ultimately generate the complex cognitive behaviors we study. Second, it is not enough for models to exist—their premises and implications must be understood by those on the front lines of empirical research.

**The Path Forward**

A more unified model shared by the community is not out of reach for neuroscience. Such exists in physics (e.g. the standard model), engineering (e.g. circuit theory), and chemistry. To move forward, we need to consider placing a similar level of value on theoretical neuroscience as for example the field of physics places on theoretical physics. We need to train neuroscientists and psychologists early in their careers in not just statistics, but also in mathematical and computational modeling, as well as dynamical systems theory and even engineering. Computational theories exist (Marr, 1982), and empirical neuroscience is advancing, but we need to develop the relationships between them. This is not to say that all neuroscientists should spend their time building computational models. Rather, every neuroscientist should at least possess literacy in modeling as no less important than, for example, anatomy. Our graduate programs generally need improvement on this front. For faculty, if one is in a soft money position or on the tenure clock and cannot afford the time to learn or develop theories, then why not collaborate with someone who can? If we really care about the question of how the brain works, we must not delude ourselves into thinking that simply collecting more empirical results will automatically tell us how the brain works any more than measuring the heat coming from computer parts will tell us how the computer works. Instead, our experiments should address the questions of what mechanisms might account for an effect, and how to test and falsify specific mechanistic hypotheses (Platt, 1964).

Selection of the week

October 31, 2014

The legendary twentieth century violinist David Oistrakh playing Claude Debussy’s Clair de Lune, from over 50 years ago.  Unfortunately, I can’t listen to this piece without being reminded of the 2001 film Ocean’s Eleven. It wasn’t a bad film per se but I certainly don’t want to have it be associated with one of the iconic pieces from the classical repertoire.

 

 

How we got to now

October 30, 2014

I’ve been watching the highly entertaining PBS series “How we got to now” based on the book and hosted by Steven Johnson.  You can catch the episodes here for a limited time.  Hurry though because the first two episodes expire tomorrow.

Robert Solow

October 29, 2014

Don’t miss Nobel Laureate Robert Solow on econtalk.  Even at age 90, he’s still as sharp as ever.

http://www.econtalk.org/archives/2014/10/robert_solow_on.html

The Ebola response

October 25, 2014

The real failure of the Ebola response is not that a physician went bowling after returning from West Africa but that there are not more doctors over there containing the epidemic where it is needed. Infected patients do not shed virus particles until they become symptomatic and it is emitted in bodily fluids. The New York physician monitored his temperature daily and reported immediately to a designated Ebola hospital the moment he detected high fever. We should not be scape goating physicians who are trying to make a real difference in containing this outbreak and really protecting the rest of the world. This current outbreak was identified in the spring of 2014 but there was no international response until late summer. We know how to contain Ebola – identify patients and isolate them and this is what we should be doing instead of making  emotional and unhelpful policy decisions.

Selection of the week

October 24, 2014

How about some Tango?  Here is Argentine composer Astor Piazzolla performing his composition Adios Nonino on the accordion.

 

Selection of the week

October 17, 2014

The utterly unique Canadian Brass performing Rimsky-Korsakov’s “Flight of the Bumblebee”.

It takes a team

October 15, 2014

Here is a letter (reposted with permission) from Michael Gottesman, Deputy Director for Intramural Research of the NIH, telling the story of how the NIH intramural research program was instrumental in helping Eric Betzig win this years Nobel Prize in Chemistry.  I think it once again shows how great breakthroughs rarely occur in isolation.

Dear colleagues,

The NIH intramural program has placed its mark on another Nobel Prize. You likely heard last week that Eric Betzig of HHMI’s Janelia Farm Research Campus will share the 2014 Nobel Prize in Chemistry “for the development of super-resolved fluorescence microscopy.”  Eric’s key experiment came to life right here at the NIH, in the lab of Jennifer Lippincott-Schwartz.

In fact, Eric’s story is quite remarkable and highlights the key strengths of our intramural program: freedom to pursue high-risk research, opportunities to collaborate, and availability of funds to kick-start such a project.

Eric was “homeless” from a scientist’s viewpoint. He was unemployed and working out of a cottage in rural Michigan with no way of turning his theory into reality.  He had a brilliant idea to isolate individual fluorescent molecules by a unique optical feature to overcome the diffraction limit of light microscopes, which is about 0.2 microns. He thought that if green fluorescent proteins (GFPs) could be switched on and off a few molecules at a time, it might be possible using Gaussian fitting to synthesize a series of images based on point localization that, when stacked, provide extraordinary resolution.

Eric chanced to meet Jennifer, who heads the NICHD’s Section on Organelle Biology. She and George Patterson, then a postdoc in Jennifer’s lab and now a PI in NIBIB, had developed a photoactivable version of GFP with these capabilities, which they were already applying to the study of organelles. Jennifer latched on to Eric’s idea immediately; she was among the first to understand its significance and saw that her laboratory had just the tool that Eric needed.

So, in mid-2005, Jennifer offered to host Eric and his friend and colleague, Harald Hess, to collaborate on building a super-resolution microscope based on the use of photoactivatable GFP. The two had constructed key elements of this microscope in Harald’s living room out of their personal funds.

Jennifer located a small space in her lab in Building 32. She and Juan Bonifacino, also in NICHD, then secured some centralized IATAP funds for microscope parts to supplement the resources that Eric and Harald brought to the lab.  Owen Rennert, then the NICHD scientific director, provided matching funds. By October 2005, Eric and Harald became affiliated with HHMI, which also contributed funds to the project.

Eric and Harald quickly got to work with their new NICHD colleagues in their adopted NIH home.  The end result was a fully operational microscope married to GFP technology capable of producing super-resolution images of intact cells for the first time. Called photoactivated localization microscopy (PALM), the new technique provided 10 times the resolution of conventional light microscopy.

Another postdoc in Jennifer’s lab, Rachid Sougrat, now at King Abdullah University of Science and Technology in Saudi Arabia, correlated the PALM images of cell organelles to electron micrographs to validate the new technique, yet another important contribution.

Upon hearing of Eric’s Nobel Prize, Jennifer told me: “We didn’t imagine at the time how quickly the point localization imaging would become such an amazing enabling technology; but it caught on like wildfire, expanding throughout many fields of biology.”

That it did! PALM and all its manifestations are at the heart of extraordinary discoveries.  We think this is a quintessential intramural story. We see the elements of high-risk/high-reward research and the importance of collaboration and the freedom to pursue ideas, as well as NIH scientists with the vision to encourage and support this research.

Read the landmark 2006 Science article by Eric, Harald, and the NICHD team, “Imaging Intracellular Fluorescent Proteins at Nanometer Resolution,” at http://www.sciencemag.org/content/313/5793/1642.long.

The story of the origins of Eric Betzig’s Nobel Prize in Jennifer Lippincott-Schwartz’s lab is one that needs to be told. I feel proud to work for an organization that can attract such talent and enable such remarkable science to happen.

Kudos to Eric and to Jennifer and her crew.

Michael M. Gottesman

Deputy Director for Intramural Research


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