Sick Papes sat down with Jonathan Tang to discuss his recent paper “A Nanobody-Based System Using Fluorescent Proteins as Scaffolds for Cell-Specific Gene Manipulation.” Sick Papes also did this interview the old fashioned way, like an idiot, by recording and transcribing rather than emailing and relaxing. Hence the big delay. But like rediscovering a bottle of once lost liquor in your dirty clothes pile, you should be excited: Jonathan took advantage of antibodies from a camel to make GFP – the head honcho fluorescent protein for cellular visualization – friggin’ functional, as the key scaffolding ingredient in a threesome of transcriptional hedonism. He calls these nanobodies “transcription devices” and if you F with GFP, you better start getting creative.
Jonathan Tang et al. 2013. A Nanobody-Based System Using Fluorescent Proteins as Scaffolds for Cell-Specific Gene Manipulation. Cell 154, 928–939
SP: I’d like to start this interview with a quote from the movie Nacho Libre. Have you ever seen Nacho Libre?
SP: Okay well it’s a movie where Jack Black plays a Mexican wrestler and at one very important point in the movie he says, “Under the clothes we find the man and beneath the man we find… his nucleus.” What type of man are you and what’s up with your nucleus?
JT: Okay. I guess on the surface I’m pretty shy person. Underneath I am someone who wants to change the world. And my nucleus? I hope it’s functioning well, with little UV induced damages.
SP: You’ve invented a technology for retrofitting transgenic organisms called “transcription devices dependent on GFP.” Can you explain GFP and “transcription devices” to our readership?
JT: GFP stands for green fluorescent protein, it was discovered in a jellyfish 40-50 years ago and emits green fluorescent light. GFP has been put to use as a tool in molecular biology since 1994, allowing researchers to tag proteins and visualize cellular processes. This has been a powerful, Nobel prize winning tool for researchers. Regarding “transcription devices,” that’s a fancy name I gave to an engineered, hybrid transcription factor. The idea is to make the device activate transcription, but only when its two independent protein parts are tethered to GFP. If GFP is present in the cell, the device be fully formed and functional to initiate transcription in a downstream gene of interest.
SP: Most SP readers have a 3rd grade education. How would you explain this technology to a 3rd grader?
JT: I don’t know if a 3rd grader would understand genes, but here it goes: the transcription devices are present throughout an organism but remain inactive except in cells with GFP. In these cells, devices tether themselves to GFP and initiate events inside cells to turn on other genes.
SP: How does this interaction actually work?
JT: It’s based on protein-protein interactions. The devices are binding proteins derived from camel antibodies that bind to GFP with high affinity. The reason why we use camel antibodies is because the antigen recognition domain is contained in a single peptide segment. Conventional antibodies are hard to express in cells as they are made of two proteins joined by a breakable disulfide bond. I found that there were pairs of the these camel antibodies that recognized different parts of GFP and could co-occupy GFP. This allows one to simultaneously tether different protein domains on to GFP. In this case, I tethered a DNA binding domain with one camel antibody and a transcription activation domain with another. GFP is then the scaffold for forming an active transcription factor which can then initiate transcription of any gene of interest downstream of its genome binding site.
SP: It seems like you kind of owe the camel a lot. Pretend I’m a camel. What would you like to say to me?
JT: I guess for the sake of the next Nature paper, tell me why you evolved single chain antigen binding domains. Please tell me that. If you do, I’ll pay you back with a lot of water.
SP: Like middle-author type water?
JT: Like a gallon of water. Other than that, thank you very much you solved my problem.
SP: At any point in your project, did you make a scale model of GFP in your kitchen to try and identify where the transcription devices would bind?
JT: Not quite like that, but I did do a look at a lot of crystal structures and computational models of GFP structure, along with the structures of the GFP binding nanobodies. I just kept trying to fit them together. Turns out it was a waste of my time, because in the end I had to empirically pair-wise test all 6 nanobodies.
SP: How happy were you when you found a pair that worked ?
JT: I was pretty happy actually. I was in the lab at like 5 o’clock in the morning (editors note, JT is a confirmed night owl) and there was nobody around. I was just so happy, but there was no one to tell. Then my adrenaline went up and I walked around the building looking for people to tell but there was still no one. So I went back to work.
SP: That was a breakthrough moment in the project?
JT: Yes. There was no way to know if the system would work at all. I only had the six nanobodies which were generously given to us.
SP: It’s amazing how a couple of hours of positivity can energize us to wade through a swamp of disappointment that can last years.
JT: That’s true. Prior to that moment there was a lot of disappointment.
SP: Did you try other systems?
JT: Yes, I tried to make a Cre recombinase into a GFP-dependent into Cre recombinase. It was a naïve idea. But those experiments were helpful, as they did told me that the nanobodies could direct GFP to distinct compartments in the cell. That was the first clue the reagents I had could tether GFP.
SP: How do you think about, or visualize, these high affinity interactions between the nanobody devices and GFP? Like a key in a lock? Bugs on a windshield?
JT: Yes, I think of them as locks and keys, they simply come together. Without the high affinity, I don’t think it would work very well.
SP: One of my favorite things about this paper is that the technology that’s developed is not only an important proof of concept, but is immediately of practical use. What is the current utility of your GFP nano-bodies? Where do you see this technology going forward?
JT: One of the exciting applications is for retrofitting transgenic GFP animals. Many GFP lines have been engineered to express GFP in genetically defined populations of cells. For example, the GENSAT (http://www.gensat.org/index.html) project has over 1,500 mouse lines for labeling neuron populations in the brain and retina. Now the question is can we use these nanobody devices to perturb function in GFP expressing cells. One idea would be to turn on the light-sensitive ion channel channelrhodopsin in GFP expressing brain cells to make these neurons fire action potentials with light. As we show in the paper, with this technology you can probe the downstream brain circuitry from the cells that were previously only visualizable. In the long term, this paper suggests that GFP itself is a great transgene. Because it can be used for anything. You can imagine building many types of synthetic systems that use GFP to turn on or off different cellular processes, by mating GFP animals to other animals carrying nanobody devices or by introducing these devices with viruses.
SP: Have you thought about targeting your transcriptional devices to other proteins too?
JT: You mean using other proteins for turning on the system? That’s something that we’re looking into doing.
SP: One more question for you, on behalf of the molecular aficionado readership: In your paper, you note that there are pros and cons to your devices vs. traditional recombinases for manipulating genomes. Can you explain those pros and cons?
JT: The current system uses three components to form the hybrid transcription factor. While this is not as efficient as functional single molecules like Cre, it does have the ability to achieve more specificity through intersectional expression of the individual components. The other problem we experienced was having too much GFP, which sequesters individual nano-devices without the paired binding necessary for transcriptional activation. So the nano-devices have to be tested with each transgenic line. Unlike with Cre, which tends to enact permanent changes in a cell and its progeny, this system can also be reversible, dependent on the continued presence of GFP.
Year in and year out since the beginning of time, the amber fields of research programs across this great land are sprinkled with NSF fertilizer and grow the science crops that feed our hungry brain-mouths. While most days we feed our bloated carcasses on the high fructose corn syrup of the mind, every once in a while, you fill your cow horns with the right kind of manure, nail the astrological planting cycle and BLAMMO! - when the research harvest comes in, it comes in big. Well, it’s a boom year and the organic veggie du jour is bee learning and cognition. Here’s just one hors d’eouvre to whet your appetite:
My most vivid memories of childhood summers come from wandering along the Maine coast listening to my Aunt describe the auras of unwitting passersby, from “deep-blue” for the kid on a skateboard, “wispy green” for the owner of the Life is Good shop, and “surprisingly rectangular camo-colored” for the potbelly-sporting middle-aged man with a warm Budweiser and a Kiss lunchbox. Like these divine beach-goers, all living things (including the most heartless beasts of all creation: plants) give off subtle electrical fields. Despite its profound implications for literally everything, research on electric field perception has been mainly restricted to publications in Frontiers in Quack Science and F1000’s “What the $&*% do we know?” section. Two recent papers, though, are finally lending heft to the otherworldly electro-perceptational abilities of bees.
Up first is a sick pape showing that bees can sense electric fields created by plants. By creating artificial flowers (“E-flowers”, or E-cigarettes for bees) where they could measure and manipulate the electric field, Clarke and friends showed that bees can learn to differentiate between flowers that are completely identical except for their electric field. Mind-blowingly, the mere presence of the bee near a flower also changes the flower’s electrical pattern, so bees may be able to use their aura-sniffing abilities to figure out which flowers have been recently cleaned out by some other nectar-hungry bee.
While this study definitively showed the presence of the Third Eye in bees, more questions are raised than answered: Does the third eye align with the seventh chakra? Can the NSA use it to track my Private Browsing content? What causes Third Eye Blindness?
Thankfully, in a case of cosmic alignment, within a couple of weeks of this pape coming out, YET ANOTHER sick pape from a totally separate group gave us insight into how this might work. Coulomb’s law states that two charged particles will exert a physical force upon each other. Since insect antennae carry a charge, they could theoretically move in the presence of an electric field, allowing bees to perceive these electric fields.
In a beautiful series of “set em up and knock em down” experiments in our second sick pape, Greggers and amigos showed that bee antennae move in response to electric fields and that these movements juice up some specific neural pathways that allow the bee brains to perceive electricity. Indubitably sick.
sick papes would like to officially call out those nobel prize winners who only grow their hair out after they got their prize. i’m embarrassed i even have to write this, you poser shits. as if you weren’t already getting enough attention. if we see you in the street we’re apt to de-tail you through the confiscation of your “life is good” scrunchy. get a grip, last warning.
Gene-Swapping Spits Insight into the Mouth of the Vertebrate Mind
Nithianantharajah et al. 2012. Synaptic scaffold evolution generated components of vertebrate cognitive complexity. Nat Neuro
Ryan et al. 2012. Evolution of GluN2A/B cytoplasmic domains diversified vertebrate synaptic plasticity and behavior. Nat Neuro
Start private browsing. Under my favorite categories of experiments there on the right you’ll find “gene swapping.” Click on that. O sweet gene swapping. I’m back. It’s like momma earth just spat out DNA here just so experimentalists could do gene swap experiments and fucking rub their hands together and snort and drink coffee and wait for the results. I should start explaining gene swap experiments in this sentence but I just need to say one more time: in the world of dazzling complexity that is the cell or (eek) tissue or even (eek eek) the whole enchilada, swapping genetic elements offers a straightforward molecular razor for whatever. Currently we do it one element at a time, but in the future, who knows how many combinations we can apply and track before our brains explode.
Okay a gene swap experiment is pretty much exactly like it sounds. Change a single gene in some subtle or not so subtle way to something else. Make it non-functional say, or maybe just get a mutation that turns the protein in the human form or resistant to a flavor of post-translational modifications or swap subsets of gene-parts to figure out what part of the protein does what. And it’s slick as shit. Cause that baby’s siblings don’t have the change and you just compare your normal dude to the mutant you’re studying. Get at that infinitely complex cool and mysterious result stemming from something very discreet you did on the atomic/nanometer scale. Not bad human experimentalism not bad!
I’m blowing chunks on a couple of back to back Nature Neuroscience boon-diggler gene swap experiments right now: Nithianantharajah et al. and Ryan et al. (2012). I honestly follow this shit dropping from the Grant lab in the UK but I don’t understand it. I mean I understand it. I get their experiments for sure and that they’re trying to use a comparative approach across species to examine the evolution of the synapse and, well, cognition. But as pretty much the only guys in this business, it’s hard to predict what they’re going to pop out next.
Nithianantharajah et al (just fyi, it takes 10 ocean mana to tap this character into play but it’s worth it cause he’s got 12 hit points) assay the cognitive capabilities of mice that lack one of four Dlg genes. The Dlg family constitute major structural components of that sweet little signaling organelle that makes up the receiving half of the excitatory synapse. If the post-synaptic density is a lobster trap, then the Dlgs are the different gauges of chicken wire. There are four of these boogers because of ancient genome duplications in the vertebrate lineage. So to understand how each Dlg contributes to cognition is to understand how the duplication of genes allow each dupli-can’t to involve into a dupli-can!: a twisted sister of it’s own specialized function. One cool thing about this pape is the authors assay the cognitive prowess of each Dlg mutant mouse by forcing them to play an iPad. Like our society, but literally thirsty instead of spiritually thirsty. Each individual Dlg knock out showed different cognitive deficiencies suggesting a lack of functional redundancy in each of the 4 genes. Interestingly, Dlg3 knock out mice showed increased performance in tasks requiring cognitive flexibility and attention, meaning they might have a shot at beating my Mom at bejeweled. The take home load is that in these knock-out swap experiments, the authors demonstrate that ancient genome duplications allowed for the elaboration of the cognition of mice. That’s a pretty big rip on theory bong. Thanks straightforward knock out experiments and tablet computing!
Ryan et al. swap out the intracellular tails of another set of duplicated synaptic genes. This time the targets are the two main subunits of the NMDA receptors. NMDA receptors are ion channels that serve as coincidence detectors of neuronal activity and flux calcium, in what is equivalent of a particular synapse sending a text message about it’s state (“party’s on/party sux”) to it’s nearest neighbors and in some cases the friggin nucleus. The tails of the two subunits are a particularly informed switch since these parts of the proteins are the most divergent and function to bind different swaths of intra-cellular molecules. So it would seem each tail recruits a different signaling network to respond to calcium. So how do we test how this tail divergence influences cognition? GENE SWAP and iPADS baby!
So keep in mind: swapping out the tail of sub-unit A onto B means that both proteins have A tails and no B tail exists. So get double-duty of one tail and a complete lack of duty of the other. So whatever phenotypes emerge from these swappings could be due to a lack of B or over-binding from A (or synergies in between).
These dudes conveniently grouped the behaviors that were insensitive to the swap, only sensitive to unidirectional swaps, or sensitive to both swaps. Only impulsivity related behaviors required having both tails. Perception, anxiety, coordination and general activity levels required having one tail or the other. Learning in general remained intact when tails were swapped. Using the divergent tails of the NMDA receptor tails as a proxy, the authors suggest that more sophisticated regulation of motivational and emotional behaviors was selected for during the early evolution of vertebrates; learning is based on function that is redundant across tails and thus an older phenomenon.
Wow! Duh! And that’s how it goes in the field of synapse evolution
Waters, J., Holbrook, C., Fewell, J., & Harrison, J. (2010). Allometric Scaling of Metabolism, Growth, and Activity in Whole Colonies of the Seed‐Harvester Ant Pogonomyrmex californicus The American Naturalist, 176 (4), 501-510 DOI: 10.1086/656266/>
We all know the feeling: You’re lying naked in a sun-soaked field after taking a fistful of mushrooms and watching waves of energy explode through your friends’ braincases. And no matter how long you watch the trees breathe, just can’t shake the question: “Where does my body end and the world begin?” Turns out this cosmic question has a hallowed tradition, and just about no one knows how to draw boundaries around a body.
The little guys that fucks with our best minds most royally on this distinguished issue are the social Hymenoptera (ants, bees, and wasps). Dudes have been flubberbusting long and hard about whether we should think about the bees in a hive (or people in a city, or dicks in a game of dick jenga) as a wonderful communion of separate beings or as all just the dangly bits of one MegaMan. As the disturbing old saying goes, there’s many ways to skin a cat, but what perverted shitbag wants to to skin a cat a bunch of different ways? So the world was on the verge of turning its back forever on this age old question and exploding in a supernova of its own ignorance.
That is until some brave souls (Dr. James Waters and colleagues) figured out the illest of ways to blow the lid off a part of this problem. But let me slow my roll a bit and fill in the rubbly background that makes it crystalline just way this pape is so sick:
So, what these dudes did was investigate this same problem in ants, the superest of superorganisms. In an ant colony, you should be able to predict how much energy the whole colony is using based on their average body mass (e.g. you should be able to just sum up the metabolic rate of a bunch of small ants). But when they put whole colonies of these little guys in a fancy box that measures how fast they’re using up their cosmic energies, turns out they’re doing exactly not that. Specifically, their metabolic rate is what you’d predict for a single organism that had the collective mass of all the ants. And metabolic rate changes with colony size the same way it does for bigger bodies. So, in summary, ants (a) are fucking crazy, and (b) on both the mystical and physical planes appear to be working just like a single, physically integrated body does. Why? Lord knows. But this paper is opening up ways to answer that question and new ways to think about the most basic aspects of how organisms are put together. Sick.
Eliasmith, C., Stewart, T., Choo, X., Bekolay, T., DeWolf, T., Tang, Y., & Rasmussen, D. (2012). A Large-Scale Model of the Functioning Brain Science, 338 (6111), 1202-1205 DOI: 10.1126/science.1225266
The H. P. Lovecraft novella At the Mountains of Madness is a story about scientific hubris, and the insignificance of human achievement when confronted with the vastness that is the cosmos. The central characters are scientists searching the Antarctic for novel geological or biological forms. They uncover a world of strange, ancient beings, seemingly preserved in the ice. Their discovery has profound implications for the nature of biological evolution, and perhaps the history — and future — of earth itself. The primary entity they find is a god-like tentacled, winged, gilled monstrosity with strange geometrical features. We are given this account:
“It had digestion and circulation, and eliminated waste matter through the reddish tubes of its starfish-shaped base … The nervous system was so complex and highly developed as to leave [the scientist] aghast. Though excessively primitive and archaic in some responses, the thing had a set of ganglial centers and connectives arguing the very extremes of specialized development. Its five-lobed brain was surprisingly advance and there were signs of a sensory equipment, served in part through the wiry cilia of the head, involving facts alien to any other terrestrial organism … It was partly vegetable, but had three-fourths of the essentials of animal structure.”
I had a similar reaction when reading about S.P.A.U.N., the monstrous creation described by Eliasmith et al. in their pape: “A large-scale model of the functioning brain”. The title alone suggests a new and scary precipice of human achievement. And a most sick pape.
S.P.A.U.N. is half way between a robot and a computer program. It has a small camera attached to its head, and a robotic appendage extending from an implied torso that is capable of drawing all manner of digits between 0 and 9. Its brain has multiple sub-systems that independently control the encoding of visual input, reward, working memory, decoding, and motor output. The thrust of Eliasmith et al.’s pape is not that any one component of S.P.A.U.N. is new, but that it can perform not just one task but a variety of tasks, all of which humans can perform, and that its modular architecture resembles and realistically models at least some aspects of the human brain.
How close does S.P.A.U.N. come to resembling the brain? Each of its systems are meant to correspond to cortical and sub-cortical brain areas or functions, though much of the correspondence seems superficial. For example, S.P.A.U.N. has a system for handling “Visual Input”. It’s implied that it corresponds to areas V1, V2, V4, and IT of the primate visual pathway. But S.P.A.U.N. cannot mimic known processing in all those areas because we still don’t know what they do! The visual system of S.P.A.U.N. also reveals that its individual neurons are not as realistic as you might think. The authors stress that it uses biologically-realistic neurons with neurotransmitter dynamics, but most of the visual system instead uses simple linear combinations and thresholding. It’s hard to evaluate these shortcuts because the system is so complex.
So S.P.A.U.N. can do some cool tricks and kind of maybe looks like a brain. Can it fight a bear in one-on-one combat? Definitely not. S.P.A.U.N.‘s proponents admit that it is not the most impressive robot. When I googled “what is the sweetest robot?” I found this strawberry-picking robot. It uses 3D image processing to evaluate ripeness, and can delicately pluck luscious plump strawberries from their stems. That seems more impressive than S.P.A.U.N.
Becoming the best robot is not S.P.A.U.N.’s goal. What is the goal? Building AI systems that perform complex, physical, interactive tasks with flexible, modular systems resembling known biology, that could in principle help us understand how brains work. A longstanding, and recently popularized but criticized, tradition in AI is to build systems that have vast representational power, memory, and fancy statistical learning algorithms, but generally produce simple outputs. For example, a computer vision system that encodes a complex image but only needs to decide which of 10 different kinds of objects it is looking at. But most animals evolved to do more than spit out a 10-bit vector. They engage in real-time with a dynamically changing world. S.P.A.U.N.’s complex motor output and working memory is a step forward, but its emphasis on symbolic reasoning — which number comes next in a sequence? — is excessively abstract. If such symbolic reasoning evolved as an abstraction of more directly-physically-realized functions — when can I jump to make sure I catch the next mouse? — why not first try to build robots that can accomplish those tasks? I recommend the exciting research program of the “embodied robotocist” Rodney Brooks, especially his efforts in the 1990s to build robots with distributed control systems for path finding and navigation. He also appears, looking entirely crazed, in this movie, which features robots, lion taming, tree grooming, and naked mole rats. We are truly standing beneath the Mountains of Madness.