May 9, 2016
Steve Siegelbaum
“Storing memories through cortico-hippocampal circuits”
Category: Uncategorized
2016 McGovern Symposium: Lindsey Glickfeld
May 9, 2016
Lindsey Glickfeld, Duke University Medical School
“Functional specialization in the mouse visual cortex”
2016 McGovern Symposium: Surya Ganguli
May 9, 2016
Surya Ganguli, Stanford University
“A theory of neural dimensionality, dynamics and measurement”
2016 McGovern Symposium: Carl Petersen
May 9, 2016
Carl Petersen, Ecole Polytechnique Federale de Lausanne
“Neural circuits for goal-directed sensorimotor transformation”
2016 McGovern Symposium: Welcoming Remarks
May 9,2016
Mark Harnett, McGovern Institute
2016 McGovern Symposium: Jennifer Raymond
May 9, 2016
Jennifer Raymond, Stanford University
“Neural learning rules in the cerebellum”
2016 McGovern Symposium: Gautam Awatramani
May 9, 2016
Gautam Awatramani
“The fine balancing act of GABAergic/cholinergic retinal starburst amacrine cells”
Controlling RNA in living cells
MIT researchers have devised a new set of proteins that can be customized to bind arbitrary RNA sequences, making it possible to image RNA inside living cells, monitor what a particular RNA strand is doing, and even control RNA activity.
The new strategy is based on human RNA-binding proteins that normally help guide embryonic development. The research team adapted the proteins so that they can be easily targeted to desired RNA sequences.
“You could use these proteins to do measurements of RNA generation, for example, or of the translation of RNA to proteins,” says Edward Boyden, an associate professor of biological engineering and brain and cognitive sciences at the MIT Media Lab. “This could have broad utility throughout biology and bioengineering.”
Unlike previous efforts to control RNA with proteins, the new MIT system consists of modular components, which the researchers believe will make it easier to perform a wide variety of RNA manipulations.
“Modularity is one of the core design principles of engineering. If you can make things out of repeatable parts, you don’t have to agonize over the design. You simply build things out of predictable, linkable units,” says Boyden, who is also a member of MIT’s McGovern Institute for Brain Research.
Boyden is the senior author of a paper describing the new system in the Proceedings of the National Academy of Sciences. The paper’s lead authors are postdoc Katarzyna Adamala and grad student Daniel Martin-Alarcon.
Modular code
Living cells contain many types of RNA that perform different roles. One of the best known varieties is messenger RNA (mRNA), which is copied from DNA and carries protein-coding information to cell structures called ribosomes, where mRNA directs protein assembly in a process called translation. Monitoring mRNA could tell scientists a great deal about which genes are being expressed in a cell, and tweaking the translation of mRNA would allow them to alter gene expression without having to modify the cell’s DNA.
To achieve this, the MIT team set out to adapt naturally occurring proteins called Pumilio homology domains. These RNA-binding proteins include sequences of amino acids that bind to one of the ribonucleotide bases or “letters” that make up RNA sequences — adenine (A), thymine (T), uracil (U), and guanine (G).
In recent years, scientists have been working on developing these proteins for experimental use, but until now it was more of a trial-and-error process to create proteins that would bind to a particular RNA sequence.
“It was not a truly modular code,” Boyden says, referring to the protein’s amino acid sequences. “You still had to tweak it on a case-by-case basis. Whereas now, given an RNA sequence, you can specify on paper a protein to target it.”
To create their code, the researchers tested out many amino acid combinations and found a particular set of amino acids that will bind each of the four bases at any position in the target sequence. Using this system, which they call Pumby (for Pumilio-based assembly), the researchers effectively targeted RNA sequences varying in length from six to 18 bases.
“I think it’s a breakthrough technology that they’ve developed here,” says Robert Singer, a professor of anatomy and structural biology, cell biology, and neuroscience at Albert Einstein College of Medicine, who was not involved in the research. “Everything that’s been done to target RNA so far requires modifying the RNA you want to target by attaching a sequence that binds to a specific protein. With this technique you just design the protein alone, so there’s no need to modify the RNA, which means you could target any RNA in any cell.”
RNA manipulation
In experiments in human cells grown in a lab dish, the researchers showed that they could accurately label mRNA molecules and determine how frequently they are being translated. First, they designed two Pumby proteins that would bind to adjacent RNA sequences. Each protein is also attached to half of a green fluorescent protein (GFP) molecule. When both proteins find their target sequence, the GFP molecules join and become fluorescent — a signal to the researchers that the target RNA is present.
Furthermore, the team discovered that each time an mRNA molecule is translated, the GFP gets knocked off, and when translation is finished, another GFP binds to it, enhancing the overall fluorescent signal. This allows the researchers to calculate how often the mRNA is being read.
This system can also be used to stimulate translation of a target mRNA. To achieve that, the researchers attached a protein called a translation initiator to the Pumby protein. This allowed them to dramatically increase translation of an mRNA molecule that normally wouldn’t be read frequently.
“We can turn up the translation of arbitrary genes in the cell without having to modify the genome at all,” Martin-Alarcon says.
The researchers are now working toward using this system to label different mRNA molecules inside neurons, allowing them to test the idea that mRNAs for different genes are stored in different parts of the neuron, helping the cell to remain poised to perform functions such as storing new memories. “Until now it’s been very difficult to watch what’s happening with those mRNAs, or to control them,” Boyden says.
These RNA-binding proteins could also be used to build molecular assembly lines that would bring together enzymes needed to perform a series of reactions that produce a drug or another molecule of interest.
2016 Scolnick Prize Lecture: Dr. Cornelia Bargmann
Title: Genes, Neurons, Circuits and Behavior: An Integrated Approach in a Compact Brain
Speaker: Cornelia Bargmann, The Rockefeller University
Date + Time: March 30, 2016 @ 4pm
Location: 46-3002 (Singleton Auditorium)
Abstract:
Behavior is variable, both within and between individuals. We use the nematode worm C. elegans to ask how genes, neurons, circuits, and the environment interact to give rise to flexible behaviors. This work has provided insights into four kinds of behavioral variability mediated by overlapping circuits: the gating of information flow by circuit state over seconds, the extrasynaptic regulation of circuits by neuropeptides and neuromodulators over minutes, the modification of behavior by learning over hours or days, and natural genetic variation across generations.
2016 Sharp Lecture in Neural Circuits: Dr. Markus Meister
Title: “Neural computations in the retina: from photons to behavior”
Speaker: Markus Meister, Caltech
Date + Time: March 8, 2016 @ 4pm
Location: 46-3002 (Singleton Auditorium)
Abstract: The retina is touted as the brain’s window upon the world, but unlike a glass pane, the retina performs a great deal of visual processing. Its intricate circuits use ~70 different types of neuron. The output signals in the optic nerve are carried by 20 different types of retinal ganglion cell, each of which completely tiles the visual field. Thus the eye communicates twenty parallel representations of the visual scene. This raises several questions: What is being computed here, can we understand the visual feature reported by each type of ganglion cell? How is this feature computed by the circuit of neurons and synapses that leads to that ganglion cell type? And finally, why are these particular features getting computed, rather than some other set? In recent years, all these research areas have been turbocharged by modern genetic tools, especially the ability to visualize and modify select neuron types within a circuit. Some general insights are:
What? The various ganglion cell types fit on a spectrum from simple “pixel encoders” to “feature detectors”. A few types encode a very simple function of the image, like the local contrast, with a continuously varying firing rate. However, most types fire quite rarely and report specific features, for example differential motion between the foreground and the background. Some ganglion cells seem to play an alarm function; they are silent except under very specific stimulus conditions associated with threats.
How? It has emerged that dramatically different computations can result from circuits using the same kinds of neuronal elements, but arranged in a different sequence or combinations. In fact many of the twenty circuits in the retina share the same elements. On at least one occasion the same neuron is used to transfer signals in both directions! An important source of nonlinearity on which the computations are based is the sharp thresholding of signals at the bipolar cell synapse, which has emerged as a very versatile circuit element.
Why? It has been proposed that each of the twenty ganglion cell types of the retina is Evolution’s answer to a specific behavioral need that is served by the visual system. If so, then the selective silencing of one type of ganglion cell should affect only selected visual behaviors. Early experiments suggest this is a promising avenue of research.