What can a singing bird teach us about a jump shot?

image

Do you play music or sports? When you first learned how to play, you probably started by copying someone who was more experienced (like a coach, or an instructor), and you probably had to practice quite a bit in order to become good. Have you ever wondered what was going on in your brain when you learned these skills? The answer might actually lie in the brain of a musical bird called the zebra finch.

Zebra finches take courtship very seriously because they are monogamous. Males perform a song and dance to woo females. It is apparent that their song is vital to their mating rituals. Each bird’s song is unique, but it shares similarities to the song of that bird’s father. That’s because zebra finches first learn to sing by copying their dads. They start by “babbling,” then imitate each note that their father sings until they develop their own stereotyped song.

In the last few decades, neuroscientists have started to study how the zebra finches learn their songs. As a result, they’ve discovered some stunning likenesses to our own brains. For example, we now know that the birds learn and produce songs with a series of brain regions which are much like the parts of our brain that plan out motor sequences (such as swinging a tennis racket or playing scales on a musical instrument).

There are quite a few parallels between zebra finch songs and these learned motor sequences. Almost every one of these sequences starts out as an awkward jumble of partial actions. A novice musician has to think about each note in a scale as they’re playing it, while a professional can play almost any scale off the top of their head. An amateur basketball player might not have a very polished jump shot, but with coaching, they can make their motion more fluid. Just like the finches’ songs, a human’s learned motor sequences are very individualized. For example, if you look at many baseball players throwing pitches in slow-motion, you’ll notice tons of small differences in their deliveries.

When neuroscientists study zebra finch songs, they hope to learn more about how we create, develop, and maintain these personalized behavioral sequences. Imagine being able to see everything happening inside Joshua Bell’s brain as he became a violin virtuoso, or looking inside Ken Griffey Jr.’s brain as he develops his swing.

Want to know more about the state of the art in zebra finch research? You’re in luck! Michael Long, a neuroscientist at NYU, is coming to speak at UCSD as part of the DART Neuroscience Seminar Series.

What type of stuff is he going to talk about? In one recent publication, he described a mechanism that the birds use to prevent changes to a song once it’s been learned. It’s kind of like the finch’s version of saving a document as “read only.” Dr. Long found this mechanism by studying a finch brain area called HVC (which is analogous to the human “premotor cortex,” an area we use for action planning). Previous work has shown that HVC neurons become more active when anesthetized birds hear their tutor’s song. The Long Lab has developed a sophisticated way to monitor HVC cells in awake birds. They noticed that, when the finches weren’t unconscious, HVC activity during songs actually changed over development: juvenile animals had lots of activity, while adult birds had very little.

Through a series of very technically challenging experiments, Dr. Long and his lab discovered why this change occurred. They showed that HVC was actually being inhibited when the finches heard part of a song from their tutor that they already knew well. Since the adult birds knew their songs already, their HVC neurons were practically silent. The researchers proposed that this inhibition of HVC was a mechanism for protecting the developing song from being changed.

This is a huge finding in the field. It confirms that the song-learning circuit is specifically “tuning out” parts of songs that it already knows, and (most importantly) proposes a mechanism for how the bird brain can do that. There’s a good chance that our brains use analogous mechanisms to learn motor skills from a tutor or coach; perhaps someday scientists can apply this knowledge to human motor actions.

If you’re interested in learning more about zebra finches, come to The Marilyn Farquhar Auditorium this Tuesday at 4 PM (in the Center for Neural Circuits and Behavior building). It’s going to be a great lecture.

Sam Asinof is a first-year grad student in the UCSD Neurosciences program.  He’s currently rotating in Dr. Tina Gremel’s laboratory, where he studies how mice form habits.  In his free time, he loves to both play music and watch sports.   

Exploring the Dichotomous Consciousness

“One individual studied well, and thoughtfully, might enable you to draw conclusions that apply to the entire human species.”

-David Roberts, Professor of Surgery and Neurology at Dartmouth-Hitchcock Medical Center

The fascinating story of the split-brain patient dates back to the 1940’s. You might rightfully ask: “What is a split-brain patient?”

brain-split

Split-brain patients are individuals who have been plagued by intractable epilepsy — so much so that they were willing to undergo split-brain surgery, which is essentially a procedure that severs the connections between the left and right hemispheres of the brain. This surgical procedure was meant to prevent the spread of seizure activity from its site of origin, thereby controlling the occurrence of debilitating epileptic seizures. The procedure is also known as a corpus callosotomy because the anatomical structure that connects the two hemispheres of our brains is called the corpus callosum, the so-called highway system of information transfer in the brain.

“It was a total shot in the dark.”

– Michael Gazzaniga

The first group that investigated these patients in the 1940’s claimed that there were no significant cognitive or behavioral impairments as a result of split-brain surgery. Fast forward to the 1960’s and along came Michael Gazzaniga, a driven young student at Dartmouth. During his junior year, a Scientific American article on how nerves grow piqued Gazzaniga’s interest, so he wrote a letter to the author, the one and only Roger Sperry, one of the biggest names in neurobiology. In his letter, Gazzaniga inquired about research opportunities — a move he now refers to as a “shot in the dark” — and landed an NSF summer fellowship at Caltech.

gazz2

Gazzaniga as a student at Caltech in 1963

Sperry’s group at Caltech had been studying split-brain rats, cats, and monkeys for some time, and were observing dramatic effects on behavior, which raised a huge question mark in their minds about why earlier assessments of split-brain humans had not revealed significant post-surgical differences. They hypothesized that surgeries done in the 1940’s had not severed the corpus callosum and anterior commissure completely. Gazzaniga was thus tasked with coming up with novel and better ways of testing split-brain patients. So he did…

And his findings introduced the notion of functional lateralization to the field of neuroscience:gazz3

When split-brain patients were presented with visual information (such as an object or a word) in their right visual field, they were able to verbally identify the stimulus. Interestingly, if visual information was presented in their left visual field, patients were unable to do so — in fact they would typically say, “I don’t know.” To understand this phenomenon we must recall the following generalizations:

  1. Information in the right visual field is known to be processed by the left hemisphere, and information in the left visual field is known to be processed by the right hemisphere (see above figure).
  2. Certain aspects of language are known to predominantly reside in the left hemisphere of the brain.

From his observations, Gazzaniga came to the conclusion that split-brain patients were unable to verbally identify stimuli presented in their left visual field because, though the information would travel to the right hemisphere, it would not be transferred to the left hemisphere where ‘language resides’ due to the severed connection between the two hemispheres.

There is a twist however. Patients who stated that they “did not know” what the stimuli presented in their right visual field was were able to draw what they saw with their left hand. These observations along with many many follow-up studies testing for part-whole relations, apparent motion detection, mental rotation, mirror image discrimination, etc. led to the idea that there is perhaps a right hemisphere dominance for visuospatial processing. These ideas are not meant to be mutually exclusive for one hemisphere or the other. In fact, one patient clearly demonstrates that certain aspects of language, such as spelling, can also reside in the right hemisphere: P.S., a teenager split-brain patient, was asked “Who is your favorite girlfriend?” with the word ‘girlfriend’ flashing only in his left visual field. He was unable to answer the question verbally because the information remained in his right hemisphere; however his left hand (controlled by the right hemisphere) was able to select Scrabble letters and align them to spell’L-I-Z.’

gazz4.jpg

Split-brain patients were the key to studying the functions of the two hemispheres independently, and Gazzaniga recognized the value in capitalizing on what this unique patient population had to offer to the advancement of neuroscience. Among his many accomplishments are serving on the President’s Council on Bioethics between 2001-09, basically founding the field of cognitive neuroscience with fellow psychologist/linguist George A. Miller, and being awarded the Guggenheim Fellowship for Natural Sciences. Come hear him talk on lessons learned from split-brain research this Tuesday, January 12 at 4 pm.

Ege A. Yalcinbas is a first-year student in the neurosciences graduate program currently rotating in Dr. Chalasani’s lab. Michael Gazzaniga was one of the first neuroscientists she read about in high school so she is excited to fangirl him at his talk on Tuesday.

References:

Goodnight Fly: What Drosophila Can Teach Us About Sleep and Memory

In a lab studying the fly

There was a beaker

And a latex glove supply

And a microscope sitting

Next to a guy

And there was a help tab

Open in Matlab

And a fly mushroom body

And a desk that was sloppy

And a genetic screen

And a cabinet of caffeine

And a channel and a cell

And a silica gel

And industrious, brainy lab personnel

 

Goodnight fly

Goodnight guy

Goodnight microscope next to the guy

Goodnight tube

And the glove supply

Goodnight Matlab

And goodnight computer tab

Goodnight desk that is sloppy

And goodnight mushroom body

Goodnight genetic screen

And goodnight forms of caffeine

Goodnight science journal

And goodnight rhythm diurnal

Goodnight activation channel

And goodnight APL cell

Goodnight serotonin

Goodnight silica gel

And goodnight to the hardworking lab personnel

Goodnight GABA molecule

Goodnight DPM pair

And goodnight sleeping flies everywhere

– by Margaret Wise Brown (with slight revision by Anja Payne)

 

Like the ever-wise Margaret Wise Brown, Leslie Griffith and her lab are interested in how sleep is generated. Unlike Margaret Wise Brown, however, the Griffith lab’s interest in sleep goes beyond the parental desire to ease bedtime routines. The Griffith lab is working to answer key questions about sleep such as how the brain changes during sleep and whether these changes are involved in learning and memory.

In order to investigate these questions, the Griffith lab uses Drosophila Melanogaster to probe the role that specific neurons play in sleep and memory consolidation. In their recent paper (Griffith et. al, 2015) they demonstrate that the dorsal paired medial (DPM) neurons are able to promote sleep. Since DPM neurons are also involved in memory consolidation, this paper is one of the first to identify a shared anatomical location for both sleep and memory.

The effect of DPM neuronal activation on sleep occurs via the release of serotonin (5HT) and GABA onto α’/β’ mushroom body (MB) neurons. Activation of DPM neurons during memory consolidation results in inhibition of α’/β’ MB neurons. Since α’/β’ MB neurons are wake-promoting, inhibition of these neurons results in a decrease in sleep. The discovery that DPM neurons inhibit MB neurons is a novel finding and raises many questions regarding the circuit-level function of these neurons.

Several explanations for the utility of DPM inhibition have been proposed. One proposed explanation is that inhibition of α’/β’ MB neurons may impose a directionality on MB feedback which would lead to memory transfer and consolidation. This explanation parallels findings in mammals that show that broadly-projecting inhibitory neurons may coordinate excitatory activity in the hippocampus and neocortex which may control the timing of replay events during sleep.

 

For further information, and for the benefit of your own edification, come see Dr. Leslie Griffith perform on Tuesday, January 5th, 2016 in the CNCB Marilyn Farquhar Seminar Room and/or refer to the paper below.

Haynes P. R., Christmann B. L., Griffith L. C. (2015). A single pair of neurons links sleep to memory consolidation inDrosophila melanogaster. eLife 4:e03868. 

 

Anja Payne is a first-year Neuroscience graduate student at UCSD. Her neuro-scientific interests include almost everything under the sun with an ever-so-slight focus on learning and memory. Her non-scientific interests include zoos, beaches, puzzles, and her adorable, scruffy, mutt.

Exploring the neural basis of semantic concept formation

How can you tell a puppy from a kitten? Ask any first-grade class and you’ll get a range of answers. Some might start with visual information, like whether the animal has whiskers, how its ears look, or what kind of tail it has. Others might use auditory information, like the different sounds of barks and meows. The more creative might want to see if the animals smell or even taste different.

Though none of these answers is any more correct than the others, each reflects a different approach to the problem of categorization. In cognitive neuroscience, the ability to categorize objects is thought to require the existence of a distinct “concept” for each category, containing information about the typical properties, or features, that correspond to items in that category. The exact neural mechanisms by which concepts are formed and maintained, however, are not fully understood. According to one set of theories, concept representations are distributed across sensorimotor areas, with each area representing activation of a specific feature. By this mechanism, knowledge of the concept is a direct result of connections between different feature areas. Alternatively, other theories have proposed the existence of integration areas that connect to multiple sensorimotor regions and contain intermediate representations of concept knowledge. Though the set of features used to define a concept can span multiple sensory modalities (eg. vision, touch), the intermediate representation of a concept appears to exist in a theoretical semantic space, independent of any individual modality.

In a recent paper from the lab of Sharon Thompson-Schill, the role of these intermediate areas was examined using functional magnetic resonance imaging (Coutanche & Thompson-Schill). Specifically, this paper tested whether there is evidence to support the anterior temporal lobe (ATL) as a “convergence zone”, or an area where various feature fragments are consolidated to form a coherent representation of the object concept. During scanning, subjects viewed a screen of visual noise, while attempting to detect the presence of a specific fruit or vegetable within the noise. The researchers analyzed brain activity during the time period before the fruit or vegetable was actually on screen, meaning the subject was actively thinking about the concept for the fruit or vegetable, but was not actually viewing it. These memory-driven neural activation patterns were used to determine the type of information represented by the ATL during concept retrieval, and how it interacts with the feature information represented in other cortical areas.

Key findings:

1. Activation in the ATL is specific to the identity of the retrieved concept, i.e. the specific fruit or vegetable that is being recalled. Using MVPA and a roving searchlight (https://en.wikipedia.org/wiki/Functional_magnetic_resonance_imaging#Statistical_analysis), the authors were able to identify a cluster of regions near the location of the left ATL that had above-chance classification accuracy (below).

Screen Shot 2015-12-13 at 8.34.29 PM2. When a concept is retrieved, areas that represent specific features of the object (in this case, color and shape) are specifically activated. Again using MVPA, the authors identified a bilateral region of lateral occipital cortex (LOC) and right V4 as areas that encode feature fragments corresponding to the concept being retrieved.

3. Successful retrieval of the object identity is concurrent with activation of the feature fragments corresponding to that object. Concurrent decoding of color and shape within feature regions (LOC and V4) was found to be specifically predictive of successful object identity decoding in the ATL. This result further supports the importance of the ATL in binding the information stored in distributed feature areas into a coherent representation of an object concept.

These results demonstrate that the ATL is likely to function as a convergence zone, providing a plausible neural substrate for the formation and retrieval of feature-based object concepts. They also open the door to further speculation about the role of top-down processing in identification of objects by features, as well as how features may be differently weighted depending on the type of sensory information available. Current and future work by the Thompson-Schill lab may address such questions as well as contributing further insights into the neural mechanisms underlying conceptualization.

Source:

Coutanche, M.N., & Thompson-Schill, S.L. (2014). Creating concepts from converging features in human cortex. Cerebral Cortex.

Robot Monkeys and Neuroplasticity

Jose Carmena,

Studies robotic monkeys.

From lands far away.

His call to monkeys

“Brain-machine interfaces

Let you move again”

BMIs decode

Neural spikes, feed back to brain,

And create circuits.

“But while brains adapt,”

Jose and monkeys wondered,

“Can the BMI?

And if it did change,

Could it impair performance,

As others suggest?”

But CLDA* –

To new circuits it adapts,

Parameters change.

Monkeys had a task –

Control the cursor with brain,

And reach the target.

fig1_taskSetup

Fig 1. Experimental setup, and CLDA. A, B – CLDA used on day 1 to improve performance. Decoder then kept fixed. C, D – Two monkeys trained to hold at center, then reach for target. Monkeys used an arm or BMI to control a cursor.

CLDA used

Until monkey had success.

Then, only brain changed.

Improved performance,

Substantial by Wilcoxon,

Accurate movements.

behavioralComposit_v2

Fig 2. Behavioral performance in two-learner system (i.e. with neural and decoder adaptation). A,C – Performance across time in both monkeys improved. B – Reach trajectories across time. D – Average improvements in performance; tested by one-sided, paired Wilcoxon sign-rank test. “Seed” refers to performance with initial decoder. “Early” refers to day 1 performance. “Late” refers to best performance after day 1.

Neuron tuning curves,

Sharpened across training days,

Looked like performance.

So, CLDA

Stabilized cortical maps.

Effect was success.

fig3_v3_tuningChanges

Fig 3. Changes in neural tuning. A, B, C – Fitted tuning curves, across time, for various sample neurons. D – Changes in MD and PD across time; grey indicates neurons not significantly tuned. E – Pairwise correlation of ensemble tuning maps across time. F – Map correlations for each day (red) overlaid on task performance (black).

“But two things adapt,”

Jose and monkeys wondered,

“Which matters the most?”

With CLDA,

More improvement was observed.

Mirrored tuning change.

MD** and PD***,

Neuron tuning properties,

Varied with success.

If BMI wrong,

Assigned wrong MD/PD,

Tuning tried to match.

Once learning is done,

Neurons reach peak earlier.

Initiate reach.

Neurons can adapt

To the decoder employed

In tuning and time.

fig4_v4_tuningChangeMechanisms

Fig 4. Extent of neural adaptation is related to performance improvement. A – Increases in performance, and correlation between tuning and performance, for three samples, one of which had no CLDA applied (black trace). B – Map correlation on day 1 against change in task success. C – Ensemble-averaged change in MD or PD against change in task success. D – Fraction of BMI units with significant changes in MD and PD. E, F – Change in MD or PD against decoder weight (E) or error (F) assigned to each unit for either MD (E) or PD (F). Black lines are linear regressions.  G – Time histogram for sample units. H – Onset of direction tuning (left) and peak firing rate (right), early or late in learning.

“Still, one last question –

New circuits resist old ones?”

Said Jose et al.

“Because old circuits

Can overlap with new ones,

Interfere in task.”

Monkey maintained force,

While it performed the reach task,

At interspersed times.

This impaired success,

Disrupted monkey learning,

Only in SC****.

Impairment profound.

Disrupted neural tuning.

Lessened by learning.

fig5_simultaneousControl

Fig 5. Native arm movements do not interfere. A, B – Monkey J performed BMI-SC (simultaneous control) task. J first acquired force target with arm, then used BMI to move cursor. C – Performance across time. Blue dots are when CLDA was applied. D – Average BMI and BMI-SC task performance. E, G – Comparison of MD and PD in both tasks. F – Fitted tuning curves for two sample units.

Jose and monkeys.

CLDA and brain change.

They improve success.

They look to future.

Optimization is next.

More robot monkeys.

————————————————————–

Notes:

* Closed loop decoder adaptation

** Modulation depth

*** Preferred direction

**** Simultaneous control of both force and BMI-controlled reach task

Open Archive article:

Orsborn et al. (2014) Closed-Loop Decoder Adaptation Shapes Neural Plasticity for Skillful Neuroprosthetic Control. Neuron, p. 1380 – 1393.

____________________________________________________________________________________________

Javier How is a 1st year Neuroscience student currently rotating with Saket Navlakha at the Salk Institute. Upon a second reading of this post, he remembers why he’s not in an MFA program.

A New Path for Alzheimer’s Disease Research

Alzheimer’s Disease (AD) is the most common type of dementia (60% – 70% of dementia cases) and causes aberrant memory, thinking and behavior that progresses over time and ultimately results in death, usually within eight years of noticeable symptom onset. Symptoms generally first appear in people in their 60’s, and in 2015, an estimated 5.3 million Americans suffer from the disease of whom 5.1 million are age 65 and older. AD is currently the sixth leading cause of death in the United States.

There are two broad classes of AD, familial AD (FAD) and sporadic AD (SAD). FAD is relatively rare, accounting for between 1% and 5% of cases, and is characterized by either autosomal dominant inheritance or inheritance of genetic mutations in one or more of three genes, those encoding for: amyloid precursor protein (APP), presenilin 1 (PS1) and presenilin 2 (PS2). Inheritance of these genes causes increases in the production of AB42, which is the primary molecule in senile plaques. The etiology of SAD is much less clear, but is believed to derive from interactions between uncertain genetic and environmental risk factors. The best known genetic risk factor for SAD is the inheritance of the epsilon4 allele of the apolipoprotein E (APOE), which increases the likelihood of developing AD by 3 times in heterozygotes and 15 times in homozygotes. Additionally, genome-wide association studies (GWAS) have found 19 additional genetic markers that appear to affect risk. There are currently no known cures for AD, and treatment options are available but largely ineffective.

The current major hypothesis in the field of AD research characterizes AD pathophysiology using the amyloid cascade model. In this framework, amyloid beta (AB) fragments are thought to oligomerize and accumulate extracellularly as plaques, accompanied by tau protein hyperphosphorylation and neurofibrillary tangles, which causes neurotoxic events that result in neurodegeneration. This process starts primarily in the medial temporal lobes and frontal cortices, and eventually spreads throughout the brain.

Given the impact AD has on the general population, a major research effort is underway to better understand the disease and discover more effective treatment options. There are currently greater than 100,000 articles in Pubmed on the etiology, mechanisms, and typology on Alzheimer’s disease. However, there remains much to be discovered, and therapies generated based on this literature have by and large proven ineffective.

How could this be?

Lawrence Goldstein et. al. proposes that our efforts have fallen short partly because research on AD thus far has relied primarily on animal models and non-neuronal human cells to investigate the molecular mechanisms associated with the disease. Animal models are problematic because not one has generated a phenotype that fully characterizes the disease in its many forms, and often requires expressing human genes in a non-human genetic context. Using non-neuronal human cells is also problematic, mainly because they lack many of the most important qualities that make neurons unique, such as: size, ability to generate action potentials, extensive interconnectivity, compartmentalization into axonal and somatodendritic regions, and several others.

Goldstein et. al. suggest that a new era in AD research is underway, and will use human induced pluripotent stem cells (hiPSC). This technique allows human neurons cultivated from people with AD to be reprogrammed to a pluripotent state, which can then be differentiated into a variety of cell types such as neurons, astrocytes, oligodendrocytes, and other brain cells. Given that these cells were derived from people with AD and therefore have higher ecological validity than animal models or non-neuronal human cells, they can be used to more efficaciously test mechanisms of the disease, identify therapeutic targets, and evaluate genetic risk factors.

The use of hiPSCs in AD research is fairly new, but the few articles published thus far are promising. Goldstein et. al. breaks down the state of AD research using hiPSCs into three broad categories: 1) FAD mutations in APP (see above); 2) FAD mutations in PS1 and PS2, and; 3) effects of AB toxicity on different types of neurons. The results of these studies are shared in the table below.

Alzheimers Disease Table

Thus, the use of hiPSCs is an exciting new avenue of research into the possible causes and treatments of AD. However, if this new line of research proves to be the linchpin for finding an effective treatment or cure, there is a broader implication for neuroscientists: how much should we be relying on animal models and non-neural human cultures? To what extent are they useful? Can we devise a sound conceptual framework for deciding between methodologies? These are tough questions that scientists will struggle to answer in the very near term. However, if one branch of science can handle rapid growth and the requirement of choosing between myriad techniques, modern neuroscience is a good bet.

About the author: Daniel Stern is the same as he was for last week’s post, just slightly more rotund post Thanksgiving stuffing.

The Elegant Discovery of a Fear Memory Engram

Introduction

The purpose of this post is to make as accessible as possible the sequence of experiments that culminated in the first thorough evidence for the existence of an engram. An engram can be characterized as the brain’s physical representation of a memory in a constellation of neurons that were active while the memory was acquired, and are required to be active in order to recall that experience. The following post is a synthesis of an already very well written review of Sheena A Josselyn’s life work on discovering engrams for fear learning in the lateral amygdalae of mice.

Primer

Specific ensembles of neurons in the lateral amygdala (LA) are necessary for specific fear memories.

  • CREB (cAMP response element-binding protein) is a cellular transcription factor tthought to be involved in the formation of long-term memories, and has has been shown to play an important role in the formation of fear memories
  • Viral vectors over-expressed CREB in a subset of LA mouse neurons and facilitated fear memory formation AND localized the memories to the neurons over-expressing CREB, the engram.
  • Selective ablation of neurons over-expressing CREB (and therefor ‘containing’ the fear memory) selectively erased that particular fear memory, did not impact other fear memories, and did not preclude future fear memory formation.

Experiments

The following is an attempt to create an easily accessible synopsis of decades of work and the innumerable experiments it took to discover the existence of a fear engram.

Experiment 1 - JosselynExperiment 2 - JosselynExperiment 3 - JosselynExperiment 4 - Josselyn

About the Author: Daniel Stern is a first year graduate student in the Neuroscience PhD program at UCSD. His life can be characterized as a gradual shift from east to west coast, having recently escaped both Chicago and the prospect of becoming a lawyer in NYC. He is very happy and humbled to be doing something he loves instead.

A meditation on worms, synapses, and basic science

I’ll admit. I’m not a worm person.

Don’t get me wrong, I am well-versed in the value of C. elegans to science in general and neuroscience in particular. A worm is a compact circuit of identifiable neurons, a bundle of highly-documented genetic material, and shows surprisingly robust behavioral states. My bias against worms–I like my model organisms warm and fuzzy–is somewhat symbolic of the larger mismatch between basic science and public interest. Namely, C. elegans seem so far removed from my own physiology that I can’t help but wonder how significant such research will be for my narcissistic self.

In a 2011 TED talk, titled “Lost in Translation”, Dr. Daniel Colón-Ramos gives an eloquent defense for the importance of basic science research. He discusses a conundrum in science, one I encounter often when I try to describe my research to people who are not academics. As he says, “There is a huge disconnect between what people like about science,” namely outcomes that make our lives safer, easier, and healthier, “and what scientists actually do.” There is a perception, particularly for the life-sciences, that all research should be specifically to solve a problem, or cure a disease. A perception that if science is not actively trying to improve our lives then it is not worth pursuing.

Occasionally statements like this come from confused friends and family members, and these concerns I’m happy to allay. But such sentiments are worrying when they come from certain public figures or politicians. As an answer to those who wonder why all this federal money goes to research on things like fruit flies and “bacteria sex,” Colón-Ramos discusses a series of experiments on his model organism of choice: C. elegans. He describes the work of Brenner, Holvitz, and Sulston, whose work in mapping the lineage of C. elegans cells led to discovering the genetic regulation of cell death. Work that has significant implications for cancer research, and for which they shared the 2002 Nobel Prize in physiology. “Is this example exceptional?” Colón-Ramos asks. “It’s exceptional in its outcome, but it’s not exceptional in its trajectory. This is how basic research is done.”

In discussing the meandering nature of basic research, Colón-Ramos poses the question: why does basic science work this way, and why are research projects not more linear? “The reason is because we are explorers. We don’t have the answers.” I find my inner expeditionary warmed at the thought.

The Colón-Ramos lab’s sliver of neuroscience frontier is understanding how synapses and neural connections are formed. In their 2013 paper, “Synapse location during growth depends on glia location,” they investigate how specific synaptic connections are managed during substantial physical growth. Synapses are often formed early in development, but as animals continue to grow the neurons must maintain specific connections. For example, C. elegans undergoes a four-fold increase in growth between larva and adult, yet must maintain the synaptic contacts they established prior to this expansion.

Despite my comments above, I do see great value in C. elegans as a model organism. Genetic manipulations that would be prohibitive in higher order animals–in terms of time, cost, and techniques–are readily available in nematodes. This allows a more granular investigation of synapse formation and glia interaction than in another system.

In this paper, they investigate abnormal synaptic contacts found at a symmetric pair of nematode interneurons called AIY–because its C. elegans and you can name every neuron–that are thought to play a role in integrating different sensory inputs. The aberrant synaptic connections are observed in a mutant known as cima-1, a loss-of-function mutant named for “circuit maintenance.”

Hey, hidden alt text!

Figure 1: (A) Schematic diagram of AIYs (grey) in the C. elegans head. Green marks presynaptic sites. They identify three regions along the AIY neurite: one near the AIY cell body that is devoid of synapses (Zone 1, dashed box); and two regions with substantial synaptic connections (Zones 2, 3). (E,I) Presynaptic regions marked with the protein GFP:SYD-1. (J) Quantification of the AIY presynaptic pattern. The length of the ventral presynaptic regions (Zones 1, 2) increases with age. (K) The length of ventral presynaptic region divided by total presynaptic region is shows the general pattern of AIY synapses.

The output of the AIY neurons is determined very early. Synapses are formed during embryogenesis, as the Colón-Ramos lab observed by tagging the synapses with GFP (Figure 1E,I). While the length of this region of the neuron increases as the animal grows from larval-stage 1 (L1) to adult, the pattern of synapses across this length remains constant in the wild-type (WT) worm (Figure 1 J,K).

Normally “Zone 1” (figure 1A) contains no synapses, but in the cima-1 mutant novel synapses form in this zone as the worm grows larger. This indicates cima-1 is involved in preventing ectopic, unwanted synapses during this post-developmental growth phase. These AIY neurons normally synapse on to a postsynaptic partner, a pair of neurons known as RIA. However, the ectopic synapses in the cima-1 mutant do not contact the RIA neuron. So what is inducing these synapses? It turns out the answer is every neuroscientist’s favorite nonneuronal cell: glia!

In the adult worm, cima-1 is largely expressed in epidermal cells (4A), but not neurons. In cima-1 mutants, expression of functional cima-1 cDNA rescues the normal synaptic pattern, abolishing the ectopic connections, when the cDNA is added to epidermal cell lineages (4E), but not when added specifically to AIY neurons, or to all neurons.

Figure 4: (A) A larval animal displaying the endogenous cima-1 expression. (E) Quantification of tissue-specific rescue. Expression of cima-1 cDNA in AIY, or pan-neuronally does not prevent abnormal synapses. However, expression of cima-1 cDNA in epidermal cells does

Figure 4: (A) A larval animal displaying the endogenous cima-1 expression. (E) Quantification of tissue-specific rescue. Expression of cima-1 cDNA in AIY, or pan-neuronally does not prevent abnormal synapses. However, expression of cima-1 cDNA in epidermal cells does

Thus cima-1 expression is necessary to prevent weird, unwanted synapses as the worm grows larger. However, this cima-1 expression is in epidermal cells, not neurons. These epidermal cells don’t directly contact the AIY neurons. Instead, the ventral cephalic sheath cells (VCSC), which are nematode glia similar to vertebrate astrocytes, are located between the cima-1 expressing epidermal cell and the AIY interneurons (figure 5A).

The cima-1 mutants VCSCs have abnormally long endfeet that extend into Zone 1, where the ectopic AIY synapses form (figure 5M). The wacky endfeet show a strong correlation to the emergence of abnormal synapses. When cima-1 is expressed only in epidermal cells, the normal glia shape is rescued. Thus cima-1 in epidermal cells maintains proper glia morphology during growth.

Figure 5: (A) A cross section EM image of a wild-type animal in the Zone 2 region of AIY. VCSC glia (red) lie between the epidermal cells (purple) and AIY Zone 2 synapses (green). (E, I, M, and Q) Are cartoons of data showing simultaneous visualization of AIY presynaptic sites (green) and VCSC glia (red) in each experiment type and stage. In Q, rescue occurred by expressing cima-1 cDNA in epidermal cells

Figure 5: (A) A cross section EM image of a wild-type animal in the Zone 2 region of AIY. VCSC glia (red) lie between the epidermal cells (purple) and AIY Zone 2 synapses (green). (E, I, M, and
Q) Are cartoons of data showing simultaneous visualization of AIY presynaptic sites (green) and VCSC glia (red) in each experiment type and stage. In Q, rescue occurred by expressing cima-1 cDNA in epidermal cells

So, what does the cima-1 in epidermal cells actually do? It appears to negatively regulate the fibroblast growth factor known as EGL-15(5A). Losing EGL-15 in the cima-1 mutants restores glia morphology and prevents the ectopic synapse formation. Going the opposite direction, expressing excess EGL-15 in wild-type animals, induces the wacky glia and abnormal synaptic sites in the AIY neuron (figure 7L).

Figure 7: Schematic for the interaction between cima-1 and egl-15(5A) in epidermal cells (purple) regulating VCSC glia (red) morphology and AIY synapses (green).

Figure 7: Schematic for the interaction between cima-1 and egl-15(5A) in epidermal cells (purple) regulating VCSC glia (red) morphology and AIY synapses (green).

The Colón-Ramos lab establishes that the cima-1(wy84) allele corresponds to a missense mutation in the unnamed gene F45E4.11–you can’t name everything, even in nematodes, apparently. It codes for a transmembrane protein, belonging to a family of solute-carrier transporters. Although the specific cargo for the CIMA-1 protein is not yet known, the lab hypothesizes that the membrane transporter moves acidic monosaccharides that modify or regulate EGL-15 levels.

Tl;dr this paper identifies the cellular and molecular mechanism for maintaining the distribution of presynaptic sites during C. elegans growth, after synaptic contacts have been made.

If you are interested in hearing more about how C. elegans manage their synapses, come see Dr. Colón-Ramos’ DART seminar, “Molecular mechanisms of synaptic development and function: lessons from C.elegans,” November 17 @ 4:00pm, CNCB.

And don’t miss Journal Club, November 16 @ 5:00pm, when Alie Caldwell will present this paper and discuss Daniel Colón-Ramos’ outreach/science communication work.


  1. Shao et al. “Synapse location during growth depends on glia location” (2013) Cell 154, 337-350.
  2. “Lost in Translation: the Value of Basic Research in Medicine” (2011) TEDxSanJuan  *Bonus: it includes a picture of his adorable triplet daughters scientifically investigating the edibility of sand

Bethanny Danskin is a first-year student in the Neurosciences PhD program at UCSD. She is currently rotating with Dr. Byungkook Lim, and is interested in tracing and manipulating in vivo circuits. She appreciates that frontiers are now knowledge-based, which she can explore from the comfort of her blankets. She can occasionally be found sidewalk-ranting at passersby about the importance of basic science.

The Harvey Karten Story

To learn more about Dr. Harvey Karten–neuroscience Professor Emeritus, member of the National Academy of Sciences, and this week’s seminar speaker–please check out Ashley Juavinett’s NeuWriteSD post, “Birds, Brains, and Boats: The Harvey Karten Story”.

Join us at 4 PM on Tuesday, October 27th, in the CNCB Marilyn Farquhar Seminar Room for what is sure to be an excellent and engaging talk: “Evolutionary connectomics and the origins of the neocortex.”

Mastering the Art of Neuronal Wiring

If you want a reminder of the “big picture” of neuroscience, look no further than Liqun Luo’s website.  He starts off with a couple fun neuro facts, the first of which is that there are 1011 neurons in the human brain.  In other words, we have almost as many neurons as there are stars in the Milky Way. Doesn’t that make you feel kind of special?  But even crazier than that is the number of synapses. On average, each neuron makes 103 synaptic connections with other neurons, for a grand total of 1014 synapses (or a hundred trillion if you’d prefer).  By the time you go past the power of 1012, the internet has a lot fewer suggestions for how to make that large of a number tangible.  The best I could find is that if you stacked a hundred trillion dollar bills, you could reach the moon and back 14 times. The point is we have a LOT of synapses in our brain. So how is it possible for those hundred billion neurons to properly wire those hundred trillion synapses?

Dr. Luo studies this wiring specificity in fruit flies (with a casual 105 synapses) and mice (with a slightly less casual 108 synapses). We know that proper neural circuit assembly requires a spatially and temporally precise chain of developmental events to form precise connections between specific neurons. But what exactly does that entail? In his lab’s recent paper, entitled “Toll Receptors Instruct Axon and Dendrite Targeting and Participate in Synaptic Partner Matching in a Drosophila Olfactory Circuit,” the steps of neural circuit wiring explained so clearly, it could be part of Julia Child’s cookbook…

comic

While the axon guidance part has been well studied, how pre- and post- synaptic binding partners identify each other remains poorly understood.  This led Dr. Luo’s lab to conduct a confocal-based RNAi screen of 278 genes through 768 lines to look for wiring specificity molecules in the Drosophila olfactory system.  With every knockdown, the lab would use look for resulting developmental targeting defects of Olfactory Receptor Neurons (ORN) and Projection Neurons (PN).  Interestingly, they found that after a Toll-6 knockdown, there was a dorsal shift of the VA1d PN dendrites (see Figure 1), which normally arborize at the anterior surface of the antennal lobe, and the VA1d ORN axons, which normally project to the VA1d glomerulus. They also found that the knockdown of Toll-7, another member of the Toll receptor family, led to the medial mistargeting of Va1dPN dendrites and ORN axons (see Figure 1). To confirm the findings of the knockdowns, the targeting of ORN axons was studied in both Toll-6 and Toll-7 null mice and dorsal and medial mistargeting, respectively, were again found (see Figure 1).

Figure 1

Figure 1. Identification of Toll-6 and Toll-7 as Wiring Specificity Molecules in an RNAi Screen. All images are single confocal sections of adult antennal lobes, with magenta showing neuropil staining and other colors showing axons of specific ORN classes and dendrites of specific PN classes as indicated. N is number of antennal lobes tested.

Dr. Luo’s lab conducted an extremely thorough characterization of the role Toll-6 and Toll-7 play in axon targeting, so I’ll just touch on a couple interesting experiments that they did.  The Luo lab wanted to know if Toll-7 acts autonomously on VA1d and DA ORNs.  So to see if the production of Toll-7 in PNs was necessary, they performed an RNAi knockdown using the PN-specific promoter Mz19-GAL4. The Toll-7 knockdown in PNs had no effect on the axonal targeting of the ORNs or the dendritic targeting of the PNs.  However, an ORN-specific knockdown of Toll-7, through the Pebbled-Gal4 promotor, led to VA1d axon mistargeting identical to the pan-neuronal Toll-7 knockdown (see Figure 2).  When the antennal lobes of the ORN-specific Toll-7 knockdown flies were stained with an anti-Toll-7 antibody, Toll-7 staining was no longer seen in the anterolateral glomeruli (see Figure 2).  These findings suggest that the ORNs are responsible for the production of the Toll-7 wiring specificity molecule. Interestingly, some targeting defects in PN dendrites were seen with the ORN-specific Toll-7 knockdown.  As PN dendrites are pre-patterned in the antennal lobe before the arrival of ORN axons, it wasn’t thought their arrival affected dendritic target selection. However, this suggests flexibility in PN dendritic target selection based on ORN axon targeting.  Finally, the Luo lab took advantage Mosaic analysis with a repressible cell marker (MARCAM), a technique they had previously developed, to see if Toll-7 acts autonomously on ORNs.  With MARCAM, they created and tagged a sub-population of ORNs that were Toll-7 -/- while leaving the remaining ORNs Toll-7+/+.  Then, they studied single wild-type ORN axons through a population of Toll-7 null ORNs.  Interestingly, there were no targeting defects in the wild-type ORNs growing in the presence of the mutants—indicating that Toll-7 acts autonomously.

Figure 2

Figure 2. Toll-7 Is Expressed in ORN Axons Targeting Anterolateral Glomeruli and Is Required in ORNs

If hearing about any of these crafty experiments or pioneering genetic tools has left you wanting to know more, come to Dr. Linqun Luo’s talk June 9th at 4pm in CNCB. It might be the last seminar of the series, but certainly not the least!

References:

1. Ward A, Hong W, Favaloro V, Luo L (2015). Toll receptors instruct axon and dendrite targeting and participate in synaptic partner matching in a Drosophila olfactory circuit. Neuron 85(5):1013-28.

2. Innumerable Julia Child Photos


Written by Kelsey Ladt, a (currently) sleep deprived 1st year Neuroscience student in Dr. Subhojit Roy’s lab.