Galectin-1 (Gal1)ad to Reduce Inflammation after Spinal Cord Injury

Dr. Popovich (who may moonlight as a famous NBA coach on the side or just shares the name) has focused his research efforts on the complex interactions between the immune system and the regrowth and regeneration of the spinal cord after injury. In his lab’s recent work in the journal Molecular and Cellular Neuroscience, Dr. Popovich explores the interaction of Galectin-1 (Gal1) on macrophages and astrocytes at the site of injury in the central nervous system (CNS). As many may already know, a longstanding issue in the field is trying to understand why peripheral nervous tissue (PNS) can regenerate while CNS tissue cannot. Before this paper’s publishing, Gal1 was already known to elicit regeneration in the PNS via its effect on macrophages, but it remained an open question what if any were the effects in the CNS. The Popovich lab took a quantitative approach to answering this question by systematically identifying the expression levels of this protein over a four week time course following spinal cord injury. Interestingly, this work demonstrates a strong upregulation of Gal1 at the injury site as compared to uninjured spinal cord both in macrophages and astrocytes. This suggests a potential target of manipulation going forward in an attempt to tilt the immune-axis in the CNS towards a more conducive environment for regeneration.

Galectin-1 is a switch hitting molecule that changes its activity based on its redox state: when oxidized it is a monomer that acts much like a cytokine, but in its reduced form Gal1 dimerizes and subsequently has a stronger binding affinity with lectins. Of interest to spinal cord injury regeneration is this oxidized monomer form of Gal1, which may assist in axon regrowth. The expression of Gal1 was characterized using the full toolkit of molecular biology (western blots/qRT-PCR) along with immunohistochemistry to determine the localization of Gal1 at various time points. The spinal cord injury was formed using a standard method in which a contusion is applied to the spinal cord of an anesthetized rat.

As stated before, a clear increase in the protein Gal1 monomer at the injury site occurs at the 7 and 14 day time points, which is the height of the inflammatory response in spinal cord injury. mRNA was upregulated at the 3 day time point. Further, protein upregulation was confirmed by quantifying immunoreactivity density. The rest of the paper focuses on the localization and cell specific expression of Gal1. Using fluorescent microscopy, the Popovich lab demonstrates Gal1 up-regulation in macrophages and astrocytes in a series of beautiful images, but furthers the paper by quantifying their images rigorously to provide valuable data on where and when Gal1 is being upregulated in the CNS.
=Fig 1
 Figure 1:A comparison of Gal1 and OX42 a macrophage/microglia marker in uninjured (left) and injured (right) spinal cord. Notice the greater expression of Gal1 in macrophages in the injured tissue in e’ and e’’.

One of the issues raised near the conclusion of the paper is what the monomeric Gal1 is actually doing to macrophages. Gal1 may be reducing these macrophages overall inflammatory response or causing them to differentiate into a “reparative” phenotype. The final two figures of the paper demonstrate one aspect of Gal1 modulatory effect on macrophages: the protein seems to reduce their levels of phagocytosis. Using ED1 as a marker for phagocytic activity, the paper demonstrates a reduction in the colocalization of Gal1 and ED1 at 7 days. Further these macrophages seem to contain less phagocytosed lipids as imaged using the Oil Red O (ORO) stain. Running a linear regression between the coexpression of Gal1 and ORO showed a negative relationship between ORO and Gal1 expression. In sum these figure suggests that Gal1 promotes less phagocytosis in inflammatory macrophages.
=Fig 2
 Figure 2:A comparison of Gal1 and ORO (a marker for phagocytosed lipids). Notice the significant negative correlation between Gal1 immuno- positive cells and ORO density. Conversely, r-t represent a sub-population of macrophages that go against the trend and are highly Gal1+ and have high ORO density.

Overall this paper demonstrates a coherent and quantitative approach to the molecular biology of spinal cord injury, and brings clarity to much of the previous work done on Gal1. By stringently observing time points and quantifying not only expression but cellular localization the Popovich lab has provided a wealth of data on Gal1’s role in the immune response to spinal cord injury. It will be interesting to learn how this data is being used to potentially manipulate the immune response in spinal cord injury, perhaps by increasing Gal1’s expression levels at the injury site.

Marc Marino is a first year Neurosciences student currently rotating with Dr. Roberto Malinow. He is currently enjoying the fact that the San Diego Padres look like they were all injected with a bucket of Gal1 (no longer inflamed and terrible). 


Gaudet AD, Sweet DR, Polinski NK,Guan Z, Popovich PG. Galectin-1 in injured rat spinal cord: Implications for macrophage phagocytosis and neural repair. Molecular and Cellular Neuroscience. 2015; (64):84-94. doi: 10.1016/j.mcn.2014.12.006

Calcium translates between the electrical and chemical languages of the brain

The nervous system is incredibly fast. A dog runs across the street in front of you, and your foot instinctively jumps to the brake pedal. All of this occurs in a fraction of second, often before you even have time to fully process the scene in front of you (was it a dog? or a raccoon?).

The brain is fast because electrical signals in the brain travel fast (over 200 miles and hour, in some cases). And it isn’t just reflexive actions, like slamming your brakes, or lunging to catch a falling glass. We can make complex decisions in the blink of an eye, especially with a little practice and training:

And yet, our decisions and actions, which exist only for a brief instants of time, are captured as memories that can endure for decades.

Electrical signals provide speed, but the brain relies on biochemical reactions as a substrate for slower, more permanent processes like learning and memory. While a discrete electrical impulse typically lasts a few milliseconds, a newly synthesized protein can last for minutes, hours, days, or even years before being degraded. A fundamental question is how these two fundamental languages of the brain — electricity and biochemistry — communicate to each other, despite their widely varying time scales.

Dr. Richard Tsien has been studying this question for many years, and certainly ranks among the very top contributors to this field. His principal insight was that calcium ions participate in both electrical and biochemical signaling, allowing brain cells transmit information from rapid (electrical) signals to slow (chemical) processes that store memories and re-calibrate the system.

Back in 1985, Tsien was the first to characterize the various types of voltage-gated calcium channels [1]. These are proteins that form holes/pores in in the cell’s membrane, and open rapidly in response to an electrical potential. When these channels open, calcium ions (Ca2+) flow into the cell from the extracellular space. These ions carry electrical charge, but also interact with a stupefying number of biochemical pathways that regulate gene expression, protein synthesis and degradation, molecular trafficking, the release of hormones and neurotransmitters, and much more.

Dr. Tsien has tirelessly and meticulously chased down many of these calcium-activated pathways over the years. Far too many to enumerate in this brief summary. But the back-and-forth interplay between electrical and biochemical signaling emerges as a common theme of his work. For example, Tara Thiagarajan, Dr. Tsien, and others [2,3] discovered that chronically blocking electrical activity induces a biochemical response from neurons, causing them to increase the strength of their excitatory connections to other neurons. Calcium plays a pivotal role in this response, as outlined in the flow chart below:

flowchart

In this case, calcium works a bit like the thermometer in a thermostat: a drop in calcium signals that activity levels have dropped too low, and turns on the “heat” (excitatory connections between neurons) to compensate. In other work, Tsien and colleagues have studied the calcium-activated pathways that reconfigure synaptic connections to store long-term memories [4], and tune gene expression [5].

Given the privileged position of calcium between the electrical and chemical languages of the brain, it is not surprising that many neuropsychiatric disorders are associated with dysfunction in calcium signaling. For example, progressive memory loss in Alzheimer’s disease is associated with a slow creep in internal calcium levels [6]. Prescribing memantine (Namenda), one of the two approved classes of drugs for Alzheimer’s, can sometimes slow the rate of memory loss by partially blocking calcium ion flow into neurons. While most of Dr. Tsien’s work is focused on unraveling basic biological mechanisms, his lab has also published papers on the role of calcium dysregulation in schitzophrenia, Timothy syndrome, ataxia, and Down Syndrome.

If you are interested in diving into the details of this work, come see Sam Scudder discuss a recent paper from the Tsien lab (6pm, Monday journal club), which examines how calcium signals in distal dendrites regulate gene expression from afar:

Ma et al. γCaMKII Shuttles Ca2+/CaM to the Nucleus to Trigger CREB Phosphorylation and Gene Expression (2014). Cell 159(2): 281-294.

And be sure to stop by CNCB to see Dr. Tsien’s talk on April 7th at 4pm, in the CNCB auditorium.


Alex Williams is a first-year student in the Neurosciences PhD program at UCSD. He applies computational and theoretical techniques to study the molecular mechanisms of neural plasticity and stability. He tweets @ItsNeuronal. Also, Running. Lifting. Burritos.

Do you understand the words that are coming out of my mouth?

I watched Interstellar last month under the stars on campus here at UC San Diego. Several weeks later, the words of this Dylan Thomas poem still resonate within my mind. How is my brain able to understand and remember this spoken message? When I have reached the wise old age of the wonderful Michael Caine, how will my auditory cortex have changed its ability to process these words? Dr. David Woods of UC Davis’s Human Cognitive Neurophysiology Laboratory at the VA Medical Center in Martinez, California is in the business of evaluating age-related changes in speech perception and verbal memory.

Randy Glasbergen, http://glasbergen.com

Age-related decline in verbal processing is in part due to changes in central auditory processing, but little is known of how healthy aging affects the human auditory cortex. Dr. Woods’s laboratory seeks to evaluate age-related changes in speech perception and memory in groups of young and older subjects and correlate them with structural and functional changes in human auditory cortex using several magnetic resonance imaging (MRI) techniques. Dr. Woods proposes to first establish baseline behavioral measures in speech reception thresholds in noise and auditory verbal short-term memory, then investigate age-related changes in auditory cortex surface structure using high-resolution T1-weighted MRI combined with cortical surface mapping. This allows for the analysis of auditory cortex thickness, area, and curvature. Diffusion tensor imaging, a functional MRI (fMRI) technique used to look at water diffusion, is applied to measure age-related changes in neuropil density and fiber connectivity; this connectivity will then be correlated with prior measured changes in cortical thickness and curvature. Behavioral measures of aging subjects can lend insight into how anatomical changes are associated with behavioral impairment. Lastly, Dr. Woods ties all this structural and behavioral data together with functional organization in the auditory cortex. Using fMRI techniques, he examines the automatic and attention-dependent processing of simple tone stimuli and consonant-vowel-consonant (CVC) syllables. Comparison of tone and CVC processing will elucidate the regions of auditory association cortex that show specific activations to different auditory stimuli and the neural circuits engaged in speech processing.

Screen Shot 2015-03-29 at 12.58.25 PM

Fig. 1 | (A) Cortical surface locations of activation peaks associated with phonological processing of speech sounds from a meta-analysis by Turkeltaub and Coslett (2010). Red dots indicate cortical surface locations of the reported coordinates on the left hemisphere of 60 individual subjects, while blue dots indicate those on the right hemisphere. Cyan cross indicates median location of activations in the left hemisphere; yellow cross indicates that of right. (B) Cortical surface locations of regions responding to CV syllables in comparison to bird song elements, musical instruments, or animal sounds, from Leaver and Rauschecker (2010). See (A) for color associations.

Previous work by Dr. Woods has sought to use population-based cortical-surface analysis of fMRI data to characterize the processing of CVCs and spectrally matched amplitude-modulated noise bursts (AMNBs) in human auditory cortex. Using average auditory cortical field locations (Fig. 1) defined from tonotopic mapping in a previous study, Woods et al. found that activations in the auditory cortex were defined by two stimulus-preference gradients.

Fig. 2 | A schematic map of auditory cortical fields showing stimulus preferences for consonant-vowel-conosonants (CVCs - indicated in ORANGE) and amplitude-modulated noise bursts (AMNBs - indicated in GREEN). The ratio of orange to green reflects relative magnitude of activations with respect to each stimulus type.

Fig. 2 | A schematic map of auditory cortical fields showing stimulus preferences for consonant-vowel-conosonants (CVCs – indicated in ORANGE) and amplitude-modulated noise bursts (AMNBs – indicated in GREEN). The ratio of orange to green reflects relative magnitude of activations with respect to each stimulus type.

Medial belt auditory cortical fields preferred AMNBs (Fig. 2 – green fields), while lateral belt and parabelt fields preferred CVCs (Fig. 2 – orange fields). This preference extends to the core cortical fields, as shown by the medial regions of primary auditory cortex (Fig. 2 – A1) and the rostral field preferring AMNBs and lateral regions preferring CVCs.

Fig. 3 | Quantification of activations by color and brightness. (A) Mean percent signal change of activations coded by brightness. Colors shows stimulus preference (CVC = red, green = AMNB). Yellow regions are activated by both stimuli. (B) Auditory cortical field locations projected onto average curvature map of the superior temporal plane.

Fig. 3 | Quantification of activations by color and brightness. (A) Mean percent signal change of activations coded by brightness. Colors shows stimulus preference (CVC = red, green = AMNB). Yellow regions are activated by both stimuli. (B) Auditory cortical field locations projected onto average curvature map of the superior temporal plane.

Woods, et al. also found a difference in magnitude of activation amongst the different field groups to the two stimuli. Anterior ACFs showed smaller activations (Fig. 3 – dullness of anterior fields), but more clearly defined, singular stimulus preferences (Fig. 3 – only green or red in anterior fields, rather than mixing) than posterior fields (Fig. 3 – note brightness and yellow color in posterior fields).

Fig. 4 | Effects of attention on activation magnitudes in different field groups. UA = unimodal auditory, BA = bimodal auditory attention, BV = bimodal visual attention.

Fig. 4 | Effects of attention on activation magnitudes in different field groups. UA = unimodal auditory, BA = bimodal auditory attention, BV = bimodal visual attention.

Attention significantly enhanced responses throughout the auditory cortex and within every field group, indicating that attentional enhancements to CVCs and AMNBs had similar magnitudes and distributions over auditory cortex (Fig. 4 – mean % of attentional enhancement indicated). This demonstrates that preference gradients are unaffected by auditory attention, which suggests that the preferences of each auditory cortical field reflects automatic rather than attention-dependent processing of difference sound features.

The above investigations are only a brushstroke on Dr. David Woods’s eclectic experimental canvas. To hear more about his adventures in verbal processing and memory, come join in on a lively journal club presentation by Erik Kaestner, a graduate student in the UCSD Neurosciences Graduate Program, on Monday, March 30th at 5:30 PM in Pac Hall 3502. Then, come hear Dr. David Woods himself speak:

Screen Shot 2015-03-29 at 2.02.38 PM

Thanks for listening!

David L. Woods,Timothy J. Herron, Anthony D. Cate, Xiaojian Kang, and E. W. Yund. Phonological Processing in Human Auditory Cortical Fields. Front Hum Neurosci. 2011; 5: 42. Published online 2011 Apr 20. doi:  10.3389/fnhum.2011.00042


Eulanca Y. Liu is a first year graduate student in UCSD Neurosciences and third year in the UCSD Medical Scientist MD/PhD Training Program. When not studying the physiological basis of fMRI in the lab of Dr. Rick Buxton, she enjoys jet-setting, calligraphy and graphic design, opera/theatre/dance, a day at the museum, great single-malt scotch, a hot caffeinated beverage, and dabbling. She will spend the weekend watching the Formula 1 Malaysia Grand Prix whilst writing this post. She can be reached at eyl015 at ucsd.edu and read at medium.com/@ZanzibarByCar. Feedback is welcome!

How is your auditory cortex processing the engine sounds of this Ford Fiesta ST?

Acid on the Tongue: How Taste Cells Mediate Sour Transduction

Imagine what your childhood would have been like if you could never experience the tantalizing taste of a WarHead or the painfully-pleasing sensation of biting into a sour lemon? What if you suddenly couldn’t appreciate the sour beer filing your mug on a warm spring night? While most of our neurons are contained within our brain, life would certainly not be the same without the complete set of cells comprising our nervous system.

Dr. Emily Liman researches the physiological basis of perceiving sensations such as pain and taste. Recently, Dr. Liman revealed that sour transduction is mediated by a proton current enriched on the apical surface of taste cells. Strong acids can activate this proton current, causing the cell to fire a burst of action potentials and generate an acid-evoked inward current. Previously, the PKD2L1/PKD1L3 heterodimer was identified as a marker for sour taste cells, however the function of this channel is not necessary for perceiving sour taste. In the current study, Dr. Liman used transgenic mice, calcium imaging and electrophysiological recordings to identify the elusive mechanism of sour transduction.

Do you remember stuffing your mouth with a fistful of sour patch kids or gummy worms? A variety of acids were responsible for the delicious yet tarty taste you experienced. Similarly, Dr. Liman demonstrated that stimulating PKD2L1-expressing cells with hydrochloric acid (HCl; pH 5) or acetic acid (HOAc; pH 5) induced a rapidly inactivating inward current in those cells (Fig. 2A, B). This cellular response was even present in sour taste cells lacking functional PKD2L1/PKD1L3 channels (Fig. 2C), supporting that an alternative mechanism contributes to sour transduction. Since this acid-response must be mediated by some molecular mechanism, a variety of pharmacological agents were used to alter the concentration of Na+, Ca2+ or Cl, testing the possibility that a cation or anion mediates the cell’s response to acid. However, these manipulations revealed that a sour taste cell’s response to an acid is not mediated by an ionic conductance (Fig. 2C). Instead, H+ is the likely charge carrier allowing a sour taste cell to respond to extracellular acidification.

Liman Fig. 2

Interestingly, pharmacological agents such as Bafilomycin, Cd2+, Desipramine and Amiloride, which block proton currents, had no effect on reducing the acid-evoked inward current. Only Zn2+, which is known to block many channels including the proton channel, Hv1, could inhibit the acid-response in sour taste cells (Fig. 3D).

Liman Fig. 3

Additionally, Dr. Liman used UV uncaging of NPE-caged protons and Ca2+ microfluorimetry to measure how a cell might respond to extracellular acidification. In this manner, a proton current was simulated by uncaging protons at the apical surface of the sour taste cell. Calcium imaging showed that sour taste cells responded to the uncaged protons with a burst of action potentials, a large inward current, and an elevation of intracellular Ca2+ (Fig. 5B).

Liman fig. 5

While the specific mechanism underlying the proton current remains a mystery (a proton channel or transporter are the likely candidates), I’ll personally sleep better at night knowing that I’ll wake up with my sour taste cells intact and ready to enjoy a warm cup of tea infused with freshly cut lemon.

Come and hear Dr. Emily Liman’s talk Taste and the single cell: Uncovering mechanisms of sour transduction, Tuesday, March, 17th at 4pm in the Center for Neural Circuits and Behavior Marilyn C. Farquhar Conference Room.

Bankole Aladesuyi is a first-year Neurosciences student currently rotating with Dr. Xin Jin. When not reminiscing about a childhood spent eating candy for lunch, he enjoys playing soccer, making music and reading science fiction.


Chang, R. B., Waters, H., & Liman, E. R. (2010). A proton current drives action potentials in genetically identified sour taste cells. Proceedings of the National Academy of Sciences107(51), 22320-22325.

The Hidden Visceromotor Maps in Motor Cortex

This week UCSD is proud to feature the work of Peter Strick, Chair of Neurobiology at the University of Pittsburg. Dr. Strick’s lab investigates the circuitry of the brain, focusing on cortical motor areas as well as the basal ganglia and cerebellum.

Motor cortex has long been known to contain somatotopically organized motor “homunculi,” regions with point-for-point correspondence between the body and its representation in the brain. Recent work in Dr. Strick’s lab has discovered that primary motor cortex (M1) and rostromedial motor area (M2) also contain somatotopic map of visceromotor functions.

The autonomic nervous system is the functional subdivision of the central nervous system responsible for unconscious monitoring and control of visceral organs. It allows us to maintain homeostasis despite constant changes in temperature, food intake, and physical activity. This system is not exclusively reactionary, suggesting possible allostatic regulation in which higher brain centers generate anticipatory activity to prevent imbalances before they occur.

In this paper Strick injected rats’ kidneys with a modified rabies virus to identify the cortical origin of their autonomic innervations. Rabies virus is a useful tool for tracing circuits because it travels exclusively in the retrograde direction (from a postsynaptic neuron into the presynaptic neurons that contact it) and does so in a time-dependent fashion (infection spreads at a constant rate). Varying the time the virus is allowed to spread can tell us which neurons are involved in each stage of a circuit: first-order neurons (those directly innervating the kidneys) are infected first, followed by second-order, and so on and so forth.

F1.large

Figure 1. Retrograde transneuronal transport of rabies virus through neural circuits that innervate the kidney.

Contrary to classical models in which cingulate and insular cortex provide the primary cortical control of visceral function, the first cortical neurons infected were located overwhelmingly in M1 or M2. Further, these neurons were concentrated heavily in areas representing the trunk, consistent with the known spinal location of renal sympathetic preganglionic neurons. These results suggest that sympathetic visceromotor control originates in motor cortex and is organized in a somatotopic manner similar to motor control.

F3.large

Figure 2. The first neurons to reach cortex are located most densely in the trunk representations of M1 and M2.

The discovery of visceromotor control in motor cortex has important implications. M1 and M2 have different roles in the control of conscious movement; while M1 is involved in the execution of movement, M2 is critical to the planning and preparation of movements. If visceromotor control parallels this distinction, M2 may provide the anticipatory processing necessary for allostatic regulation. Further, interconnectivity between M1 and the basal ganglia (BG) may explain why some Parkinson’s patients (a neurodegenerative disease that affects the BG) experience autonomic dysfunction (symptoms often assuaged by deep brain stimulation of BG).

If you are interested in this and other work by the Strick lab, join us tomorrow at 4:00 at the UCSD Center for Neural Circuits and Behavior, where Dr. Strick will be speaking about unraveling the “brain-body” connection.

Levinthal DJ, Strick PL. The motor cortex communicates with the kidney. J Neurosci 32: 6726–6731, 2012.

Michael Metke is a first year graduate student in the Neuroscience program at UCSD.

Joshua Sanes: Regulation of Retinal Repulsion (and Rainbows)

This week, the Dart Neurosciences Seminar hosts Joshua Sanes, a professor of molecular and cellular biology at Harvard. Dr. Sanes’ scientific interests revolve around key fundamental questions in systems neuroscience: How do circuits form in development, how do they change over maturity, and how do they function to process signals in adulthood? To this end, Dr. Sanes has spent many years probing the retina as a model for the rest of the brain. In particular, he has been a pioneer in identifying retinal subtypes, probing their connectivity dynamics, and manipulating different parts of the circuit to evaluate their importance.

Screen Shot 2015-03-02 at 1.14.08 PM

Recently, Dr. Sanes and his lab members have focused on molecules that play in role in forming these synaptic connections- namely the cadherin superfamily. It has been known that in the retina, cells of the same subtype are spaced apart such that few neighboring cells are of the same subtype. However, a mechanism for the formation of such a mosaic was unknown. In a 2012 Nature paper, Jeremy Nay, Monica Chu, and Joshua Sanes identified two transmembrane proteins found in a subset of retinal cells that regulate positioning of Starburst Amacrine Cells (SACs) and Horizontal Cells (HCs) during development. In this paper, the group showed that these two proteins (MEGF10 and MEGF11) act as repulsive ligands, where cells that express these proteins will repel other cells expressing the same protein. They further showed that the protein is required as part of both the ligand and receptor sides, a homotypic interaction. This results in ‘exclusion zones’ around a particular cell, creating the mosaic spacing seen between cells of the same subtype in the retina. While the previous hypothesis was that each subtype had a unique ligand-receptor signal, these results show how two proteins work cooperatively to create a repulsive mosaic for horizontal cells, and how one of those proteins (MEGF10) also acts on SACs. As these two cell types are in different layers, it opens up the possibility that these mosaic ligands are layer specific, reducing the necessary diversity to create a structures, mosaic map in the retina.

Screen Shot 2015-03-02 at 1.14.27 PM

What is the importance of elucidating these mechanisms? As Dr. Sanes mentions frequently, the retina is an easy-to-access proxy for other brain areas. There are numerous examples of neuronal arrays throughout the CNS. This first evidence of homotyptic repulsion could lead to an understanding how uniformity is established in the brain.

Since the 1970s, Dr. Sanes has approached the question of neural synapses in different ways. From neuromuscular junctions in frogs, to discovering and cloning the protein lamin B2, he has tried to explore the molecules that form the junction, and how disturbing the system affects the synapse. With Jeff Lichtman, an expert in live imaging, Dr. Sanes hopes to continue probing the nature of cell-to-cell connections and synapse formation and change over time. In recent years, this duo has gained fame for the creation of ‘brainbow’ mice, lines of transgenic mice that express varying amounts of three fluorophores in each cell, creating a veritable rainbow of colors that allows visual separation of each cell and its axon from neighboring cells. The entire field of neuroscience will benefit as Dr. Sanes continues to push boundaries in the fields of development of retinal architecture, synapse formation, and circuit evolution.

brainbow2.2

If you’d like to find out more about these studies, come listen to Dr. Joshua Sanes on Tuesday, March 3rd in the Center for Neural Circuits and Behavior Marylin C. Farquhar Conference Room!

(Sahil Shah is an M.D./Ph.D. student in the lab of Jeffrey Goldberg. He is studying protein synthesis and transport in the retinal ganglion cell as it relates to aging and disease.)

Kay, J. N., Chu, M. W., & Sanes, J. R. (2012). MEGF10 and MEGF11 mediate homotypic interactions required for mosaic spacing of retinal neurons. Nature483(7390), 465-469.

Dr. Naoshige Uchida: Different Types for Different Likes

Listen to the post and follow along!

Who makes decisions?

How do we decide?

To run in fear, or hide?

Reward-based circuitry must be,

A part of our machinery!

Dr. Uchida has a lab,

At Harvard, you know, Boston, Mass,

They dive into the mouse’s brain,

Specifically the VTA.

If acronyms are scaring ya:

Ventral tegmental area.

Let’s take a look at what they did,

Examine their experiments.

|==========================|

At first they taught a mouse to tell,

Apart rewards based just on smell.

Sometimes a big reward, some small,

Sometimes no water came at all.

Sometimes an air puff to the face,

The mouse must differentiate.

It does – and licks more when it knows,

That water through lick port will flow.

Uchida Figure 1

They then recorded from some cells,

To see if they could really tell,

Which ones respond to which CS,

And which prefer reward the best.

Which cells might fire differently,

In groups or independently,

And their results – it’s no surprise,

Were elegant, I’ll summarize:

They clustered cells in groups, you see,

Type I, type II, and even III.

The types have different properties,

And different n-t-m release.

Type I spikes to rewarding cue,

More tonically spike cells Type II,

Type III cells are more limited,

And seem to be inhibited.

Uchida Figure 2

They optogened some channel rho,

To see how clean their data shows,

Each type of cell can be predicted,

By the n-t-m ejected.

And indeed results were clean,

Type I cells release dopamine,

And type II cells release the massive,

Gamma-Aminobutyric acid.

Uchida Figure 3

Wonderful! So now it’s known,

Each cell has functions of its own,

“And now,” the lab group said, “let’s show,

How they predictive error code!”

“Let’s signal that a big reward,

Is on its way, but just before,

The water comes we’ll just be fronting,

One of ten times the mouse gets nothing!”

“Oh no!” announced the type I cells,

“There’s no reward, dude, what the hell?!”

After the US time was finished,

They saw type I was quite diminished.

In contrast type II neurons went,

So crazy during punishment.

Rewarding stim plus puff of air,

The type II neurons said “Oh yeah!”

Uchida Figure 4

So now we know more things about,

Reward-based circuits in the mouse,

The push and pull of different types,

The dopamine and GABA life.

Come listen to Uchida speak,

He’ll have the answers that you seek.

On these experiments, and more,

Tuesday, CNCB, at four!

|==========================|

Uri Magaram is a first year graduate student in the UCSD Neuroscience program.  He is currently rotating in the lab of Jeff Isaacson.

Email: umagaram@ucsd.edu

Cohen JY, Haesler S, Vong L, Lowell BB, Uchida N. Neuron-type-specific signals for reward and punishment in the ventral tegmental area. Nature. 2012 Jan 18;482(7383):85-8. doi: 10.1038/nature10754.

Dr. Garret Stuber: Driving emotions with light

Why do we do what we do? What agent drives our decision-making? What motivates one person to work hard and another to stay in bed all day? These questions have no simple answers (if any at all), but neuroscience researchers believe they can access the underpinnings of motivated and reward-seeking behavior by studying the neural circuits that drive such behaviors. The lab of Dr. Garret D. Stuber at University of North Carolina at Chapel Hill does just that.

GStubFig. 0 | Dr. Garret D. Stuber with electrophysiology rig

Using techniques in-vitro (after an animal has been sacrificed for study) and in-vivo (while the animal is alive and behaving), scientists in the Stuber lab try to identify and isolate neural circuits that guide behavior in health and disease. In this post, we will look at a recent publication examining a particular circuit implicated in integrating a range of opposing emotional states and guiding behavior. Specifically, the authors focus on neuronal projections from the bed nucleus of the stria terminalis (BNST) to the ventral tegmental area (VTA), by first examining the electophysiological properties of this circuit, and then manipulating the circuit in-vivo to confirm that their predictions about its function can be observed in an awake, behaving mouse.

The BNST is considered to be a part of the extended amygdala. Many people readily recognize the amygdala as an important brain center for emotional processing (in fear and anxiety) and, naturally, decision making related to those emotions. The VTA is a group of neurons that include dopaminergic projections to various brain regions, and is functionally implicated in both reward and aversion. Thus, studying the connections between the BNST and the VTA could shed light on the neural substrates for integrating differing emotional states and driving anxiety-related behavior. Elucidating these mechanisms could have marked implications for diseases such as addiction, anxiety, and depression.

Since neurons in the brain can project to many different areas, the authors first had to ensure that they could identify neurons that reside in the BNST (particularly the ventral BNST: vBNST) and project to the VTA. To do this, the authors used a technique called optogenetics, wherein special ion channels that can be activated by light are expressed in neurons of interest. To ensure and categorize functional connections between the two areas, the authors used a clever trick: they stimulated, sequentially, with light, the cell bodies in the vBNST and their axon terminals in the VTA while recording from the vBNST. Because action potentials have no directional preference, but cannot travel through sections of axon that are in a refractory period (having just propagated an action potential), functional connections stimulated under precise conditions would induce “action potential collision.” The recording schematic is illustrated in Fig 1b, with an example recording in Fig 1c, and Fig 1d shows the attenuation of the antidromic spike (propagating backwards along the axon), indicating “spike collision”.

Figure 1 Stubber

Because the behavior of neurons projecting from vBNST to VTA is not homogenous, the authors next separated the population into a subset of excitatory projecting neurons (glutamatergic) and inhibitory projecting neurons (GABA-ergic). They did this by using promoters for Vglut2 or Vgat, which express differentially in the two populations, and recorded EPSCs and IPSCs in VTA neurons, respectively. To examine the significance of these two populations, the authors recorded from each type of neuron during aversive foot-shock stimuli and found that excitatory glutamatergic neurons increased their firing during foot shocks and subsequent relevant cues, while inhibitory neurons decreased their firing to aversive foot shock (Jennings et al, 2013).

To test whether this observed pathway would be sufficient to drive anxiety-related behaviors, the scientists next activated these subsets of neurons in-vivo. By expressing ChR2 in excitatory vBNST neurons, they could stimulate the vBNST-VTA pathway with light pulses when the mouse was in a specific context, in this case one of two chambers. They observed that mice significantly avoided the chamber paired with the photostimulation, indicating an anxiety-like response. To corroborate this observation, an injection of DNQX, which blocks glutamate activity, abolished this avoidance. Furthermore, mice that were photostimulated in an open-field test spent significantly more time in corners of the observation table versus control mice (Fig 4, Jennings et al 2013).

Figure 4 Stubber

As activating the excitatory pathway to VTA resulted in avoidance and anxiety-like behavior, the scientists also tested whether there would be behavioral consequence to activating the analogous inhibitory pathway. Indeed, stimulating the inhibitory pathway resulted in significant place preference to the chamber that was paired with the stimulation; again, this result was abolished by GABAzine, a GABA inhibitor. Mice even nose poked to receive the photostimulation, indicating a reward-related behavior. In fact, this pathway was successful in alleviating anxiety-related freezing produced by aversive foot shocks, providing strong evidence that these vBNST-VTA neurons are strongly related to anxiety or anxiety buffering (Jennings et al 2013).

This study by Dr. Stuber’s lab provides strong evidence for a neural pathway that regulates anxiety and reward-seeking behavior. Identifying these pathways could be a promising lead towards identifying physiological disruptions that cause anxiety, depression, and addiction. If you’d like to find out more about these experiments (and perhaps other, even more recent studies), come listen to Dr. Garret D. Stuber on Tuesday, February 3 in the Center for Neural Circuits and Behavior Marylin C. Farquhar Conference Room!

Uri Magaram is a first year graduate student in the UCSD Neuroscience program.

Jennings, J. H., Sparta, D. R., Stamatakis, A. M., Ung, R. L., Pleil, K. E., Kash, T. L., & Stuber, G. D. (2013). Distinct extended amygdala circuits for divergent motivational states. Nature496(7444), 224-228. doi:10.1038/nature12041
 

Same diagnosis, different working brains: Rethinking functional connectivity in autism

In recent years, there has been an explosion of interest in the neuroscience of autism spectrum disorder (ASD), and much of this research has targeted how activity in the autistic brain differs from that in the neurotypical brain. However, delving into this literature can be frustrating and immensely confusing; a quick PubMed search for “autism functional connectivity” returns over 300 results with titles describing connectivity in ASD in frequently contradictory terms such as “altered”, “preserved”, “disrupted”, “reduced”, “hyper”, etc. This is somewhat unsurprising given the well-known heterogeneity of ASD(s), but particularly for the purposes of improved diagnoses, interventions and general understanding of the disorder, it would be advantageous to be able to parse out some core characteristic(s) of the disorder.

As chance (and luck) would have it, this week’s speaker, Dr. Marlene Behrmann from Carnegie Mellon University, has been making quite a few headlines in the last week for doing just that. In a paper published in Nature Neuroscience on Jan. 19th, she and her co-authors propose that rather than a particular pattern of over- or under-connectivity being a distinguishing feature of autism, a core characteristic of ASD is having an “idiosyncratic brain”. In other words, whereas neurotypical adults tend to display relatively similar and stereotyped patterns of functional connectivity, adults with ASD deviate from this pattern in an inconsistent, “idiosyncratic” way.

What led Dr. Behrmann and her co-authors to this conclusion was an initial observation that compared to control brains, their ASD group as a whole demonstrated what the authors call a “regression to the mean” effect. This observation came from an analysis of resting-state fMRI scans (in which BOLD signal changes are measured in the absence of a task and taken as an indication of spontaneous activity, revealing functional networks through correlated patterns of activation) from five different datasets with a total of 73 controls and 68 ASD subjects. In areas of the brain with highly correlated activity between hemispheres (“homotopic interhemispheric connectivity”) in the control group, the ASD group demonstrated reduced connectivity; but where there was less interhemispheric connectivity in the control group, there was comparatively increased connectivity in the ASD group.

Although there are a number of possible explanations for this phenomenon, the one which Dr. Behrmann and her co-authors’ data best support is that there is substantial variance within the ASD group in terms of their patterns of functional connectivity, whereas control subjects are more similar to each other. The following schematic figure from their paper illustrates this nicely:

nn.3919-F2

Figure 1: A schematic of how “regression to the mean” in ASD could arise from spatial distortions of interhemispheric functional connectivity

As one can see, when three hypothetical ASD subjects each have areas of strong (yellow/orange) and weaker (blue) interhemispheric connectivity but these areas are spatially inconsistent, their group average would be attenuated in each direction toward a mean level of connectivity.

And in fact, this is precisely what Dr. Behrmann and her co-authors observe. In each of their five datasets, individual control subjects’ patterns of homotopic interhemispheric connectivity are more correlated with each other (as demonstrated by more red/yellow in Figure 2a) whereas ASD subjects seem to be less correlated with each other (more blue; quantified in Figure 2b).

nn.3919-F3

Figure 2: In all 5 datasets, there was greater inter-subject similarity in patterns of interhemispheric functional connectivity in the control than in the ASD group

Interestingly, the degree of autistic subjects’ deviation from the control group’s mean connectivity pattern was modestly correlated with behavioral measures of ASD as indicated by Autism Diagnostic Observation Schedule (ADOS) scores. In particular, there were significant correlations between total ADOS or ADOS communication scores and distortion index (i.e., the within-subject variance of interhemispheric connectivity differences) such that subjects with more severe symptoms had more idiosyncratic connectivity profiles. Moreover, they found similarly variable patterns of functional connectivity that deviated from the more stereotyped control patterns when they also looked at heterotopic interhemispheric connectivity (connectivity between different regions across hemispheres) and intrahemispheric connectivity in each hemisphere, although these connectivity deviation measures did not correlate as strongly with the behavioral measures. Nevertheless, Dr. Behrmann and colleagues’ data support their proposal of “functional idiosyncrasy” as a signature of individuals with ASD.

At first, one might say, “Ok, so functional connectivity in autism varies by individual; that seems obvious, what’s the big deal?”. I would argue that a major implication of this study is that it provides a new framework for thinking about functional connectivity in autism. Not only does it help clarify many of the discrepancies in previous studies’ findings, it helps guide future studies by focusing not only on how two groups (i.e., ASD and control) are different from each other but also on how the within-group variances differ between groups. Instead of the highly variable nature of ASD being merely a nuisance for statistical tests, it is precisely the variability in deviation from the “control template brain” which sets it apart. This also raises some interesting questions for how “canonical” vs. “idiosyncratic” connectivity profiles develop. For instance, the authors suggest that the functional idiosyncrasies in autism could arise (at least in part) from autistic individuals’ abnormal interactions with their environment throughout development, whereas neurotypical individuals who (presumably) interact with their environment in a more typical way would have similar functional connectivity patterns reinforced. This is an interesting hypothesis that could be addressed by looking for similarly variable and deviant connectivity patterns in some other clinical populations.

This is likely to be the primary topic of Dr. Behrmann’s lecture given this paper’s recent publication and the fact that her talk is titled “Alterations in canonical cortical computations in Autism”. However, Dr. Behrmann has been extremely influential in other realms of cognitive neuroscience research as well. She also studies aspects of complex visual processing such as object recognition, mental imagery, spatial attention, face processing and more, and employs a wide range of methods including behavioral measures in brain-damaged patients, functional neuroimaging, and computational modeling (you can find links to her MANY publications on her website – definitely worth the read!). Dr. Behrmann is both a wonderful speaker and a wonderful scientist, so don’t miss her on Tuesday, January 27th at 4pm in the Center for Neural Circuits and Behavior Marilyn C. Farquhar Conference Room!

Megan Kirchgessner is a first-year Neurosciences graduate student currently rotating with Dr. Eric Halgren. When not in lab or in class, she is probably running around the Torrey Pines Gliderport and getting distracted by the sunset, listening to weird music and pretending to be indie, eating excessive amounts of carbs, alternating between watching independent films and Will Farrell comedies, or stalking the La Jolla Cove sea lions.


Hahamy, A., Behrmann, M., & Malach, R. (2015). The idiosyncratic brain: distortion of spontaneous connectivity patterns in autism spectrum disorder. Nature Neuroscience. Advanced Online Publication. doi:10.1038/nn.3919

Yeast: good for beer, pizza dough, and drug development?

Dr. Lindquist’s Lab, using yeast, has taken an innovative approach to study and develop treatments for synucleinopathies, which are diseases that exhibit the toxic buildup of α−synuclein protein. These diseases include Parkinson’s Disease and dementia with Lewy bodies, which have increased in prevalence as our population ages. The use of yeast as a model to look at the underlying genetics of the pathology has several benefits: 1) The biological processes involving α−synuclein are evolutionary conserved from yeast all the way to humans 2) The ability to do whole genome unbiased screens in a simple organism with a uniform genetic and epigenetic population 3) The lack of confounding factors such as different cell types and cell to cell connections 4) The ability to test many small molecules quickly and determine their mechanism of action. To be clear, Dr. Lindquist is not claiming to be modeling human disease, but rather using the genetic and molecular tools in yeast to more quickly and effectively bring treatments back into human neural models.

The Lindquist lab has recently published a paper in Science (Tardiff et al., 2013) that uses yeast to investigate the genetic underpinnings of synucleinopathies. The authors first used an unbiased screen, a method that does not attempt to target a particular pathway or protein but rather looks at cellular level readouts such as cell death or cell growth to find molecules that are cyto-protective in yeast. The authors then figured out the mechanisms of action using the powerful genetic tools of yeast. The drug of focus here was NAB2, which had been previously shown to prevent α−synuclein toxicity in other models. Because NAB2 in yeast slows growth, this phenotype was used to screen for mutants that were unaffected by the drug. This screen implicated 12 closely related genes. Subsequently after knocking these genes out one at a time, Rsp5 (NEDD4 in human) was deemed the target for NAB2. The drug was then tested in yeast expressing human α−synuclein, which demonstrated NAB2 could rescue α−synuclein expressing yeast, but could not rescue a Rsp5 mutant yeast strain from α−synuclein toxicity. Thus the authors confirmed Rsp5 as the target of NAB2 (see figure 1). The Lindquist lab further highlighted the cellular mechanisms that this approach uncovered. The α−synuclein protein over expression seems to disrupt normal vesicle trafficking, while NAB2 through Rsp5 rebalances the trafficking system. This mechanistic understanding gives the authors a new genetic node to target drug therapies towards.

Lindquist Fig 1

 Figure 1: Schematic showing the use of yeast genetic tools to uncover the mechanism of action and cellular targets for the drug NAB2 A) The node of genes which interact with NAB2 B) NAB2 native function in WT cells is linked to Rsp5 C) NAB2 blocks α−synuclein toxicity with WT Rsp5 but not in Rsp5 mutant cells thus specifically implicating Rsp5 as a target of NAB2

The power of this system is demonstrated in a second paper from the Lindquist group (Chung et al., 2013), which takes the information learned in yeast and applies it to human cell lines from PD (Parkinson’s disease) patients. Using stem cell technology which allows researchers to take somatic cells from patients and re-differentiate them into neurons, the Lindquist group used the induced pluripotent stem cells (ipSc) to test drugs and corroborate mechanisms found in yeast. These ipSc contained specific mutations to α−synuclein, which could then be corrected using CRISPR in sister cell lines. Thus each cell line used had its own specific control with only the one gene of interest mutated. The α−synuclein mutant PD cell line and the edited control line could then be compared. This method picked up on two interesting facets of α−synuclein toxicity, which had previously been seen in the yeast: 1) nitrosative stress caused by increasing nitration and 2) increased accumulation of proteins normally degraded by ERAD (ER-acssociated protein degradation). This confirmed that the yeast model of α−synuclein toxicity was legitimate and that these mechanisms were conserved from yeast to human. Finally, the authors were able to test NAB2, the drug previously tested in yeast, in the ipSc. The Lindquist Lab found that α−synuclein toxicity was prevented. Thus “closing the loop” between the two model systems and confirming their drug discovery methodology.

The Lindquist lab’s work is needless to say extremely pertinent to our ability to better discover drugs for treatment of neurodegenerative diseases. Combining model systems and using yeast allows for a potentially more effective and hopefully faster way to get small molecules into the clinic. So as you continue to shovel copious amounts of pizza and swill your beer watching the forthcoming Super Bowl, appreciate the little yeast that make everything so tasty, and could be helping us understand and treat α−synuclein related diseases.

Marc Marino is a first year Neurosciences student currently rotating with Dr. Richard Daneman. He spends his spare time yelling at sports on his TV or reading/watching science fiction, and is currently looking forward to enjoying a yeast brewed beverage. 


Tardiff, D. F., Khurana, V., Chung, C. Y., & Lindquist, S. (2014). From yeast to patient neurons and back again: Powerful new discovery platforms. Movement Disorders29(10), 1231-1240. doi: 10.1002/mds.25989

http://www.ncbi.nlm.nih.gov/pubmed/25131316

Tardiff DF, Jui NT, Khurana V, et al. Yeast reveal a “druggable”Rsp5/Nedd4 network that ameliorates alpha-synuclein toxicity in neurons. Science 2013;342:979-983

Chung CY, Khurana V, Auluck PK, et al. Identification and rescue of alpha-synuclein toxicity in Parkinson patient-derived neurons. Science 2013;342:983-987.