Progenitors from the central nervous system drive neurogenesis in cancer

Welcome to our Monthly Journal Club! Each month I post a paper or two that I have read and find interesting. I use this as a forum for open discussion about the paper in question. Anyone can participate in the journal club, and provide comments/critiques on the paper by leaving a comment below. I picked this month’s paper because if these findings are true, it is a real paradigm shifting piece of work, substantially challenging what we know about cancer (and specifically prostate cancer), the nervous system, and cell migration in general. Also, this research falls squarely into my wheelhouse, as I too work on how the nervous system and cancer communicate! It was published in Nature, and is titled “Progenitors from the central nervous system drive neurogenesis in cancer” by Claire Magnon & colleagues at the François Jacob Institute of Biology in France.

Tumors interact with their local environment and by extension, the whole organism . These interactions can result in deleterious outcomes for patients, like tumor progression, metabolic problems, anorexia, inflammation, and sleep/circadian disruption. Magnon and colleagues provide evidence that in addition to these established pathways, neural progenitor cells leave the brain and migrate to the tumor (in a model of prostate cancer), promoting cancer growth and progression. (credit:   Walker II & Borniger, 2019   ) .

Tumors interact with their local environment and by extension, the whole organism. These interactions can result in deleterious outcomes for patients, like tumor progression, metabolic problems, anorexia, inflammation, and sleep/circadian disruption. Magnon and colleagues provide evidence that in addition to these established pathways, neural progenitor cells leave the brain and migrate to the tumor (in a model of prostate cancer), promoting cancer growth and progression. (credit: Walker II & Borniger, 2019).

The primary claim in this paper is extraordinary, and I quote it here verbatim: “Here, we reveal a process of tumour-associated neo-neurogenesis in which neural progenitors leave the subventricular zone (SVZ) and reach—through the blood— the primary tumour or metastatic tissues, in which they can differentiate into new adrenergic neurons that are known to support the early stages of the development of cancer”.

Neural Progenitor Cells    (DCX+, green), Astrocytes (GFAP+, blue), and blood vessels (CD31+, red) in the mouse olfactory bulb. These cells are born in the subventricular zone, and then migrate to the olfactory bulb along the rostral migratory stream to integrate into olfactory (smell) circuits. (Credit: CC; Oleg Tsupykov).

Neural Progenitor Cells (DCX+, green), Astrocytes (GFAP+, blue), and blood vessels (CD31+, red) in the mouse olfactory bulb. These cells are born in the subventricular zone, and then migrate to the olfactory bulb along the rostral migratory stream to integrate into olfactory (smell) circuits. (Credit: CC; Oleg Tsupykov).

This raises many many questions, like…how do newly born neural cells know how to get all the way from the brain to a tumor so far away? How do these cells make it past the blood brain barrier (BBB), and even if they do make it this far….how do we know that the cells that make it to the prostate are the same ones that left the brain? There are, after all, neurons in the peripheral nervous system (PNS) that could be infiltrating the tumor to cause these effects. The idea that newly born neurons can leave the nervous system is pretty wild on its own…but the claim that they not only leave, but migrate all the way to a distant tumor where they promote its growth is amazing….if true! Below, I’ll go through the primary figures in this paper one by one and explain what the authors are showing. If I feel like something is missing, or there could have been additional work done, I will say so. So far…it feels like this paper has not gotten the attention it deserves…probably because neuroscientists rarely talk to cancer biologists!

The authors started by looking at prostate tumor samples to see if they indeed contain neural progenitor cells. To label these types of cells specifically, they applied antibodies against doublecortin (DCX) which were tagged with a green fluorescent molecule. This way, all the DCX-expressing cells (i.e., neural progenitor cells) appeared green under a microscope. As one marker is not enough to convince the editors at Nature, they showed that these cells also express other markers of immature neurons (PSA-NCAM, internexin), but not markers of mature neurons (neurofilament-heavy (NF-H)) or epithelial cells (Pancytokeratin (PanCK)).

Figure 1: Neural progenitors (DCX+, PSA-NCAM+, INA+) are found in prostate tumor samples and they provide a prognostic indicator of cancer recurrence/survival.    These neural progenitors do not express markers of epithelial cells or mature neurons, and increased amounts of these cells within the tumor is associated with high-risk tumors compared to low-risk and benign (BPH) samples. Additionally, with each part of the prostate that the tumor invades, there is a concomitant increase in neural progenitor cells. (Credit: Mauffrey et al., 2019)

Figure 1: Neural progenitors (DCX+, PSA-NCAM+, INA+) are found in prostate tumor samples and they provide a prognostic indicator of cancer recurrence/survival. These neural progenitors do not express markers of epithelial cells or mature neurons, and increased amounts of these cells within the tumor is associated with high-risk tumors compared to low-risk and benign (BPH) samples. Additionally, with each part of the prostate that the tumor invades, there is a concomitant increase in neural progenitor cells. (Credit: Mauffrey et al., 2019)

After showing that these ‘central progenitor’ cells can be found in human tumors, the researchers moved to working in a mouse model of pancreatic cancer to more finely understand how these cells get to the tumor and what they do when they get there. First, though, I want to highlight that the marker that they use (DCX) to distinguish newly-born neurons is also expressed in the peripheral nervous system, which raises concerns that their interpretation of cells traveling from the brain to the tumor might be incorrect. Using a triple-transgenic strategy, they engineered mice to express enhanced yellow fluorescent protein (eYFP) in cells that make a human version of DCX (DCX-eYFP mice). This way, all DCX-expressing cells show up as yellow under the microscope (see Fig. 2 below). In addition to these genetic manipulations, mice were engineered to express myc, a proto-oncogene that is highly expressed in most cancers, specifically in the prostate, causing mice to develop prostate tumors similar to those found in humans.

When they analyzed DCX-eYFP cells within the brain and prostate of mice with and without tumors, they found eYFP+ cells in known brain locations, but only in the prostates of mice with prostate cancer. This suggests, that these cells are somehow recruited to the prostate during tumor formation, but not during normal functioning of the pancreas. A benefit to having cells that are labeled yellow is that we can easily run them through something called a fluorescence activated cell sorter (FACS; flow cytometer). This lets us label them with additional colors to see what other proteins they express, and quantify them with single-cell resolution. Using this technique, the researchers demonstrated that DCX-eYFP neural progenitors in the prostate do not express mature cell lineage markers (i.e., they are lin-negative), boosting the idea that these cells truly are progenitor cells (see Fig. 2b).

Figure 2: In a mouse model of prostate cancer (Hi-MYC) where neural progenitor cells are labeled yellow (DCX-eYFP), these cells are found throughout the prostate, similar to DCX expression in human tumors.    Additionally, these prostate DCX+ cells express markers of neural progenitors (e.g., nestin, CD24) without markers of stem cells (e.g., SOX2). (Credit: Mauffrey et al., 2019)

Figure 2: In a mouse model of prostate cancer (Hi-MYC) where neural progenitor cells are labeled yellow (DCX-eYFP), these cells are found throughout the prostate, similar to DCX expression in human tumors. Additionally, these prostate DCX+ cells express markers of neural progenitors (e.g., nestin, CD24) without markers of stem cells (e.g., SOX2). (Credit: Mauffrey et al., 2019)

A small gripe I have with the above figure has to do with the statistics used. For panel 2b, the authors state the data were analyzed using a one-sided Student’s t-test, which is a test that should be used for simple comparisons between two groups when there is strong evidence to suggest the outcome you are looking for is true. In biology, one-sided tests are rarely used, and the use of one here suggests that the authors were rather liberal when assigning significance…even though to the eye, it seems like the data would be significant with a two-tailed test, or a 2-way ANOVA with a more conservative post-hoc test (e.g., Tukey’s HSD).

Sorry for that tangent…moving back onto the paper, the authors provided evidence that DCX+ neural progenitors in the tumor differ in a few different ways from progenitors found in the brain. Specifically, they did not express markers of stem cells (e.g., SOX2) or markers of activated neural stem cells (e.g., GFAP, GLAST, CD133…). Instead, they expressed markers of neural progenitors (e.g., nestin, CD24). This can be seen in Fig. 2d, where samples from the brain (OB, SVZ) are compared to prostate tumor samples at 16 weeks or 52 weeks following cancer-induction. Strangely, in the text the authors describe that these cells showed neuron-differentiation and neuron-projection signatures, and say these data can be found in Figure 2fbut this panel does not exist.

Figure 3: Neural progenitors in the prostate differentiate into adrenergic neurons during tumor development. (   Credit:       Mauffrey et al., 2019)

Figure 3: Neural progenitors in the prostate differentiate into adrenergic neurons during tumor development. (Credit: Mauffrey et al., 2019)

After showing that DCX+ neural progenitors are present in their mouse model of prostate cancer (Hi-MYC), they went on to see if these cells ‘commit’ to a lineage and become a certain type of neuron. Specifically, they asked whether the cells would differentiate into neurons that produce the neurotransmitter norepinephrine (noradrenaline), which are called ‘adrenergic neurons’. This is important because adrenergic neurons can have wide-ranging effects on tumor growth, cancer progression, and metastasis. In the nervous system, DCX+ cells that migrate to the olfactory bulb (OB) usually ‘commit’ to the interneuron cell fate, allowing them to integrate into the circuits that process smell information. In Fig. 3, we can see that when cells were extracted from the brain (OB) or when cells were collected from the prostate tumor and grown in a dish (in vitro), they were able to differentiate into mature neurons (NF-H+), suggesting that these cells can commit to a terminal neural fate. Specifically, when looking in the organism (in vivo), Lin- eYFP+ neural progenitors were present in the tumor (blue color in Fig. 3 f,g,h), they fluctuated in amount during the course of tumor growth, and sent projections (axons) throughout the tumor tissue (Fig. 3 d,e). When co-labeled with an antibody against tyrosine hydroxylase (TH; a key enzyme in the norepinephrine synthesis pathway), they observed that DCX-eYFP+ cells also expressed TH, suggesting that they are indeed adrenergic neurons.

Figure 4: Neural progenitors in the brain (SVZ) migrate through the blood towards the prostate tumor in the Hi-MYC mouse model of pancreatic cancer.    Note: red (TdTomato) cells that originated in the brain’s SVZ could be found in the tumor throughout tumor development! Credit: Mauffrey et al., 2019).

Figure 4: Neural progenitors in the brain (SVZ) migrate through the blood towards the prostate tumor in the Hi-MYC mouse model of pancreatic cancer. Note: red (TdTomato) cells that originated in the brain’s SVZ could be found in the tumor throughout tumor development! Credit: Mauffrey et al., 2019).

So now we know that there are neural progenitors that colonize the tumor and can differentiate into adrenergic neurons (TH+). The question then becomes…where do these cells come from and how do they know how to get all the way to the tumor? They started by looking in the brain at different neural progenitor cells, and how their numbers change over time. They noted that a sub-population (green in Fig. 4) of Lin-eYFP+ progenitors changed in the SVZ during tumor growth, adding that this may be evidence of some of these cells leaving the area or the brain altogether (to putatively migrate to the tumor). To test this, they injected a lentiviral vector encoding the fluorescent protein TdTomato (red) into the SVZ to track where the cells go (as cells coming from that region will be labeled red no matter where they go in the body). The showed that these tagged neural progenitor cells could be found in the prostate tumor environment by 8, 12, and 16 weeks following tumor induction, providing evidence that they did indeed make the migration out of the brain (Fig. 4 f,g,h)! Additionally, by labeling the vasculature (CD31+) around the SVZ, they showed that in mice with tumors, the blood brain barrier (BBB) was disrupted, suggesting that the neural progenitors are able to leave the brain because the BBB is not functioning as usual.

One note I want to make is that the lentivirus approach that they used is not ‘cell-type specific’, and cells besides their target population (DCX-eYFP+) were definitely labeled. Additionally, since the lentivirus can infect any cells in the area, and it can actually travel in the blood stream and label cells outside the brain…this represents a potentially significant caveat.

Figure 5: DCX+ progenitor cells regulate tumor development in mice. Mice lacking DCX+ cells grew tumors that were much less aggressive and invasive. (   Credit: Mauffrey et al., 2019).

Figure 5: DCX+ progenitor cells regulate tumor development in mice. Mice lacking DCX+ cells grew tumors that were much less aggressive and invasive. (Credit: Mauffrey et al., 2019).

Finally, a major unanswered question was the ‘so-what?’ question, that is, do these cells actually do anything in the tumor micro-environment, or are they just sitting there as bystander cells. To test this, the authors used another transgenic strategy to express the diphtheria toxin receptor (DTR) on DCX+ progenitor cells. This allowed them to specifically eliminate DCX+ cells, letting them test whether they do indeed influence how prostate cancer develops.

They further used a new cancer model (PC3-Luc), where tumor cells are implanted in a recipient mouse. These cells were additionally engineered to express firefly luciferase (Luc). This allows researchers to ‘see’ where the tumor cells are in each mouse non-invasively, simply by injecting luciferin and measuring the light that is given off using a specialized camera (measured in photons). Using these approaches, they demonstrated that tumors in mice lacking neural progenitor cells (DCX+ ablated) caused fewer lesions and prevented the engraftment of transplanted tumor tissue. This suggests that DCX+ progenitor cells are critical for the early stages of tumor development! More striking is the finding that selectively eliminating DCX+ cells in the SVZ significantly inhibited tumor development, adding credence to the idea that these cells really do migrate from the brain to the prostate to elicit their effects. In the opposite experiment (where they transplanted DCX+ cells into mice with established tumors), they observed enhanced tumor growth (Fig. 5 d,e)!

Together, this study has a few problems that may detract from it’s primary finding. However, if additional research demonstrates that this phenomenon is real, it could be a huge game changer for both neuroscience and cancer! If depleting neural progenitors becomes a viable option for tumor suppression, this would be a completely new avenue for the treatment of prostate cancer, and potential other malignancies as well! One huge question that remains to be answered is “what is the signal from the tumor that causes progenitor cells to migrate?”. Figuring out the answer to this will be of paramount importance in developing tangible and realistic therapeutics.

Sorry for the super long post, but I just got really into this paper! Leave a comment below and join the discussion! For the latest updates, as always, follow me on twitter @jborniger. ‘Till next time, stay curious!

Exercise enhances motor skill learning by neurotransmitter switching in the adult midbrain

Welcome to our Monthly Journal Club! Each month I post a paper or two that I have read and find interesting. I use this as a forum for open discussion about the paper in question. Anyone can participate in the journal club, and provide comments/critiques on the paper by leaving a comment below. I picked this months paper because it adds additional evidence for a really cool and understudied type of neural plasticity: neurotransmitter switching. Also, this is the first preprint featured on this website, and therefore you must take the findings in this paper with a grain of salt as they have not been peer reviewed. I do love BioRxiv, because it is a great way of getting your ideas out there before the long publication process has been completed, and it is journal agnostic. Also…I have accepted an assistant professor position at Cold Spring Harbor Laboratory, where BioRxiv was started…so the fit for this month was perfect!

This month’s paper is “Exercise enhances motor skill learning by neurotransmitter switching in the adult midbrain” by Hui-quan Li and Nicholas Spitzer at the Kavli Institute for Brain and Mind located at the University of California - San Diego. On top of his great research program, Dr. Spitzer is undoubtedly the winner of the “Best Moustache in Neuroscience” award. I will provide a brief overview of the techniques/approaches used to make it more understandable to non-expert readers. If I can’t figure something out, I’ll just say so. Check out the video below for a quick summary of how neurotransmitter switching makes up a unique form of neural plasticity.

Hui-quan Li and Nicholas Spitzer were interested in how motor learning occurs, the process by which we become better and better at specific motor tasks via trial and error (e.g., using chopsticks, speaking fluently, and quick reflexes…). This obviously involves a form of neural plasticity, as our brains need to change in some way to strengthen the circuits that improve a behavior, while refining those that detract from it. Motor learning has been intensely studied in the realm of neuroplasticity, involving circuits in the cortex, basal ganglia, brainstem, cerebellum, and spinal cord. However, whether neurotransmitter switching contributes to this form of learning was unknown. Neurotransmitter switching is an under-appreciated form of plasticity, as in most high school and college textbooks, neurons are assigned a neurotransmitter (e.g., dopamine neuron) which sticks with them for life, making the concept of a plastic ‘switchable’ neurotransmitter repertoire foreign to most students. Today I’m going to try and make the case for neurotransmitter switching, using this beautiful study as a template!

The authors started by examining how the brain changes in response to a week of aerobic exercise (a running task, shown below). They trained mice to run on a wheel throughout the week, and then tested their motor coordination on a rotating rod (rotarod) and a balance beam. They demonstrated that after a week of training, mice that ran increased their speed on the running wheel, fell off the rotarod at higher speeds of rotation, and kept better balance on the balance beam than mice that didn’t run. This learning effect lasted for up to 2 weeks following training, suggesting that mice learned the motor behavior, but this ‘motor memory’ can be lost if it is not reinforced with more exercise.

One week of running training induces motor learning, an effect that lasts at least 2 weeks post-training. (Credit: Li & Spitzer, 2019)

One week of running training induces motor learning, an effect that lasts at least 2 weeks post-training. (Credit: Li & Spitzer, 2019)

So now that we have a strong motor learning experimental set up, we can begin to understand what is going on in the brain in response to the running training. To do this in an unbiased fashion, the authors used a technique calledcFos mapping’, where the brain is sectioned following training (or no training as control) and cells are labeled with antibodies against the immediate early gene cFos. This is a proxy of recent neural activity, and lets researchers look for cells in the brain that were activated immediately before the tissue was collected (Approx 30-90 minutes before). By using this method, they found that in response to running training, neurons in a brain structure called the pedunculopontine nucleus showed signs of increased activity (more cFos+ cells detected; see below).

Running induces neural activity (cFos labeling) in multiple brain regions, and most notably in the pedunculopontine nucleus (PPN). Here, we can see that in response to running, higher amounts of cFos are detected in the PPN (red = all neurons, green = cFos (active neurons)). (Credit: Li & Spitzer, 2019)

Running induces neural activity (cFos labeling) in multiple brain regions, and most notably in the pedunculopontine nucleus (PPN). Here, we can see that in response to running, higher amounts of cFos are detected in the PPN (red = all neurons, green = cFos (active neurons)). (Credit: Li & Spitzer, 2019)

This is an interesting finding, but the authors still did not know what type of neurons were being activated by the running. This is important to know, as different neurons (even in the same brain area) can have opposing effects on behavior/learning. As there had been previous work done in this brain area, they labeled cells for some primary neurotransmitter types in the PPN: Acetylcholine and GABA.

Running induces a neurotransmitter switch from acetylcholine to GABA in the caudal pedunculopontine nucleus (cPPN). Above, we can see that the number of acetylcholine producing neurons (ChAT+) decreases in response to running, with a concomitant increase in GABA-production (GAD1+). (Credit: Li & Spitzer, 2019).

Running induces a neurotransmitter switch from acetylcholine to GABA in the caudal pedunculopontine nucleus (cPPN). Above, we can see that the number of acetylcholine producing neurons (ChAT+) decreases in response to running, with a concomitant increase in GABA-production (GAD1+). (Credit: Li & Spitzer, 2019).

Indeed, they found these two types of neurons in the PPN. They observed that in response to running, the number of active acetylcholine-producing neurons (ChAT+ and cFos+) increased dramatically in the caudal region of the PPN. However, this was associated with a decrease in the amount of acetylcholine producing neurons in this area…how could that be? It turns out that these cells were not disappearing, but switching which neurotransmitter they predominantly make (from acetylcholine to GABA) in response to running!

Acetylcholine-expressing neurons in the caudal Pedunculopontine nucleus (cPPN) lose acetylcholine and gain GABA in response to 1 week of running training. (Credit: Li & Spitzer, 2019).

Acetylcholine-expressing neurons in the caudal Pedunculopontine nucleus (cPPN) lose acetylcholine and gain GABA in response to 1 week of running training. (Credit: Li & Spitzer, 2019).

To investigate this more deeply, they used viruses that infect neurons (adeno-associated viruses (AAVs)) that carry transgenic DNA payloads (plasmid DNA; pDNA). This payload is inactive (i.e., it does not do anything by itself), but becomes active in the cell when a certain enzyme is present: Cre-recombinase. The researchers used mice that express this enzyme only in acetylcholine-producing neurons (also known as ChAT-IRES-Cre mice). By combining cre-dependent viruses with ChAT-Cre mice, they are able to express transgenes in a specific brain region in a ‘cell-type specific’ manner. They used this approach to make acetylcholine neurons in the cPPN express mRuby2, a very bright version of red fluorescent protein. This way, they can track how these neurons (which will always be red) change their neurotransmitter components in response to running. Using this technique, they demonstrated that a sizable proportion of neurons in this brain area switch their neurotransmitter of choice from acetylcholine to GABA in response to just a week of running!

Viral-mediated prevention of neurotransmitter switching (via upregulation of ChAT expression) prevents motor learning following a running task. (Credit: Li & Spitzer, 2019).

Viral-mediated prevention of neurotransmitter switching (via upregulation of ChAT expression) prevents motor learning following a running task. (Credit: Li & Spitzer, 2019).

The authors went on to test what other brain regions these cells send projections to, which likely mediates their involvement in motor learning. They used beads that travel backwards along neuron projection axons (retrograde labeling) to label cells in the PPN that project to other brain areas involved in motor behavior/learning. They found that running reduced the number of ChAT+ (acetylcholine) terminals (where neurotransmitter is released) in multiple brain regions, including the ventral tegmental area, substantia nigra, and thalamus, key regions for motor learning.

Now the question moved onto whether this neurotransmitter switching was actually important for the motor learning. In simpler terms, this is the ‘so what?’ question. To test whether the loss of ChAT expression in the cPPN was important for motor learning (or was just a side-effect), they used ChAT-Cre mice as before, but injected a virus that promoted the cells to make a ton of acetylcholine, preventing them from making the switch to GABA (see above). When they did this, the mice no longer were able to display motor learning behavior, and they performed just as badly as non-trained mice on the rotarod and balance beam tests! This demonstrates that loss of acetylcholine expression in the PPN is important for this type of motor learning.

Prevention of the Acetylcholine/GABA switch during training prevents motor learning. Using a short-hairpin targeting GAD1 (shGAD1) to knock down it’s expression in ChAT+ neurons in the PPN, the authors demonstrate that without increased levels of GABA in response to running, the mice no longer learn this task. (Credit: Li & Spitzer, 2019)

Prevention of the Acetylcholine/GABA switch during training prevents motor learning. Using a short-hairpin targeting GAD1 (shGAD1) to knock down it’s expression in ChAT+ neurons in the PPN, the authors demonstrate that without increased levels of GABA in response to running, the mice no longer learn this task. (Credit: Li & Spitzer, 2019)

What about the GABA, though? If loss of acetylcholine expression is necessary for learning, is gaining GABA also necessary? To test this, the authors again used a viral approach. Using ChAT-Cre mice to target only neurons expressing acetylcholine in the PPN, they injected a Cre-dependent short-hairpin RNA against a gene that is important in the synthesis of GABA (shGAD1). When they trained these and control mice (which were injected with ‘scrambled’ shRNA) on the running protocol, only mice with the scrambled construct (which doesn’t affect gene expression) that completed the motor learning task showed a major uptick in the number of GABA-expressing neurons (GAD1+). This suggested that their shRNA technique successfully knocked down GABA and prevented acetylcholine neurons from ‘switching’ in response to running. When these same mice had their motor learning tested on the rotarod and balance beam, those that had GABA knocked down in the PPN showed no signs of learning (see above panel (e) and (f)). Together, over expression of acetylcholine or knockdown of GABA in the cPPN prevented motor learning. This strongly indicates that neurotransmitter switching in the PPN plays a major role in motor learning. Aerobic physical exercise promotes the ability to acquire new motor skills, and it serves as a therapy for many motor disorders including Parkinson’s disease, coordination disorder, and autism. Until this study, how this worked at the neural level was poorly understood. These findings strongly implicate neurotransmitter switching as a form of neuroplasticity that underlies motor learning, and offers a potentially new target for treatment of a large variety of diseases.

That’s it for this post guys! Please share what you think in the comments below, or send me a message on twitter @jborniger! See you soon!

A gut-to-brain signal of fluid osmolarity controls thirst satiation

Welcome to our Monthly Journal Club! Each month I post a paper or two that I have read and find interesting. I use this as a forum for open discussion about the paper in question. Anyone can participate in the journal club, and provide comments/critiques on the paper by leaving a comment below. I picked this months paper because it reveals a circuit spanning multiple systems and timescales influencing one of our most essential behaviors, drinking and fluid intake. This month’s paper is “A gut-to-brain signal of fluid osmolarity controls thirst satiation” by Zachary Knight and colleagues at The University of California - San Francisco. The lead author was Christopher A. Zimmerman, a graduate student in Dr. Knight’s lab who focuses on homeostatic control of thirst. I will provide a brief overview of the techniques/approaches used to make it more understandable to non-expert readers. If I can’t figure something out, I’ll just say so.

How do we know when we’re thirsty, and how do we know when to stop drinking? This is very important, as we need to keep proper concentrations of ions (also known as osmolarity) within the fluid compartments of our body to stay alive. Osmolarity can be thought of as the relative concentrations of ions in a solution, where our body likes to stay around 300 milli-osmoles (mOsm), where each compartment (intra-cellular, interstitial, and blood) remain in equilibrium. If you drink too much water, you can die due to the drastic changes in osmolarity that occur, causing our cells to burst (lysis). If you are dehydrated, our cells wrinkle up (crenation), a phenomenon that can also lead to death. A jarring example of this occurred in 2007 when a radio station held a contest titled “Hold your Wee for a Wii”, where contestants were tasked with drinking as much water as they could without peeing to win a Nintendo Wii. Unfortunately, the radio DJs did not know anything about basic physiology and were under the false impression that you can drink as much water as you want without any detrimental effects. One contestant drank so much that the osmolarity of her blood became drastically different from other fluid compartments in her body leading to cell lysis, and she died as a result.

There are several well-known circuits in the brain that have been identified as key regulators of thirst and drinking behavior. These include the subfornical organ (SFO), the median preoptic nuclei (MnPO), and the supraoptic nuclei (SON). Together, these structures receive input from the mouth and throat on the amount of liquid that we are drinking in real time (that is, they detect the volume of fluid intake), and are rapidly inactivated upon drinking pretty much any type of fluid (See the Figure below). After not drinking for a while (i.e., we’re thirsty), the activity in these structures rises and promotes drinking behavior. There’s something that doesn’t quite add up here, and that is how do these structures know the ‘type’ of fluid that you’re drinking? If it is just measuring the volume, then we’d feel just as quenched after drinking a bottle of sea water as we would after drinking one of fresh water. The only real difference between fresh and sea water is the salt content (that is, sea water has a higher osmolarity than fresh water). That means that there must be an osmolarity detector somewhere in the body that relays to the brain information about the type of fluid that has been consumed. As Dr. Knight says, "There has to be a mechanism for the brain to track how salty the solutions that you drink are and use that to fine-tune thirst…But the mechanism was unknown."

Brain structures underlying thirst, drinking, and satiation. A major component is the subfornical organ (SFO), which receives input on the volume of fluid consumed, and directs changes in drinking behavior using excitatory (glutamate) and inhibitory (GABA) signaling. (Credit:    Zimmerman et al., 2017   )

Brain structures underlying thirst, drinking, and satiation. A major component is the subfornical organ (SFO), which receives input on the volume of fluid consumed, and directs changes in drinking behavior using excitatory (glutamate) and inhibitory (GABA) signaling. (Credit: Zimmerman et al., 2017)

Christopher Zimmerman and other members of the team tested this using a method called fiber photometry in tandem with intra-gastric (i.e., into the gut) injections of fluids with different osmolarity. Fiber photometry is a way to measure the activity of dozens or hundreds of cells in deep brain structures simultaneously and in real time, while an animal (in this case, a mouse) behaves and runs around normally. This makes it a great tool to see how different populations of neurons operate in real-world scenarios. Fiber photometry allowed the authors to see how neurons in the subfornical organ (SFO) respond when a thirsty mouse drinks naturally (in hydrated and dehydrated conditions), and how they respond when liquids of different osmolarity are injected directly into the gut (allowing them to bypass the volume sensors in the mouth). They confirmed prior studies that showed rapid reductions in SFO activity upon drinking either regular water or salty water. However, when they injected these liquids into the gut, the SFO reduced its activity only in conditions where normal water was injected, but not in response to salt water. This suggests that a signal from the gut makes it up to the brain, where it somehow conveys to the brain the osmolarity of a liquid that has been consumed (see the figure below)

The subfornical organ (SFO) rapidly reduces activity upon drinking either regular (Water) or salty (NaCl) water (panel b). However, when solutions of different salt concentrations (osmolarity) were directly infused into the gut (panel d), the SFO drastically increased its activity (panels e,f,g) as a function of the osmolarity of the solution (R^2 = 0.98; also known as a near 1 to 1 relationship). (note: F/F means ‘fractional fluorescent change’, indicating the activity of the cells being measured) (Credit: Zimmerman et al., 2019).

The subfornical organ (SFO) rapidly reduces activity upon drinking either regular (Water) or salty (NaCl) water (panel b). However, when solutions of different salt concentrations (osmolarity) were directly infused into the gut (panel d), the SFO drastically increased its activity (panels e,f,g) as a function of the osmolarity of the solution (R^2 = 0.98; also known as a near 1 to 1 relationship). (note: F/F means ‘fractional fluorescent change’, indicating the activity of the cells being measured) (Credit: Zimmerman et al., 2019).

The authors moved on to investigate how the osmolarity signal is represented in other components of the thirst circuit (i.e., MnPO, SON). Targeting vasopressin neurons in the SON, they showed that these neurons act in a similar fashion to those in the SFO. They are rapidly inhibited by drinking, but also increase activity in response to elevations in blood osmolarity (see Figure below).

Supraoptic nucleus (SON) vasopressin neurons are rapidly inactivated by drinking (panel C), and bidirectionally regulated by gut fluid osmolarity (panel d). The heat maps in (d) show the activity of these neurons where warmer colors represent higher activity. Note that increases in fluid salt content (150 mM to 500 mM) causes stepwise increases in neural activity. (Credit: Zimmerman et al., 2019).

Supraoptic nucleus (SON) vasopressin neurons are rapidly inactivated by drinking (panel C), and bidirectionally regulated by gut fluid osmolarity (panel d). The heat maps in (d) show the activity of these neurons where warmer colors represent higher activity. Note that increases in fluid salt content (150 mM to 500 mM) causes stepwise increases in neural activity. (Credit: Zimmerman et al., 2019).

Continuing their investigation of neural circuitry controlling drinking behavior and fluid balance, they measured how the final major component in the circuit (the MnPO) alters activity in different experimental paradigms. This time, they upgraded their tech from fiber photometry to using a tiny microscope (microendoscope) implanted into the mouse’s brain to see how the individual cells in the MnPO respond to fluid intake and blood osmolarity. The picture below shows the similarities and differences of microendoscopy and fiber photometry.

Both  in vivo  microendoscopy (a) and fiber photometry (b) measure neural activity by collecting light emitted by a fluorescent genetically encoded calcium indicator in neurons of interest (e.g., GCaMP6s). More cumbersome and harder to use, the microendoscope technique allows researchers to examine the activity of individual cells over long periods of time in awake, behaving mice. This is a major advantage over fiber photometry, as it allows one to understand how different cells in the circuit act in response to various stimuli. (Credit:    Resendez & Stuber, 2015   ).

Both in vivo microendoscopy (a) and fiber photometry (b) measure neural activity by collecting light emitted by a fluorescent genetically encoded calcium indicator in neurons of interest (e.g., GCaMP6s). More cumbersome and harder to use, the microendoscope technique allows researchers to examine the activity of individual cells over long periods of time in awake, behaving mice. This is a major advantage over fiber photometry, as it allows one to understand how different cells in the circuit act in response to various stimuli. (Credit: Resendez & Stuber, 2015).

Using this technique, they started by targeting neurons that make glutamate (excitatory) in the MnPO. They observed that individual neurons in this region could be clustered into three categories based on how they responded to changes in fluid intake and osmolarity. One subpopulation (cluster 1, 17%) didn’t show any response to regular saline injection but showed significant activation after salt challenge, suggesting that these neurons encode blood osmolarity. These same neurons were drastically inhibited during drinking. By contrast, neurons that fell into cluster 2 (34%) showed only quick responses independent from fluid intake (the authors think it was probably the stress or pain of injection), whereas neurons from cluster 3 (49%) were largely unresponsive.

They continued to investigate another major neural population in this region using the same technique, those that express the inhibitory neurotransmitter GABA. They also were able to segregate cells into different clusters based on their responses to fluid intake and osmolarity. Three different categories emerged. Specifically, they showed that individual neurons can be categorized as “ingestion-activated” (28%), “ingestion-inhibited” (36%), or “untuned” (35%). As the category names suggest, this means that different subsets of cells respond to fluid intake by increasing their activity, decreasing their activity, or not changing their activity at all (untuned; see panel [d] in the figure below).

GABA-producing neurons within the median preoptic nucleus (MnPO) can be clustered into “ingestion-activated”, “ingestion-inhibited”, or “untuned” based on their responses to fluid intake. in panel (d) we can clearly see segregation of these neural responses following drinking. (Credit: Zimmerman et al., 2019)

GABA-producing neurons within the median preoptic nucleus (MnPO) can be clustered into “ingestion-activated”, “ingestion-inhibited”, or “untuned” based on their responses to fluid intake. in panel (d) we can clearly see segregation of these neural responses following drinking. (Credit: Zimmerman et al., 2019)

This demonstrates that individual MnPO glutamatergic neurons receive ingestion signals from the mouth/throat, satiation signals from the gut and homeostatic signals from the blood, which they process and integrate to estimate physiological state. Additionally, these data also indicate that the majority of GABAergic MnPO neurons are strongly influenced by fluid ingestion, with smaller subsets that integrate multiple signals with relevance to fluid balance (like water availability, stress and gastrointestinal osmolarity). Importantly, these studies suggest that the concept of homeostatic need (or physiological set point) can be computed at the level of individual neurons in a circuit. These findings could point the way for new therapies for diseases that drastically alter fluid balance in the body, such as diabetes and cardiovascular disease. Additional studies on regulation of homeostasis are required to understand how these populations of cells act together to receive, integrate, and relay a signal that engages drinking behavior and the feeling of ‘thirst’.

As always, let me know what you think by leaving a comment below or messaging me on twitter @jborniger ! See you guys next time! Stay curious!

Mammalian Near-Infrared Image Vision through Injectable and Self-Powered Retinal Nanoantennae

Welcome to our Monthly Journal Club! Each month I post a paper or two that I have read and find interesting. I use this as a forum for open discussion about the paper in question. Anyone can participate in the journal club, and provide comments/critiques on the paper. I picked this months paper because it is just too cool not to talk about! Published just a couple of days ago online, this month’s paper is “Mammalian Near-Infrared Image Vision through Injectable and Self-Powered Retinal Nanoantennae” by Xue Tian and colleagues at University of Science & Technology of China. I will provide a brief overview of the techniques/approaches used to make it more understandable to potential non-expert readers. If I can’t figure something out, I’ll just say so.

Have you ever wanted to see like a rattlesnake? Have you ever yearned to have ‘thermal vision’, the type you’ve undoubtedly seen on an average episode of “Cops”? Well, thanks to recent advances in science, you may soon be able to! Our vision is restricted to wavelengths of light falling between 400 and 700 nm…that’s it, everything you’ve ever seen or can ever see is due to your retina interpreting light in this range. This is great, but it is so limiting, so much so that most people never even think about what they are missing in the non-visible range of light. Indeed, things we can see fall into < 1% of the total range of the electromagnetic spectrum (see below; visible + non-visible). Imagine what we could see with 2% of the spectrum covered!

To expand the visual capabilities of mice into the near-infrared (NIR) range, the researchers developed a nanoparticle based ‘nanoantenna’ that is injectable (into the eye), self-powered, and binds normal photoreceptors (rods and cones) in the retina. These retinal photoreceptor-binding up-conversion nanoparticles (pbUCNPs) work as mini-transducers, capable of transforming NIR light into short wavelength (visible) emissions in vivo (that is, in the living animal), that the mouse can then see normally. (Image credits: Steven White, Quora.com; Newpaper24.com)

Anyone who has worked with fluorescent particles (e.g., those conjugated to secondary antibodies), knows about excitation/emission spectra. This reflects the wavelength of light that excites the fluorescent particle (in this case, nanoparticle), and then reciprocally, what type of light the particle gives off (emission). The researchers developed so-called ‘up-conversion’ nanoparticles to allow mice to see NIR light. This means that the emission spectra of these nano-antenna were of smaller (higher energy) wavelengths of light than the excitation spectra. Specifically, these nano-antenna are excited by NIR light (~980 nm wavelength), but give off light in the visible spectrum (~535 nm wavelength). Additional modifications were made to the nanoparticles to make them water-soluble (so they could be injected in phosphate buffered saline; PBS), and make them bind (uniformly) to rods and cones in the retina). Through these biochemical tricks, they were able to create nanoantennae that sense NIR light, respond to that light by emitting light in the visible spectrum, and bind to natural photoreceptors in the retina! As an added bonus, they further showed that these nano-antennae are non-toxic (at least for 2 months), as they did not cause photoreceptor degeneration or marked activation of immune cells within the retina (microglia).

Photoreceptor-binding Up-Conversion NanoParticles (pbUNCPs) bind to natural photoreceptors (rods and cones). Above, you can see that when mice were injected with just PBS, no pbUCNP signal is observed…however when they are injected with PBS + pbUCNPs….the nanoparticles latch onto existing rods and cones, showing that they can ‘hijack’ or ‘co-opt’ normal visual pathways in the retina  (Ma et al., 2019).

Photoreceptor-binding Up-Conversion NanoParticles (pbUNCPs) bind to natural photoreceptors (rods and cones). Above, you can see that when mice were injected with just PBS, no pbUCNP signal is observed…however when they are injected with PBS + pbUCNPs….the nanoparticles latch onto existing rods and cones, showing that they can ‘hijack’ or ‘co-opt’ normal visual pathways in the retina (Ma et al., 2019).

Ok, so how did the researchers tell if the mice could indeed see the NIR light? Their first test was a simple pupillary light reflex (PLR) test. As light intensity increases, our pupils (and those of mice) constrict to prevent damage to our retinas (think about when you got your pupils dilated…and how sensitive to light you were then). NIR light does not normally induce a PLR, because our eyes are not normally sensitive to these wavelengths of light. However, mice injected with pbUNCPs showed a dramatic PLR when exposed to NIR light, suggesting they could sense the intensity of this light!

pbUNCPs allow for detection of near-infrared (NIR) light! Above, you can see the pupils of two mice, a control mouse injected with PBS, and a mouse injected with the pbUCNPs. As you can see, when exposed to no-light, both pupils are wide, indicating that they both interpret the environment as ‘dark’. However, when exposed to NIR light (980 nm), only the mouse injected with pbUCNPs shows a pupillary light reflex (PLR), indicating that they are able to discern NIR light from darkness (an ability not possessed by control mice).  (Ma et al., 2019)

pbUNCPs allow for detection of near-infrared (NIR) light! Above, you can see the pupils of two mice, a control mouse injected with PBS, and a mouse injected with the pbUCNPs. As you can see, when exposed to no-light, both pupils are wide, indicating that they both interpret the environment as ‘dark’. However, when exposed to NIR light (980 nm), only the mouse injected with pbUCNPs shows a pupillary light reflex (PLR), indicating that they are able to discern NIR light from darkness (an ability not possessed by control mice). (Ma et al., 2019)

To further probe the question of whether the mice could see this type of light or not, they recorded the activity of neurons in the retina of mice that had been injected with pbUNCPs or PBS (control). Indeed, only retinas from mice that had been injected with pbUNCPs showed electrical responses to NIR light, indicating that these nanoparticles were able to render the retina sensitive to this normally ‘invisible’ light. Importantly, the retina from mice injected with pbUNCPs also showed normal responses to light in the visible range (535 nm), suggesting that their ability to sense NIR light did not interfere with their ability to see ‘normal’ light.

Retinas from pbUCNP-injected, but not control-injected mice respond to NIR light! The first two panels above (vertical) show how a control mouse responds to visible light (top) and NIR light (no response; 2nd from top). Reciprocally, mice with pbUNCPs respond the same to both visible and non-visible NIR light.  (Ma et al., 2019)

Retinas from pbUCNP-injected, but not control-injected mice respond to NIR light! The first two panels above (vertical) show how a control mouse responds to visible light (top) and NIR light (no response; 2nd from top). Reciprocally, mice with pbUNCPs respond the same to both visible and non-visible NIR light. (Ma et al., 2019)

This physiological evidence is great, but what about something more relevant to behavior? Can mice see well enough in NIR light to make decisions in response? To test this, the authors performed a number of behavioral tests, the outcomes of which depended on whether the mouse could discriminate NIR light from visible light. The first of these tests was a widely known and well-validated test of anxiety, the “light-dark box”. This test takes advantage of the fact that mice prefer a dark over light environment (as they are nocturnal, and do not want to be spotted by a day-active predator!). Here, the researchers shined visible (535 nm) or invisible NIR light (980 nm) into the ‘light’ chamber, and tested whether control or 'pbUCNP’-injected mice responded by running into the ‘dark’ chamber. Only mice that could see the NIR light (pbUCNP-injected) responded tot he 980 nm light by running into the dark box. The mice with just normal vision could not recognize that the 980 nm light was on, and simply explored the dark and light boxes equally. Importantly, both control and pbUCNP-injected mice avoided visible light (535 nm) when it illuminated the ‘light’ chamber, suggesting that these augmented mice could see normal light just as well as the control mice.

pbUCNP-injected mice recognize and respond to NIR light cues to elicit behavioral responses. The top two panels (C,D) show results of a light-dark box test, where mice can choose to be out in the open (in the light) or retreat into a dark box (which they naturally prefer). Control mice and those injected with pbUCNPs responded to visible light (525 nm) by retreating into the dark box, however when the light was in the NIR range (980 nm), only mice injected with pbUCNPs responded, while control mice could not discern a difference between 980 nm light and darkness. In the lower panels (E,F), mice were tested for their ‘freezing’ responses in a ‘fear conditioning’ paradigm. A 535 nm (visible) light was shown for 20s before a 2 second footshock for 6 cycles to let the mice form an associative memory (where light predicts a painful stimulus (shock)). Normal mice, after forming this memory show a ‘freezing’ or ‘immobile’ response to just the light, because they ‘remember a shock is coming’. When the researchers illuminated the mice with 535 or 980 nm light after training, control mice only froze in response to the 525 nm light, while the pbUCNP injected mice froze in response to 535 and 980 nm light!  (Ma et al., 2019).

pbUCNP-injected mice recognize and respond to NIR light cues to elicit behavioral responses. The top two panels (C,D) show results of a light-dark box test, where mice can choose to be out in the open (in the light) or retreat into a dark box (which they naturally prefer). Control mice and those injected with pbUCNPs responded to visible light (525 nm) by retreating into the dark box, however when the light was in the NIR range (980 nm), only mice injected with pbUCNPs responded, while control mice could not discern a difference between 980 nm light and darkness. In the lower panels (E,F), mice were tested for their ‘freezing’ responses in a ‘fear conditioning’ paradigm. A 535 nm (visible) light was shown for 20s before a 2 second footshock for 6 cycles to let the mice form an associative memory (where light predicts a painful stimulus (shock)). Normal mice, after forming this memory show a ‘freezing’ or ‘immobile’ response to just the light, because they ‘remember a shock is coming’. When the researchers illuminated the mice with 535 or 980 nm light after training, control mice only froze in response to the 525 nm light, while the pbUCNP injected mice froze in response to 535 and 980 nm light! (Ma et al., 2019).

To further test whether mice could really see NIR light without damage to normal vision, they used a ‘Y-shaped water maze’, where mice are put in water (which they dislike) and have to discern a triangle from a circle to escape down one arm of the ‘Y-maze’. One of the arms (e.g., the one associated with a triangle) has an elevated platform underwater that the mice naturally try to find to get out of the water. The mice are trained to know that the triangle is the right choice, and then tested at a later date to see if they remember this using shapes projected in visible (535 nm) or invisible (NIR; 980 nm) light.

Mice were tested on the ‘Y-shaped water maze’, where they had to swim to escape the water by finding a hidden platform located at the end of one of the arms of the maze. In these experiments, the triangle shape pointed the way to the hidden platform. Using various patterns of visible and NIR light, they demonstrated that only pbUCNP-injected mice could perform at levels significantly above chance (50%) when images were presented in NIR and visible light, indicating they could see not only the light, but discern discrete shapes as well.   Note : Green in the above image represents shapes shown in visible light, while red indicates they were shown in NIR light (Ma et al., 2019).

Mice were tested on the ‘Y-shaped water maze’, where they had to swim to escape the water by finding a hidden platform located at the end of one of the arms of the maze. In these experiments, the triangle shape pointed the way to the hidden platform. Using various patterns of visible and NIR light, they demonstrated that only pbUCNP-injected mice could perform at levels significantly above chance (50%) when images were presented in NIR and visible light, indicating they could see not only the light, but discern discrete shapes as well. Note: Green in the above image represents shapes shown in visible light, while red indicates they were shown in NIR light (Ma et al., 2019).

They observed that no matter where they put the triangle and circle (left or right arms of the Y-maze), or what background they used (visible, dark, or NIR light), the pbUCNP injected mice almost always picked the triangle arm of the maze, allowing them to escape. In contrast, control mice could not discern the NIR light circle from the triangle, and their performance on the task was only at chance level (50% correct). This exciting study is the first to artificially enhance vision using bio-compatible nanoparticles that self-anchor to photoreceptors in the retina (rods/cones). Although mouse vision is much different than human vision (mice primarily explore their environments using smell (olfactory) cues, rather than sight), there is no biological reason why this technology couldn’t be applied to humans as well. Whether it should be….is another question! What would you do with infrared vision? How could this change the playing field for soldiers, doctors, pilots….etc…all of whom use infrared technology for very important tasks daily? Leave a comment down below and join the discussion!!

Rocking Promotes Sleep in Mice through Rhythmic Stimulation of the Vestibular System

“ Rock-a-bye baby, On the tree tops, When the wind blows, The cradle will rock. When the bough breaks, The cradle will fall, and down will come Baby, Cradle and all.” - c. 1765

Welcome to our Monthly Journal Club! Each month I post a paper or two that I have read and find interesting. I use this as a forum for open discussion about the paper in question. Anyone can participate in the journal club, and provide comments/critiques on the paper. This month’s paper is “Rocking promotes sleep in mice through rhythmic stimulation of the vestibular system” by Paul Franken and colleagues at The University of Laussane in Switzerland. I will provide a brief overview of the techniques/approaches used to make it more understandable to potential non-expert readers. If I am not familiar with something, I’ll simply say so.

There’s a whole market around baby-rocking equipment….and apparently some people will pay upwards of $1200 for one (Credit: Happiest Baby, Inc).

This month I’ll be discussing an interesting paper that tackles a topic that every parent is familiar with - rocking a baby to sleep. Why is it that gentle rocking helps us sleep…be it in a crib, car, or a loved one’s arms?

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The Vestibular System Mediates Rocking-Induced Sleep

Credit: Kompotis et al., 2019

Kompotis and colleagues addressed this question using a basic science approach in mice. I was surprised a study like this hadn’t been done before, as everyone seems to just ‘know’ that gentle rocking promotes sleep, without knowing how or why. To test this question, they equipped mice with EEG/EMG electrodes to monitor brain and muscle activity (to determine sleep states), and monitored these signals for two days of no movement and one day of rocking in the horizontal plane (at 0.25, 0.5, 1.0, and 1.5 Hz) followed by a final stationary day (shown below). They chose to start with 0.25 Hz as that is a frequency that has shown to promote sleep in humans. They found that 1.0 Hz (i.e., 1 rocking motion/second) was the optimal frequency for mice, as 0.5 Hz was too little, and 1.5 Hz promoted non-rapid eye movement (NREM) sleep at the expense of REM sleep. They tried 2 Hz as well, but it was obviously discomforting to the mice so they capped out their experiments at 1.5 Hz.

1.0 Hz gentle rocking promotes NREM sleep in mice. (A) time series of sleep patterns (NREM sleep) during two baseline days without rocking (gray line), and one day of gentle rocking at different frequencies (0.25, 0.5, 1.0, and 1.5 Hz), followed by a final stationary day. Note how 1.0 and 1.5 Hz promote more NREM sleep that other frequencies. (B) comparisons of each frequency of rocking movements and how they influenced sleep. You can see that 1.5 Hz promoted the most increase in NREM sleep, but at the expense of REM sleep, therefore 1.0 Hz was deemed the ‘optimal’ frequency, as it promoted NREM sleep without disturbing REM sleep. (Credit: Kompotis et al., 2019)

1.0 Hz gentle rocking promotes NREM sleep in mice. (A) time series of sleep patterns (NREM sleep) during two baseline days without rocking (gray line), and one day of gentle rocking at different frequencies (0.25, 0.5, 1.0, and 1.5 Hz), followed by a final stationary day. Note how 1.0 and 1.5 Hz promote more NREM sleep that other frequencies. (B) comparisons of each frequency of rocking movements and how they influenced sleep. You can see that 1.5 Hz promoted the most increase in NREM sleep, but at the expense of REM sleep, therefore 1.0 Hz was deemed the ‘optimal’ frequency, as it promoted NREM sleep without disturbing REM sleep. (Credit: Kompotis et al., 2019)

To determine how rocking influenced the quality of sleep, in addition to the amount, the authors investigated the frequency components that make up the EEG (i.e., spectral analyses). In the sleep field, sleep intensity is known to correlate with increases in NREM delta power (slow waves; 0.5-4 Hz) in the EEG, so if rocking increased NREM delta power then we could speculate that it increased sleep quality in addition to amount. However, the only frequency that seemed to influence delta activity within the EEG was rocking at 1.5 Hz, where it actually caused delta power to decrease! This suggests that rocking at 1.0 Hz promotes more sleep, but this sleep is not any ‘deeper’ or ‘intense’ than normal sleep. Indeed, rocking too fast may decrease the quality of sleep.

Rocking at 1.0 Hz promotes a shift in theta frequencies from high to low during wakefulness. (Credit: Kompotis et al., 2019)

Rocking at 1.0 Hz promotes a shift in theta frequencies from high to low during wakefulness. (Credit: Kompotis et al., 2019)

Rocking at 1.0 Hz did, however, alter the spectral (EEG frequency) components of wakefulness and REM sleep, two states that exhibit predominant theta (~6-9 Hz) rhythmic oscillations. Specifically, 1.0 Hz rocking increased low-theta and decreased high-theta frequencies during both total wakefulness and a state known as ‘active’ or ‘theta-dominated wakefulness’ (TDW; see above). A shift in the spectral components of wakefulness from higher to lower frequencies is associated with building sleep pressure and a pending transition into sleep.

To investigate a mechanism for rhythmic rocking-induced sleep, the researchers tested whether otolithic organs of the vestibular system (which monitor our head’s linear acceleration) were necessary for the effect. They tested this using mice lacking functional otoliths (nicknamed tilted mice; Otop1-tlt/tlt). When they put these mice through their rocking experiment, they showed no enhancement of sleep like their counterparts with intact otolithic organs. A final question was whether the main driver of sleep was the rhythmic (i.e., frequency) component, or the linear acceleration applied to the mouse. To test this, they equalized the linear accelerations of both the 1.0 and 1.5 Hz rocking frequencies to 178 cm/s^2. When this was done, the effect on sleep was equalized, supporting the notion that linear acceleration, rather than frequency, is the important component of rocking-induced sleep. Interestingly, vestibular afferent nerves are 3-4 times less sensitive to stimuli than those in monkeys or humans. When the authors applied this conversion to the minimal sleep-enhancing linear acceleration that affected sleep in mice (79 cm/s^2), the numbers matched those that promote sleep in humans (20-26 cm/s^2).

An interesting note that the authors make is that other sensory modalities (e.g., vision, proprioception) could further be responsible for rocking-induced sleep, as tilted mice still had these systems intact. However, this is unlikely, as there was no compensatory effect in these mice, suggesting that the majority of the effect was driven by the vestibular system (see below).

A very interesting finding from this study is that the vestibular system contributes to sleep-wake control. As the authors discuss, future studies should examine downstream pathways relaying linear acceleration signals to known sleep circuitry (e.g., the pedunculopontine tegmentum) in the brain. So…next time you try to rock your baby or pet to sleep, remember that the linear acceleration is key!

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Mice lacking functional otolithic organs (tilted mice; Otop1^tlt/tlt above) do not enhance their sleep in response to rocking (Credit: Kompotis et al., 2019).

My Top 5 Coolest Studies of 2018!

Merry Christmas and Happy Holidays to everyone! I hope you all have a great new year :) I thought it would be fun to share my top 5 coolest studies of 2018 to round out the year! This is not a list of the ‘best’ studies of the year, as that is extremely hard to quantify (although all these are pretty stellar), so they are in no particular order. This is simply a list of papers that I thought tackled some interesting problems in a novel or unique way that made me think ‘damn that’s cool’. I hope you enjoy checking them out as much as I did! Click the paper titles for direct access to them.

#5 - Recovery of “lost” infant memories in mice

The phenomenon of “infantile amnesia”, where memories from early life are rapidly lost during development, has been known for quite some time. For example, almost no one has clear memories from when they were very little (e.g., 2 years old). Is this due to a problem in memory storage at this time…or are the memories stored properly, and we just can’t ‘retrieve’ them once we are adults? To investigate this question Guskjolen & colleagues used a transgenic approach to ‘optogenetically tag’ hippocampal neurons activated during the formation of an early fear memory in mice. Then, once the mice had grown up and ‘forgotten the fearful memory’, they reactivated those cells to see if they could ‘recover’ the lost memory. Indeed, they were able to do so suggesting that infantile amnesia is likely a result of retrieval failure rather than storage failure.

Reactivation of ‘lost’ memories in mice via optogenetic stimulation of neurons that were active during memory formation in early life. (A) Stimulation of cells activated in early life 30 days later caused mice to ‘freeze’, indicating they remembered the fearful memory; (B) this effect was long lasting, up to 90 days (longest they tested) (Credit: Guskjolen et al., 2018).

Reactivation of ‘lost’ memories in mice via optogenetic stimulation of neurons that were active during memory formation in early life. (A) Stimulation of cells activated in early life 30 days later caused mice to ‘freeze’, indicating they remembered the fearful memory; (B) this effect was long lasting, up to 90 days (longest they tested) (Credit: Guskjolen et al., 2018).

#4 - The neuronal gene arc encodes a repurposed retrotransposon gag protein that mediates intercellular rna transfer

Every once in a while there’s a paper that seems to turn biology on its head. This is an example of one of those papers, where the authors show that neurons can exchange transcriptional (RNA) information via secretion of ‘virus-like’ capsules composed of proteins thought only to be important in synaptic plasticity and memory formation. The proteins encoded by the gene arc seemed to form ‘virus-like’ structures that were able to travel between cells to exchange RNA information. This is because the gene that encodes these proteins shares an ancestry with those that made up ancient retroviruses! This type of inter-cellular communication has never been described (in mammals) and opens up a completely new regulatory and signaling pathway that may be important in neurodegenerative disease.

Intercellular transfer of messenger RNA via  arc -encoded virus like proteins! (Credit: Pastuzyn et al., 2018)

Intercellular transfer of messenger RNA via arc-encoded virus like proteins! (Credit: Pastuzyn et al., 2018)

#3 - Parallel circuits from the bed nuclei of stria terminalis to the lateral hypothalamus drive opposing emotional states

Ok I had to throw in this cool study out of my lab spearheaded by the great Will Giardino! Hypocretin/orexin neurons in the lateral hypothalamus modulate positive and negative aspects of arousal (e.g., promoting arousal in both rewarding, pleasurable, and stressful conditions). How a single neural population does this is unclear, but likely depends on differential inputs arriving from other brain areas. Giardino & colleagues demonstrated that different subsets of neurons in the bed nuclei of stria terminalis (BNST; extended amygdala) send projections to synapse onto hypocretin/orexin neurons, resulting in opposing responses depending on emotional state (positive or negative)!

Different populations of neurons in the BNST respond to positive (e.g., female mouse scent) or negative (e.g., predator odor) emotional stimuli . Time zero is when the stimulus was presented to the test mouse. On the Y axis you can see the fluorescent signal from CRF or CCK neurons in the BNST during stimulus presentation (credit: Giardino et al., 2018).

Different populations of neurons in the BNST respond to positive (e.g., female mouse scent) or negative (e.g., predator odor) emotional stimuli . Time zero is when the stimulus was presented to the test mouse. On the Y axis you can see the fluorescent signal from CRF or CCK neurons in the BNST during stimulus presentation (credit: Giardino et al., 2018).

#2 - in toto imaging and reconstruction of post-implantation mouse development at the single cell level

Can we image every cell as a mouse develops to understand how a jumble of cells coordinates to make a complex organism like a mouse? Turns out we can! This one is just really damn cool. Check out the video below with a better explanation than I could ever give.

#1 - expanding the optogenetics toolkit with topological inversion of rhodopsins

What if we flipped the excitatory optogenetic protein channelrhodopsin upside down? Turns out it creates a pretty potent inhibitor of neural activity! I include this because it is such a simple idea, that turned out way better than I would have thought it could!…and the new opsin is called “FLInChR” which I thought was funny.

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BOnus! - Medial preoptic circuit induces hunting-like actions to target objects and prey

How do animals engage appropriate behaviors necessary to survive, like stalking, hunting and chasing prey? Park & colleagues discovered that neurons in the medial preoptic area of the hypothalamus projecting to the ventral periaqueductal gray in the midbrain promote these behaviors in mice. Activation of this circuit (MPA—>vPAG) caused mice to chase, leap after, and hunt inanimate objects! Make sure to check out the figure and video link below to see this hunting behavior in action!

Activation of the MPA—&gt;vPAG circuit promoted hunting-like behavior in mice. Here, the researchers drew (with a little ball on a stick) the letters “B” and “G”. When the laser was off, mice were scared of the object and stayed towards the edge of the arena, but when the laser was on, they hunted the object, so closely that they essentially drew the letters with their body chasing the ball!

Activation of the MPA—>vPAG circuit promoted hunting-like behavior in mice. Here, the researchers drew (with a little ball on a stick) the letters “B” and “G”. When the laser was off, mice were scared of the object and stayed towards the edge of the arena, but when the laser was on, they hunted the object, so closely that they essentially drew the letters with their body chasing the ball!

There were many more studies that I wanted to include on this list…but I thought 5 was a good number to shoot for as 10 would have been too much! Maybe I’ll do another list for the most ‘impactful’ studies of 2018…but that will have to wait for another time (as it takes time to assess impact! All the best and happy holidays!! —JCB

Defined Paraventricular Hypothalamic Populations Exhibit Differential Responses to Food Contingent on Caloric State

Welcome to our Monthly Journal Club! Each month I post a paper or two that I have read and find interesting. I use this as a forum for open discussion about the paper in question. Anyone can participate in the journal club, and provide comments/critiques on the paper. This month’s paper is “Defined Paraventricular Hypothalamic Populations Exhibit Differential Responses to Food Contingent on Caloric State” by Michael Krashes and colleagues at The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). I will provide a brief overview of the techniques/approaches used to make it more understandable to potential non-expert readers. If I am not familiar with something, I’ll simply say so.

Uncovering the neural populations responsible for appetite, feeding, metabolic control, and hedonic (rewarding/pleasurable) responses to food is essential for crafting new therapies for obesity, anorexia, and sickness-induced (e.g., cancer) appetite suppression. The hypothalamus is a critical structure controlling food intake and appetite, however the distinct roles of specific neural sub-populations in appetite control is not clear. If we can learn the ways different genetically-defined neural populations contribute to feeding, then we can potentially design drugs or other therapies that specifically target only those cells (to have the maximum effect with little off target problems).

The paraventricular hypothalamus (‘around the ventricle’; PVH) is an especially important hypothalamic node in appetite control, illustrated by the fact that lesions of this area cause massive obesity due to large increases in food intake. It is comprised of several neuromodulator populations (all expressing the critical transcription factor SIM1) that can be genetically targeted based on their expression of specific proteins: (1) glucagon-like peptide 1 receptor [Glp1r]; (2) melanocortin-4 receptor [Mc4r], (3) oxytocin [Oxt], and (4) corticotropin-releasing hormone [Crh]. These markers are not clear-cut, and many cells express more than one. Regardless, expression of these primary proteins demonstrates that each of these populations are largely independent from one another. The roles each of these play in appetite, food intake, and metabolism are unclear. Michael Krashes’ team tackled this problem using a variety of techniques including fiber photometry, immunohistochemistry, electrophysiology, and DREADDs.

The paraventricular hypothalamus (PVH; pictured above) contains many different unique cell types. The above image shows little overlap between two primary cell types in this region, those expressing melanocortin-4 receptor (Mc4r) and oxytocin (Oxt) (2.1% overlap).  (Credit: Li et al., 2018)

The paraventricular hypothalamus (PVH; pictured above) contains many different unique cell types. The above image shows little overlap between two primary cell types in this region, those expressing melanocortin-4 receptor (Mc4r) and oxytocin (Oxt) (2.1% overlap). (Credit: Li et al., 2018)

They started by examining the activity of these genetically defined populations throughout the entire PVH following an overnight fast or after 2 hours of re-feeding (using the immediate early gene cFos). They observed state-dependent (fasted or re-fed) changes in cFos expression, with more cFos induction in the rostral (towards the front) portions of the PVN than the medial (middle) or caudal (towards the back) regions. Looking at specific subsets of cells expressing cFos following this experiment, they demonstrate that nearly all (except Oxt) neuronal populations examined show change in activity following fasting and re-feeding (see below). Notably, Glp1r-expressing neurons more than doubled their activity following re-feeding compared to the fasting state.

Induction of cFos in genetically-defined neuronal subtypes within the PVH after fasting or fasting followed by a 2-hour re-feeding session. As you can see, nearly all subtypes showed changes in activity (except Oxt neurons) with these manipulations. Some increased activity (Glp1r and Mc4r) while Crh neurons decreased their activity upon re-feeding. (cFos in RED with cell types in GREEN).  (Credit: Li et al., 2018)

Induction of cFos in genetically-defined neuronal subtypes within the PVH after fasting or fasting followed by a 2-hour re-feeding session. As you can see, nearly all subtypes showed changes in activity (except Oxt neurons) with these manipulations. Some increased activity (Glp1r and Mc4r) while Crh neurons decreased their activity upon re-feeding. (cFos in RED with cell types in GREEN). (Credit: Li et al., 2018)

The researchers then attempted to delve deeper into this discovery through electrophysiological techniques. Using a very tiny electrode to monitor the activity of single cells in real-time, they were unable to find any differences in Glp1r neuronal electrical capacitance, resistance, holding current, or resting membrane potential. However, they did note that re-feeding increased the firing rate of these neurons, supporting their immunohistochemical (cFos) data. To move from single-cell to population level analyses, the researchers used adeno-associated viral vectors to express a fluorescent calcium indicator in their neurons of choice (using the Cre/Lox system). Then, they used fiber photometry to measure the activity of these cells in freely moving mice (see below). Calcium dynamics are proxy measures of neuronal activity, as calcium concentrations rapidly change when neurons are active.

Interestingly, the researchers found that Glp1r-expressing neurons were relatively silent during fasting, or when the mice were full. However, if food was presented to the mouse when it was hungry (fasted), these cells rapidly increased their activity! This suggests that the activity of these cells is not driven just by the availability of food, but the caloric/metabolic state of the animal (i.e., these cells act as a coincidence detector)!

Fiber photometry allows for direct imaging of neural population activity in freely moving mice. In panel (A) we can see the placement of the optical fiber in the PVH to record signals coming from Glp1r-expressing neurons. Panel (B) shows that these cells do not respond to new objects placed in the environment, but do respond strongly to food when the mouse is hungry (fasted). Notably these cells don’t respond to food when the mouse is full (fed). This effect is reduced when the food is inaccessible (minutes 0-20 of panel C), but pronounced when the mice can freely access the food (minutes 20-40 of panel C). In panel (D) we can see that this effect extends to high fat diet (HFD) in addition to the mouse’s regular chow. Crh-expressing neurons showed the opposite patten (i.e., their activity was suppressed upon chow presentation after fasting; not shown)  (Credit: Li et al., 2018).

Fiber photometry allows for direct imaging of neural population activity in freely moving mice. In panel (A) we can see the placement of the optical fiber in the PVH to record signals coming from Glp1r-expressing neurons. Panel (B) shows that these cells do not respond to new objects placed in the environment, but do respond strongly to food when the mouse is hungry (fasted). Notably these cells don’t respond to food when the mouse is full (fed). This effect is reduced when the food is inaccessible (minutes 0-20 of panel C), but pronounced when the mice can freely access the food (minutes 20-40 of panel C). In panel (D) we can see that this effect extends to high fat diet (HFD) in addition to the mouse’s regular chow. Crh-expressing neurons showed the opposite patten (i.e., their activity was suppressed upon chow presentation after fasting; not shown) (Credit: Li et al., 2018).

They repeated this experiment with all of the four cell types they examined previously. Mc4r- and Oxt-expressing neurons showed little change in these experiments. However, Crh-expressing neurons showed nearly the opposite pattern to that of Glp1r neurons, even though they are in the same brain area! This demonstrates that Glp1r and Crh neurons reciprocally track caloric state and respond in opposite patterns upon feeding after fasting. So what happens if the researchers manipulate these cells?

To do this, they used excitatory (Gq-coupled) or inhibitory (Gi-coupled or Kappa Opioid Receptor (KORD)) DREADDs. These designer receptors do not respond to any endogenous molecule in the brain, but do respond to the inert compound clozapine-N-oxide (CNO). This allows researchers to manipulate genetically-defined (e.g., Glp1r, Mc4r, etc..) neural populations with good temporal precision via systemic injections of CNO. They focused on Glp1r neurons as these showed the strongest responses during fasting and re-feeding. Activation of these neurons strongly suppressed appetite, while inhibition of these cells promoted feeding. Pre-treatment with the anorexic drug liraglutide (Lira) prevented this increased feeding response to inhibitory DREADD signaling, suggesting that Lira “can act redundantly at multiple sites and/or its action in the PVH is not critical in modulating food intake”.

Finally, the researchers examined what happens when these different PVH populations are silenced over a long period of time (weeks and months), as all of their prior manipulations only looked at relatively short time-scales (minutes to hours). To silence these neurons, they used a virus encoding the tetanus toxin (see below), which permanently silences synapses primarily via cleavage of the protein synaptobrevin. They followed these mice for 16 weeks after viral injections, examining body weight and food intake throughout the course of the experiment (see below).

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Tetanus toxin-induced chronic silencing of PVH neural subpopulations differentially affects body weight gain and food intake. In panels (A-B) we can see how silencing Glp1r neurons drastically increases body mass and food intake while panels (C-D) show a similar effect of Mc4r silencing. However, no effect was observed upon Oxt or Crh neuronal silencing. This suggests that PVH subpopulations do not serve redundant functions in weight gain and food intake. (Credit: Li et al., 2018).

Chronic silencing of Glp1r or Mc4r-expressing neurons strongly induced obesity and hyperphagia (overeating). These traits did not emerge in mice with silenced Oxt or Crh PVH neural populations. Further research needs to be done to understand how these diverse neural populations integrate and compute all that encompasses ‘appetite’ and ‘feeding’ (i.e., meal initiation, planning, satiation, and meal termination). This is a strong first step in understanding the differential responses and functions of these neurons, which will potentially lead to new treatments for metabolic diseases like obesity, anorexia, or sickness-induced appetite suppression. Future work should aim to map the afferent and efferent projections (incoming and outgoing connections) to and from these neurons, and find the critical downstream pathways controlling their anorexic or feeding effects.

Now join the discussion! Click the post title above and leave a comment!

#SFN2018 Day 4: Brain's Reward System Dictates Sleep and Wakefulness

The Ventral Tegmental Area - Reward and Arousal

During day 4, one of my favorite poster sessions took place (Sleep systems, and sleep regulators). Here, a poster that grabbed my attention was titled “GABA and glutamate networks in the VTA regulate sleep and wakefulness” from Xiao Yu, a member of William Wisden 1’s lab at Imperial College London.

Dopamine (green) and GABA (red) expressing neurons in the mouse ventral tegmental area (VTA; outlined) studies by Xiao Yu and colleagues demonstrates that these neurons bidirectionally regulate sleep and wakefulness &nbsp;(Credit: Jeremy C Borniger, PhD; Stanford University)

Dopamine (green) and GABA (red) expressing neurons in the mouse ventral tegmental area (VTA; outlined) studies by Xiao Yu and colleagues demonstrates that these neurons bidirectionally regulate sleep and wakefulness (Credit: Jeremy C Borniger, PhD; Stanford University)

The ventral tegmental area 1 (VTA) is largely known as the seat of the brain’s ‘reward’ system. This is because neurons in this area are the primary source of all the brain’s dopamine, a ‘feel good molecule’ that is responsible for the rewarding effects of drugs, sex, and all things fun. Neurons in this area signal reward by calculating the so called ‘reward prediction error’. This is the difference between an expected and unexpected reward. For example, if you expect to get one piece of candy from your mom, but then she gives you 100 pieces of your favorite treat, neurons in the VTA calculate the difference, fire, and release a large surge of dopamine proportional to the reward ‘error’. This signal acts to reinforce the behaviors that led to the unexpected reward. A ‘good’ error like this is a called a ‘positive prediction error’ while the opposite, where a reward is omitted when it is expected, is called a ‘negative prediction error’. Negative prediction errors result in less dopamine release, and therefore aversion to the behaviors that led to this unexpected ‘disappointment’. As you may well predict, drugs of abuse like cocaine, alcohol, heroin, and others elicit a strong positive prediction error, resulting in a lot of dopamine release and reinforcement of drug seeking behavior.

In addition to dopamine neurons in the VTA, there exists two other primary populations, one that expresses the inhibitory neurotransmitter GABA, and another that produce primarily glutamate, an excitatory neurotransmitter. Recent research has demonstrated that in addition to their roles in reward signaling, VTA-dopamine neurons strongly promote wakefulness, likely through their projections to the nucleus accumbens (NAc) (see image below). How other VTA populations relate to wake/sleep states remains unknown.

Activation of VTA-dopamine neurons (TH-positive) strongly promotes wakefulness. You can see that when these neurons are stimulated (by light sensitive ChR2 activation), the mice rapidly wake up (panels c,d,e) (Credit: Eban-Rothschild et al., 2016;&nbsp; Nature Neuroscience )

Activation of VTA-dopamine neurons (TH-positive) strongly promotes wakefulness. You can see that when these neurons are stimulated (by light sensitive ChR2 activation), the mice rapidly wake up (panels c,d,e)(Credit: Eban-Rothschild et al., 2016; Nature Neuroscience)

To investigate these other populations, Xiao Yu and colleagues used optogeneticschemogeneticsfiber photometry (Ca2+), and neuropharmacology to untangle the roles GABA and glutamate neurons in the VTA play in sleep/wake states.

First, they identified that most glutamate neurons in the VTA also express NOS1 (nitric oxide synthase 1), and therefore used NOS1 and vglut2-cre mice to specifically target these neurons for manipulation. VGLUT2 stands for ‘vesicular glutamate transporter 2’, and is expressed on virtually all subcortical neurons that signal via glutamate. Using viral vectors to specifically express the stimulatory (hM3Dq) or inhibitory (hM4Di) DREADDs, they demonstrated that stimulation of VTA-glutamate neurons strongly promotes wakefulness while inhibition of this population strongly promotes sleep. To investigate how these neurons promoted arousal, they stimulated their projections in different brain regions using optogenetics. They focused on two primary output regions, the lateral hypothalamus (which contains many sleep-related neural populations) ,and the nucleus accumbens. Stimulation of glutamate nerve terminals arriving from the VTA to the lateral hypothalamus strongly promoted wakefulness, while stimulation of similar fibers arriving at the NAc had a less pronounced effect. This suggests that VTA-glutamate neurons likely promote wakefulness via dual projections to the lateral hypothalamus and NAc. Importantly, the natural activity of these neurons (examined via fiber photometry) was shown to be highest during wakefulness and REM sleep compared to NREM sleep. This suggests that they normally change their firing rates during distinct vigilance states.

Example of a fiber photometry trace showing the activity of GABA neurons across sleep-wake states. As you can see, these neurons are mostly active during wakefulness and REM sleep compared to NREM sleep (wake = white background, NREM = blue, REM = red) &nbsp;(Credit: Jeremy C Borniger, PhD, Stanford University)

Example of a fiber photometry trace showing the activity of GABA neurons across sleep-wake states. As you can see, these neurons are mostly active during wakefulness and REM sleep compared to NREM sleep (wake = white background, NREM = blue, REM = red) (Credit: Jeremy C Borniger, PhD, Stanford University)

Similar experiments were done to examine the VTA-GABA population. Activation of these neurons (via DREADDs or optogenetics) strongly promoted sleep, while inhibition of this population powerfully promoted wakefulness. Activation of GABA nerve terminals from the VTA to the LH strongly promoted sleep, an opposite effect to that of glutamate stimulation in LH. This effect was partially inhibited when stimulations occurred in combination with a drug (gabazine) that inhibits GABA signaling. This suggests that it is GABA (and not other molecules) released by these neurons that is largely responsible for their effects of sleep/wake states. Finally, they hypothesized that this effect could be driven by GABA’s inhibitory influence over VTA-dopamine populations. By inhibiting VTA-GABA neurons in combination with dopamine blockade, they were able to (mostly) eliminate the effect of VTA-GABA silencing on wakefulness. This supports a model in which VTA-GABA neurons inhibit neighboring VTA-dopamine neurons in order to promote sleep.

This is an exciting research area as a major problems for drug abuse victims are insomnia and chronic fatigue, which inevitably lead to the reinstatement of drug seeking behavior. Sleep drugs targeting the VTA could really help rectify general sleep problems and specifically those related to drug abuse.

Feel free to follow me on twitter here for more!

#SFN2018 Day 3: Chili Peppers, Inflammatory Pain...and I Won an Award! :)

During the day 3 AM poster session, I managed to snag Sampurna Chakrabarti (Follow her on Twitter), a winner of the SFN Trainee Professional Development Award, to talk about her recent research on mechanisms of inflammatory pain.

To study this, she and her colleagues injected Complete Freund’s Adjuvant (CFA) into one of the knees of a mouse, leaving the other knee as a ‘control’. ‘Adjuvants’ like CFA elicit a strong inflammatory response, and can boost adaptive (primarily lymphocytes like T cells and B cells) immunity. An easy way to remember which cells are which is that T-cells mature in the Thymus gland, while B cells mature in the Bone marrow. Injections of CFA into joints is a widely used model with which to elicit an inflammatory response and study diseases like arthritis.

The dorsal root ganglia (there’s two at almost all vertebrae) relay sensory information arriving from everywhere in the body. They serve a key role in reflex responses (e.g., to a hot grill) that occur before the brain becomes “aware” that something happened, and they also act as a highway to transmit information to the spinal cord and up to the brain &nbsp;(Credit:&nbsp; Quora.com )

The dorsal root ganglia (there’s two at almost all vertebrae) relay sensory information arriving from everywhere in the body. They serve a key role in reflex responses (e.g., to a hot grill) that occur before the brain becomes “aware” that something happened, and they also act as a highway to transmit information to the spinal cord and up to the brain (Credit: Quora.com)

Causing this inflammatory reaction in the joint causes mice (and people) to experience pain, severely impairing one’s quality of life and in some cases, mobility. The open question is, “how does this inflammatory reaction cause this pain response?” and “can we prevent this to provide relief for patients with joint pain?

To measure pain responses in mice, they used a quick behavioral assay that determines how much a mouse digs down into the bedding in it’s cage. Mice naturally dig to form nests and burrows, while mice in pain can’t muster up enough energy to complete this task. As a secondary measure, Sampurna and her colleagues also measured the swelling of the knee as an index of inflammation. Their hypothesis was that inflammation sensitizes sensory neurons (located in the dorsal root ganglia ; DRG) relaying information from the knee to the spinal cord, leading to joint pain.

But how could inflammation ‘sensitize’ a neuron to joint pain? The family of proteins called TRPV (‘trip-Vee’) receptors is widely known to be important in the recognition of painful stimuli. TRPV1, specifically, is most famous for its alternative name, the ‘capsaicin receptor’. Capsaicin is the molecule in chili peppers that causes the painful burning sensation, making it a useful ingredient in things that cause pain, like pepper spray.

Neurons projecting to the inflamed knee (‘knee neurons’ labeled with FB) from the dorsal root ganglion expressed a much higher amount of the capsaicin receptor (TRPV1), without changes in the receptor for&nbsp;   Nerve Growth Factor   &nbsp;(a molecule associated with increased neural sensitivity; TrkA) &nbsp;(Credit: Chakrabarti et al., 2018;&nbsp; Neuropharmacology )

Neurons projecting to the inflamed knee (‘knee neurons’ labeled with FB) from the dorsal root ganglion expressed a much higher amount of the capsaicin receptor (TRPV1), without changes in the receptor for Nerve Growth Factor (a molecule associated with increased neural sensitivity; TrkA) (Credit: Chakrabarti et al., 2018; Neuropharmacology)

The researchers assessed whether dorsal root ganglion neurons projecting to the knee were hypersensitive by recording from- and stimulating them using electrophysiology. To identify only neurons that project to the knee of interest, they injected a retrograde label into the joint (called Fast Blue ; FB) to label upstream neurons projecting to the knee. Because neurons labeled with FB ‘glow’ under a microscope, it is easy to see and manipulate only the neurons of interest (so called ‘knee-neurons’).

They observed that following CFA administration, these neurons had a lower threshold for firing action potentials in response to a number of stimuli, including the chili pepper compound, capsaicin. This indicated that they were more sensitive to noxious stimuli, which could explain the sensation of pain elicited by the inflamed joint. But what is causing this ‘sensitization’? They used immunohistochemistry to show that knee-neurons express much higher levels of the capsaicin receptor and the combination of the capsaicin receptor and TrkA (the receptor for nerve growth factor; NGF). This supported the idea that inflammation up-regulates NGF and TRPV1 signaling to sensitize neurons, resulting in pain.

Blocking TRPV1 signaling using a receptor antagonist prevents inflammatory joint pain elicited by injections of CFA. In panels B and C you can see that without the antagonist, the mice fail to show their normal happy digging behaviors. However, with the antagonist, their behavior returns to normal, indicating that they are no longer in pain &nbsp;(Credit: Chakrabarti et al., 2018;&nbsp; Neuropharmacology )

Blocking TRPV1 signaling using a receptor antagonist prevents inflammatory joint pain elicited by injections of CFA. In panels B and C you can see that without the antagonist, the mice fail to show their normal happy digging behaviors. However, with the antagonist, their behavior returns to normal, indicating that they are no longer in pain (Credit: Chakrabarti et al., 2018; Neuropharmacology)

As a final test to see if TRPV1 is really the culprit, they repeated their digging behavior assay after CFA administration with or without the TRPV1 receptor blocker (antagonist) “A-425619”. When the actions of TRPV1 were blocked, mice with inflamed knees no longer showed signs of pain, suggesting that manipulating this pathway may be a good strategy to reduce joint pain. .

Indeed, the researchers are now moving their findings in mice onto humans to see if this effect can be repeated to improve quality of life in patients with arthritis and other joint diseases!

WC Young Recent Graduate Award

Another highlight of day 3 was being awarded the WC Young Recent Graduate Award from the Society for Behavioral Neuroendocrinology (SBN)!

William C. Young was one of the founders of modern behavioral neuroendocrinology. The SBN honors WC Young through the "WC Young Recent Graduate Award" (initially created in the 1960's by one of the society's predecessors, the West Coast Sex Conference). Selection criteria for the WC Young Recent Graduate Award are based on the doctoral dissertation, scholarly productivity, and letters of reference.

I was awarded for my work on brain-tumor interactions (mediated by the satiety hormones leptin and ghrelin). You can see my paper detailing this work here.

Me (left) and SBN President Rae Silver, a legend in the field for her  work on circadian rhythms , awarding me the WC Young Recent Graduate Award at the SBN Social event in the Marriott Marquis next to the San Diego Convention Center.

Me (left) and SBN President Rae Silver, a legend in the field for her work on circadian rhythms, awarding me the WC Young Recent Graduate Award at the SBN Social event in the Marriott Marquis next to the San Diego Convention Center.

I am honored to receive the WC Young Recent Graduate award from the Society for Behavioral Neuroendocrinology! WC Young was one of the first to recognize that many hormones play different roles depending on developmental stage. In early life, they act to ‘organize’ a system (e.g., reproductive), and later in life they ‘engage’ or ‘activate’ this system and the behaviors necessary for survival (e.g., mating, fighting, feeding…). In this way, hormones help build the hardware AND run the software!

He also recognized the myopic view of testing only animals of a single species, of a single sex, or of a single age…as problematic. This has only gotten more relevant as years have passed. We need to reinvigorate comparative neuroscience, and bring it along into the 21st century.

See a short piece I wrote about reinvigorating comparative neuroscience here.

That’s all for day 3! Tomorrow (the 6th) is jam packed with interesting stuff. So I’ll try to go HAM.

#SFN18 Day 2 Recap: Controlling Neurons with Ultrasound and a Novel Avenue for Depression Treatment?

Sonogenetics - A non-invasive method to manipulate neurons

During the AM poster sessions, one that caught my eye was from the Chalasani Lab at the Salk Institute in La Jolla, California. Several years ago, they described a method by which they could control neural activity in the nematode worm C. elegans using focused ultrasound. This paper demonstrated that ectopic expression of the mechanosensitive channel TRP-4 in neurons rendered them sensitive to ultrasound stimulation. This is a big deal because other so called ‘non-invasive’ neural manipulation techniques like optogenetics require a fiber optic probe to be placed near the cells of interest, making the manipulation of deep brain structures with high temporal precision tedious.

Sonogenetics allows for non-invasive control of neural activity. Here, in C. elegans with PVD neurons expressing the ultrasound-sensitive protein (TRP-4) and the calcium indicator GCaMP3, we can see that ultrasound exposure drastically increases calcium activity in these neurons, indicating ultrasound mediated neural activation. Warmer colors indicate more GCaMP3 fluorescence = more activity &nbsp;(Credit: Ibsen et al., 2015;&nbsp; Nature Communications )

Sonogenetics allows for non-invasive control of neural activity. Here, in C. elegans with PVD neurons expressing the ultrasound-sensitive protein (TRP-4) and the calcium indicator GCaMP3, we can see that ultrasound exposure drastically increases calcium activity in these neurons, indicating ultrasound mediated neural activation. Warmer colors indicate more GCaMP3 fluorescence = more activity (Credit: Ibsen et al., 2015; Nature Communications)

This was to be just the first step in a long process of isolating different mechanosensitive proteins and screening them in mammalian cells to find one just right for use in more complicated organisms. During the poster session this morning, Corinne Lee-Kubli, a post-doc in the Chalasani lab, provided an update on the progress in sonogenetics to date.

Using an in vitro screening method to identify ultrasound-sensitive mechanoreceptors, Corinne expressed a large variety of putative channels in cells in a dish. These cells were co-transfected with the calcium indicator GCaMP6f, a powerful and fast reporter of cell activity. The fluorescent signal was then monitored before, during, and after ultrasound stimulation in a high-throughput manner.

A subset of the putative mechanoreceptors were packaged into cre-dependent AAV-viral vectors and delivered to AgRP neurons deep in the brain (arcuate nucleus) of AgRP-cre mice. Validation of the excitatory actions of the ultrasound sensitive protein was done using a feeding assay, as AgRP neurons strongly promote feeding. Upon ultrasound (10 MHz) stimulation of the head (through the skull and entire brain), a few of the channels strongly promoted feeding responses, a trait not observed in mice expressing the control virus (encoding GFP). An important note is that ultrasound stimulation alone had no effect on feeding responses, indicating a specific effect of the putative mechanoreceptor in AgRP neurons.

AgRP neurons in the arcuate nucleus expressing the calcium indicator GCaMP6. These cells are powerful regulators of feeding behavior and metabolism  ( Credit: Srisai et al., 2017 ;  Nature Communications )

AgRP neurons in the arcuate nucleus expressing the calcium indicator GCaMP6. These cells are powerful regulators of feeding behavior and metabolism (Credit: Srisai et al., 2017; Nature Communications)

This proof-of-principle application represents a significant advancement for the nascent field of sonogenetics. Much more research needs to be done to discover the most potent and specific ultrasound sensitive protein, the kinetics of said protein, and additional tools for cell inhibition. In the future, we can expect to see multiple channels expressed in different cellular populations, each sensitive to different ultrasound frequencies. Then, ‘nested’ delivery of different ultrasound waveforms could putatively activate and/or inhibit discrete cell populations across the entire brain, simultaneously or with tight temporal control.

The power of this technique is impressive, as ultrasound can easily reach through the entire mouse brain at 10 MHz, and can go much deeper (e.g., in rat or primate brain) using lower frequencies. I look forward to what’s to come!

Bidirectional Control of Depression Through Hypothalamic Feeding Circuits

Speaking of the arcuate nucleus, during the day 2 poster sessions, one that caught my eye was titled “Chronic unpredictable stress modulates neuronal activity of AgRP and POMC neurons in hypothalamic arcuate nucleus” presented by Xing Fang in the Xin-yun Lu lab at the Medical College of Georgia at Augusta University.

Agouti-related peptide (AgRP) and pro-opiomelanocortin (POMC) neurons in the arcuate nucleus strongly regulate feeding behavior and food intake. Broadly, AgRP neurons promote feeding (orexigenic), while POMC neurons work in a reciprocal manner to suppress feeding (anorexigenic).

AgRP neurons in the arcuate promote food intake while POMC neurons inhibit food intake via their actions on downstream MC4R- expressing neurons in the paraventricular nucleus  (Credit: Carol A. Rouzer, Vanderbilt University)

AgRP neurons in the arcuate promote food intake while POMC neurons inhibit food intake via their actions on downstream MC4R- expressing neurons in the paraventricular nucleus (Credit: Carol A. Rouzer, Vanderbilt University)

Depression is characterized by aberrant responses to environmental stimuli. For example, chronic psychological stress can promote depression in humans and animal models. Stress-induced depression is characterized by anhedonia (not enjoying what you used to love), lethargy and despair, and changes in feeding behavior and appetite. How does stress cause these behaviors to come about?

Using in vivo electrophysiology, behavioral assays, and DREADDs, Fang and colleagues investigated the role of hypothalamic AgRP and POMC neurons (two populations that powerfully control appetite) in mediating these behaviors.

This work builds on previous studies by the group, long linking depressive-like behavior to alterations in feeding and satiety hormones such as leptin.

To induce depression in mice, the researchers used a technique called ‘chronic unpredictable stress’ (CUS). This model strongly promotes a depression-like state after 10 days of unpredictable stress where mice go through a gamut of constant light exposure, tail pinches, restraint, and shock stimuli, among others.

Viral injections into the arcuate nucleus of POMC-Cre mice (left panels; projections in red) shows their wide axonal distribution throughout the brain. Similarly, injections into the arcuate nucleus of AgRP-cre mice demonstrate that they also project throughout the brain, although in a different pattern (right panels, projections in green)  (Credit:  Wang et al., 2015 ;  Frontiers in Neuroanatomy )

Viral injections into the arcuate nucleus of POMC-Cre mice (left panels; projections in red) shows their wide axonal distribution throughout the brain. Similarly, injections into the arcuate nucleus of AgRP-cre mice demonstrate that they also project throughout the brain, although in a different pattern (right panels, projections in green) (Credit: Wang et al., 2015; Frontiers in Neuroanatomy)

Through their electrophysiological recordings, the researchers demonstrated that CUS decreased the firing rate of AgRP neurons but increased the firing rate of POMC neurons. When they tested the role of AgRP neurons in depressive-like behavior using stimulatory (Gq) or inhibitory (Gi) DREADDs, they were able to elicit opposite responses. Stimulation of these neurons improved depressive-like behavior, while inhibition promoted it.

Together, these studies suggest that AgRP and POMC neurons play an important role in stress-related adaptive behavior. Importantly, they provide a novel circuit related to depression which may be targetable for the treatment of the disease through pharmacological agents or lifestyle changes.

That’s all from me for day two, where I tried to focus more on posters than talks! Unfortunately I could only highlight 2 out of >5,000 interesting ones!

Follow me on twitter for live updates and stay tuned for more!

#SFN18 Day 1 Recap: Circadian Surprises and Blowing Up Brains!

Day and night, breakfast and dinner, winter and summer, wake and sleep…our lives are dominated by interacting rhythms in our environment and our behavior. Why is it that we sleep at night and not during the day? Why are heart attacks and strokes more common in the morning than the evening? How do animals adapt to winter and summer? Why do we get jet-lag, and what is it, exactly? All these questions revolve around a central subject in neuroscience: circadian clocks. During day 1 of the Society for Neuroscience annual meeting in San Diego, CA, I was treated to a nanosymposium (timely insights in circadian regulation) highlighting new and exciting research in this area. Chaired by Steven Brown and Alessandra Porcu, this session covered all aspects of circadian biology, from behavior to neuronal circuits, and from synapses to molecules.

The suprachiasmatic nuclei (pictured above) serve as the master clocks controlling mammalian circadian rhythms  (Credit: Jeremy C. Borniger, PhD; Stanford)

The suprachiasmatic nuclei (pictured above) serve as the master clocks controlling mammalian circadian rhythms (Credit: Jeremy C. Borniger, PhD; Stanford)

Here, I highlight a few of the talks that I found the most interesting. Unfortunately, I am not able to cover everything, and some really cool stuff slipped through! That’s the downside of this immense conference…there’s never enough time to see everything!

Two talks on the same protein (one in flies and the other in mammals) grabbed my attention. These talks were given by Masashi Tabuchi and Benjamin Bell, two researchers from Johns Hopkins University. During Masashi’s talk, he described a potential mechanism by which the protein Wide Awake (WAKE) regulates sleep/wake cycles in flies.

WAKE regulates sleep quality through appropriate timing of neural firing codes  (Credit: Tabuchi et al., 2018;  Cell )

WAKE regulates sleep quality through appropriate timing of neural firing codes (Credit: Tabuchi et al., 2018; Cell)

He showed that irregular neural firing rates during the day (regulated by WAKE) promote arousal while regular firing patterns during the night promote sleep.

Ben Bell followed up his talk by taking their findings in flies to mammals, describing a mammalian ortholog to the fly WAKE protein (called mWAKE). mWAKE is highly enriched in the master clock, the suprachiasmatic nucleus (SCN) suggesting it plays a role in circadian time keeping or regulation.

Unlike in flies, knockout of mWAKE in mice only caused mild problems in sleep/wake states. However, through measuring locomotor activity, the researchers found that these mice were extremely hyperactive (>5 times more active than wild-type mice). Curiously, this trait (phenotype) only came about during the dark phase (mice are nocturnal, so active during the dark phase). To investigate this further, the researchers examined the firing rates of SCN neurons during the day and night. Normal mice have a large difference between the night and day in SCN firing rates, with peak neural activity occurring during the day. However, mWAKE knockout mice showed no difference between day and night, with firing rates remaining high all the time!

Additionally, cells lacking mWAKE showed blunted responses to the inhibitory neurotransmitter GABA, and this lack of inhibition may explain the hyperactive profile mice lacking mWAKE had. Finally, they examined (using an mWAKE reporter mouse) where mWAKE expressing cells project to throughout the brain. They found that cell bodies were distributed throughout the brain, in all major arousal centers. Importantly, they seemed to be discrete from other neuromodulator systems present in these areas, like hypocretin/orexin neurons in the lateral hypothalamus, or histamine neurons in the tuberomammillary nucleus.

Significant more research is required to fully understand the role this protein plays in sleep/wake states. Is it a ‘master regulator’ of arousal? Does it interact with every ‘arousal center’ differently or does it have a distributed ‘homogenous’ effect across the brain. When does mWAKE start to express during development? Does this coincide with changes to sleep-wake behavior during early age? I’m excited to follow this story going forward!

Expansion Microscopy - ‘Just add Water’

Microscopes are getting beefier and beefier, more complex and expensive, with the sole purpose of being able to see tiny, tiny things just a little bit better. Enter ‘expansion microscopy’, an idea that literally works in the opposite direction to that goal. Instead of ‘zooming in closer’, expansion microscopy aims to ‘blow things up’ in order to see the (once) tiny details (like synapses, or nuclear pores…) on a conventional microscope. Remember those dinosaurs that would expand when you added water as a kid? I sure do…and expansion microscopy works pretty much the same.

Although this technology has been around for a few years, it is just getting started in terms of its ease of use, applicability to different samples (proteins, RNA, DNA, lipids…), and support community. All info on this fascinating technique is available at ExpansionMicroscopy.org.

First described by Edward Boyden and colleagues at the MIT Media Lab in 2015, expansion microscopy is rapidly being applied across fields, species, and disciplines to examine extremely fine structures at the nanoscale (10-20 nm).

Expansion microscopy allows for uniform expansion of a biological sample. Here, we see a brain slice (in panel B) which has been weaved into a polymer mesh with biomolecular anchors. When the polymer is expanded (‘Just add water’), it pulls the biomolecules along with it, maintaining the relative spacing between structures. In (C ) we can see that same brain slice ‘expanded’, revealing tiny pieces of biology previously too small to see (Credit: Chen et al., 2015;  Science ).

Expansion microscopy allows for uniform expansion of a biological sample. Here, we see a brain slice (in panel B) which has been weaved into a polymer mesh with biomolecular anchors. When the polymer is expanded (‘Just add water’), it pulls the biomolecules along with it, maintaining the relative spacing between structures. In (C ) we can see that same brain slice ‘expanded’, revealing tiny pieces of biology previously too small to see(Credit: Chen et al., 2015; Science).

Ed Boyden provided a ‘state of the art’ summary of expansion microscopy to date at a minisymposium today titled “new observations in neuroscience using superresolution microscopy” chaired by Michihiro Igarashi. He gave a quick overview on how they developed the idea that was to become expansion microscopy, through adapting old techniques from the early 1980’s. Next, he discussed the problems of ‘expansion’, the primary one being ’ how can we evenly expand a sample without losing valuable spacial relationships between proteins, DNA, RNA etc…? To overcome this problem they needed to develop biomolecular anchors, which link the molecular target to the polymer mesh. In this way, isometric expansion of the mesh results in the same for the anchored sample.

Using this technique, many researchers have expanded tissues to look at things like synaptic proteins and microtubules at a much finer detail than what was previously possible with conventional confocal microscopes. Others have adapted the technique to work with in situ hybridization, allowing for expansion and quantification of RNA. Dr. Boyden’s lab is also working on expanding non-soft tissues, like bone, and using expansion microscopy in the clinic to diagnose and investigate cancer in unprecedented detail (so called ‘expansion pathology’).

By combining expansion microscopy with RNA visualization (ExFISH) and sequencing (MERFISH), hundreds of transcripts can be examined simultaneously in situ!

Towards the end of the talk, Dr. Boyden highlighted some open questions in the field. These questions focused on a few primary themes:

  • Can we validate expanded samples below 10-20 nm?

  • Is expansion ‘pulling’ synapses apart, leading us to false conclusions?

  • Can we use this technique to probe protein-protein interactions?

  • Whats the smallest thing we can expand? Can we expand a virus? A DNA origami??

  • How much can we expand a sample while maintaining all relevant spatial relationships?

To take the last question, Dr. Boyden’s team reasoned, if we can expand something once, why not twice, or thrice?? They put samples through an iterative process allowing for expansion up to 20x the original size!! (shown below)

Iterative Expansion Microscopy allows for sample expansion up to 20x! Panel A shows dendritic spines without expansion, panel B shows the same at 4.5x expansion, and panel C shows dendritic spines at 20x expansion after the iterative process is complete  (Credit: Chang et al., 2017;  Nature Methods )

Iterative Expansion Microscopy allows for sample expansion up to 20x! Panel A shows dendritic spines without expansion, panel B shows the same at 4.5x expansion, and panel C shows dendritic spines at 20x expansion after the iterative process is complete (Credit: Chang et al., 2017; Nature Methods)

A cool side effect of expansion is that it involves filling the sample with water, making it essentially transparent, and useful for long-range circuit mapping at high detail or speeding up techniques like light-sheet microscopy. We are only at the surface of what is possible with this and other super-resolution techniques. I look forward to all the exciting things to come!

That’s my two cents for day 1. Keep an eye out for more coverage of some of the coolest stuff at SFN 2018!

My Coverage of #SfN2018 (Nov. 3-7th)

Hey all! This coming month we will be taking a break from regular journal club to cover the happenings at the biggest scientific meeting in the world: The Society for Neuroscience (SfN) annual meeting in San Diego, CA.

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If you’ve been to the News section of this website, you’ve noticed that I have been selected to be an ‘official blogger’ for the meeting, so many of the posts here will be simultaneously posted on NeurOnline.

My primary areas of focus are Theme I: Techniques, and Theme F: Integrative Physiology & Behavior

Me in what is possibly the wrinkliest shirt in existence at #SFN 2015.

Me in what is possibly the wrinkliest shirt in existence at #SFN 2015.

In this introductory post, I wanted to share my itinerary so you know what talks/posters I find interesting and which I plan on visiting. If you’re a meeting attendee, please feel to reach out and talk to me about your research or about other things at the meeting that you think are interesting.

Please keep an eye on these blog posts for in depth coverage of the meeting. For ‘real-time’ updates, follow me on twitter.

Check out my itinerary for the meeting HERE. This itinerary is (of course) subject to change so don’t take it as dogma.



T cells in patients with narcolepsy target self-antigens of hypocretin neurons

Welcome to our Monthly Journal Club! Each month I post a paper or two that I have read and find interesting. I use this as a forum for open discussion about the paper in question. Anyone can participate in the journal club, and provide comments/critiques on the paper. This month’s paper is “T cells in patients with narcolepsy target self-antigens of hypocretin neurons” by Frederica Sallusto and colleagues at ETH-Zurich. I will provide a brief overview of the techniques/approaches used to make it more understandable to potential non-expert readers. If I am not familiar with something, I’ll simply say so.

Hypocretin (green) neurons in the mouse lateral hypothalamus co-stained with anti-cFos (red). Latorre et al., 2018 demonstrate that autoreactive CD4+ T-cells in patients with narcolepsy specifically target epitopes present on these neurons, leading to their destruction (Credit: JCB).

Hypocretin (green) neurons in the mouse lateral hypothalamus co-stained with anti-cFos (red). Latorre et al., 2018 demonstrate that autoreactive CD4+ T-cells in patients with narcolepsy specifically target epitopes present on these neurons, leading to their destruction (Credit: JCB).

Narcolepsy is a relatively rare neurological disease caused by the selective destruction of hypocretin (hcrt; also known as orexin) neurons in the lateral hypothalamus. Destruction of these neurons causes patients to experience excessive daytime sleepiness, cataplexy (spontaneous loss of muscle tone), dream-like hallucinations, and sleep paralysis. There is a strong genetic association between development of narcolepsy and specific genotypes of the antigen presentation complex HLA-DQB1 (HLA-DQB1*06:02), additional evidence of immune dysfunction, and increased incidence of the disease following influenza vaccination. These findings suggest a role for the immune system in the etiology of narcolepsy. Indeed, the hunt for autoreactive T-cells targeting hypocretin or hypocretin receptors has been on for over a decade. The present paper fills this gap by empirically showing that patients with sporadic narcolepsy have CD4+ T-cells that target hypocretin, essentially proving an autoimmune etiology for this disease.

This has been a hard problem to crack as five epitopes from the hcrt precursors were predicted to bind HLA-DQB1*06:02, however a recent study failed to find evidence of auto-reactive CD4+ T-cell targeting of hcrt precursors (Kornum et al., 2017). Additionally, a study claiming to find CD4+ T-cell mediated autoimmunity targeting hcrt and cross-reactivity to an epitope present on the 2009 H1N1 influenza virus was retracted when the authors failed to replicate their own findings (de la Herran-Arita et al., 2014). Others have demonstrated (in mouse models) that H1N1 infection can lead to narcolepsy-like symptoms in mice (Tesoriero et al, 2015), and reprogramming of T-cells to target hypocretin neurons leads to their destruction and the development of a narcolepsy phenotype (Bernard-Valnet et al., 2016).

Specific CD4+ autoreactive T-cell clones targeting hypocretin peptides in patients with narcolepsy (Latorre et al., 2018).

Specific CD4+ autoreactive T-cell clones targeting hypocretin peptides in patients with narcolepsy (Latorre et al., 2018).

Sallusto and colleagues began by obtaining peripheral blood samples from 16 patients with narcolepsy (and who had the HLA-DQB1*06:02 allele) and complementary healthy controls (also containing the putative autoimmune allele). As autoreactive T-cells are extremely rare to begin with, they began by sorting memory T cells (CD45RA− CD4+) to high purity (>98%), through labeling them with carboxyfluorescein succinimidyl ester (CFSE). These cells were then ‘pulsed’ with antigen presenting cells (APCs; monocytes) containing different amino acid sequences of the protein precursor (pre-prohypocretin) to Hcrt-1 and Hcrt-2. This method only showed 1 patient with an ‘activated’ response of the T cells to stimulation with APCs containing peptides of pre-prohypocretin. Dissatisfied with this result, the authors tried a more sensitive approach through generated ‘T cell libraries’.

Hcrt-specific autoreactive T cells detected using the T cell library method. Each dot represents a single T cell, with proliferation measured in response to peptide stimulation reported in counts per minute (c.p.m) after incubation with [3H]-thymidine to label proliferating cells. ‘Positive’ T cell responses were considered &gt; 2,000 c.p.m as the background (unstimulated) proliferation rate was ~1,500 c.p.m. Note the strong proliferative response of T cells to hcrt peptide fragments in narcoleptics (P#) versus controls (C#). (NT1 = narcolepsy type 1, NT2 = narcolepsy type 2) (Latorre et al., 2018).

Hcrt-specific autoreactive T cells detected using the T cell library method. Each dot represents a single T cell, with proliferation measured in response to peptide stimulation reported in counts per minute (c.p.m) after incubation with [3H]-thymidine to label proliferating cells. ‘Positive’ T cell responses were considered > 2,000 c.p.m as the background (unstimulated) proliferation rate was ~1,500 c.p.m. Note the strong proliferative response of T cells to hcrt peptide fragments in narcoleptics (P#) versus controls (C#). (NT1 = narcolepsy type 1, NT2 = narcolepsy type 2) (Latorre et al., 2018).

When autoreactive T cells from narcoleptics were pulsed with B cells containing a ‘hypocretin peptide pool’, nearly all (all but one) patient samples showed a strong response to hcrt peptides, indicating that they indeed recognized and responded to this amino acid sequence. In contrast, there were only 3 (out of 12) proliferating responses in control patient samples. Regardless, the magnitude of the response was much higher in narcoleptics than in controls. This approach let them determine that the frequency of the hcrt-reactive T cells was very small (~21.4 cells per 10,000,000 CD4+ T cells). Interestingly, these autoreactive T cells were also found in patients lacking the HLA-DQB1*06:02 allele, and those without hcrt deficiency.

Epitope mapping of hcrt and TRIB2-specific autoreactive T cells. Each patient sample contains T-cell populations that react to different regions along the hypocretin and TRIB2 amino acid sequences. This is driven by antigen presentation through HLA-DR/DQ/DP, as blockade of these interactions prevent T cell expansion upon stimulation (Latorre et al., 2018).

Epitope mapping of hcrt and TRIB2-specific autoreactive T cells. Each patient sample contains T-cell populations that react to different regions along the hypocretin and TRIB2 amino acid sequences. This is driven by antigen presentation through HLA-DR/DQ/DP, as blockade of these interactions prevent T cell expansion upon stimulation (Latorre et al., 2018).

To investigate further, the researchers examined whether these patients also had autoreactive T cells targeting another protein highly expressed in hypocretin-neurons, called tribbles homologue 2 (TRIB2). Indeed, patient T cells showed a highly proliferative response to stimulation with pieces of this protein as well. The authors further confirmed ‘killer’ CD8+ T cell responses to hypocretin peptides from patient samples, but not controls. They further characterized these cells by examining what proteins they secrete and what mRNAs they transcribe in response to hcrt stimulation.

By analyzing the T-cell receptor β -chain variable region (TRBV) in each T cell clone, the authors demonstrated that these autoreactive T cells are not homogenous, and target multiple epitopes along hcrt-1 and hcrt-2. Finally, and importantly the authors failed to find evidence for ‘molecular mimicry’ between hcrt and influenza antigens, as T-cells from patients with narcolepsy failed to proliferate in response to an influenza vaccine containing A/California/7/2009 H1N1 strains or to one containing CA09 H1 haemagglutinin. Therefore, the association between vaccination and the development of narcolepsy still remains a mystery.

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Modulation of anti-tumor immunity by the brain's reward system

Welcome to our Monthly Journal Club! Each month I post a paper or two that I have read and find interesting. I use this as a forum for open discussion about the paper in question. Anyone can participate in the journal club, and provide comments/critiques on the paper. This month’s paper is “Modulation of anti-tumor immunity by the brain’s reward system” by Asya Rolls and colleagues at the Technion - Israel Institute of Technology. I will provide a brief overview of the techniques/approaches used to make it more understandable to potential non-expert readers. If I am not familiar with something, I’ll simply say so.

Figure 1: Using DREADDs to selectively manipulate VTA-Dopamine neurons in the context of cancer.

Figure 1: Using DREADDs to selectively manipulate VTA-Dopamine neurons in the context of cancer.

Discussion

In their paper, Rolls and colleagues used viral vectors encoding Cre-dependent Designer Receptors Exclusively Activated by Designer Drugs (DREADDs) to investigate how the brain alters peripheral cancer growth. By injecting these viral constructs into the ventral tegmental area of tyrosine hydroxylase-Cre mice, specific DREADD expression within only VTA-dopamine neurons (TH+) was accomplished. Gq-coupled DREADDs allow for activation of these neurons by systemic injections of the inert* molecule clozapine-N-oxide (CNO).

This is a good approach for several reasons. (1) it allows for cell-type specific control, (2) the kinetics of CNO are well known, allowing for a good amount of temporal precision, (3) it allows for non-invasive (IP injection) control of the brain, without damaging or destroying the cells. Despite this, there are caveats, such as receptor desensitization from repeated administrations of CNO, and the daily stress of IP injections. These findings will have to be replicated using disparate techniques, like optogenetics, or a more ‘natural’ way to activate these cells, via a ‘CNO drinking’ protocol, or even social/sexual interaction which are known to activate VTA-DA neurons.

After achieving cell-type specific DREADD expression, the authors gave mice subcutaneous tumors (LLC or B16 cancer cells), and then gave them daily injections of CNO. Mice that were ‘VTA-activated’ had smaller tumors than control mice that did not express the DREADD in the VTA (as seen in Figure 1). To examine how this signal from the brain might reach the tumor, the authors ablated the sympathetic nervous system using 6-hydroxydopamine (6-OHDA), a neurotoxin which destroys adrenergic nerve terminals (distinguishing feature of the sympathetic nervous system). Mice that were SNS-ablated (or received a beta-adrenergic receptor antagonist) failed to show an effect of VTA-DA activation on tumor growth. They further showed that VTA activation altered norepinephrine concentrations specifically in the bone marrow, an important immune compartment. This strongly supports the hypothesis that VTA-DA neurons alter tumor growth via SNS innervation of the bone marrow.

Narrowing in on the bone marrow, they showed that VTA activation reduces the number of myeloid derived suppressor cells (MDSCs), which can promote tumor growth via immune suppression and the promotion of angiogenesis. MDSCs contained beta-2 adrenergic receptors, which made them sensitive to VTA-DA activation (through the SNS). Finally, adoptively transferring MDSCs from VTA-activated mice to control mice recapitulated the anti-tumor effect of VTA-activation. This suggests that modulation of the immune system via a discrete population of neurons within the brain acts (at least in part) to suppress tumor growth.

Figure 4: ‘VTA-activated’ myeloid derived suppressor cells (MDSCs) are necessary and sufficient to suppress tumor growth. Adoptive transfer of ‘activated’ MDSCs suppressed tumor growth in mice that had not been ‘VTA-activated’.

Figure 4: ‘VTA-activated’ myeloid derived suppressor cells (MDSCs) are necessary and sufficient to suppress tumor growth. Adoptive transfer of ‘activated’ MDSCs suppressed tumor growth in mice that had not been ‘VTA-activated’.

These findings are in line with early research showing rats with a hyperreactive dopaminergic system have reduced tumor growth, metastasis, and angiogenesis compared to control rats (Teunis et al., 2002). These results need to be confirmed through alternative methodology and cancer models, but this paper represents an exciting new target for peripheral cancer suppression (through modulation of the brain). Indeed, the authors acknowledged this possibility in their earlier paper showing that VTA-DA activation alters both adaptive and innate immunity, suggesting that this may (at least in part) be responsible for the ‘placebo effect’ (Ben-Shaanan et al., 2016).

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*Note: recent research has demonstrated that CNO’s action is likely through it’s metabolism to the bioactive molecule clozapine (Gomez et al., 2017).

Banner Image: VTA-dopamine neurons expressing a sgRNA against BMAL1 (Credit: JCB).