It turns out that naked mole rats are resistant to cancer? Why? The answer may be called hyaluronan. Head over and check it out!

What causes these compulsive, repetitive behaviors? We’re not sure, but today’s paper suggests a role of the circuit between the striatum and the orbitofrontal cortex, areas associated with impulsivity and repetitive behaviors. And it could be that increasing activity within certain parts of this circuit might help shut down some repetitive behaviors, giving us important insight into how repetitive behaviors work.

(Figure 1A)

Burguiere et al. “Optogenetic Stimulation of Lateral Orbitofronto-Striatal Pathway Suppresses Compulsive Behaviors” Science, 2013.

I should begin by noting that Ed also covered this paper over at Not Exactly Rocket Science, along with another paper about making compulsive behaviors. It’s a really cool look at the two papers and you should definitely check it out! Me, I’m interested in the circuit involved here, and why stimulating one part may end up inhibiting behavior.

The authors of this study started with a model of obsessive behavior, the SAPAP3 knockout mouse, which I actually wrote a bit about recently. This mouse has a knockout of a special protein associated with synapses. Without it, mice display obsessive (well, repetitive, we can’t really ask the mouse if they are obsessing) grooming behavior, grooming their faces so much that they will cause lesions to form. The authors wanted to look at what caused this behavior, and what could potentially stop it.

They started by training the mice to respond to a cue.

(Figure 1B, a schematic of the training)

First they would hear a tone, then a bit later they would get a drop of water on their foreheads. This is irritating to mice, normal and knockout alike, and they will start grooming their faces. As you train the mice up, at first they will start grooming when they hear the tone in anticipation of the water droplet, with more grooming when the water droplet appears. But as training goes on, normal mice will stop grooming in response to the tone, they will learn they can wait until the water droplet actually happens. In contrast, the SAPAP3 knockout mice will continue grooming in response to the tone.

(Figure 1D)

What you can see above is the frequency of grooming in the mice, where black is the wildtype and red is the knockout mouse. You can see at the beginning of training (top graph), both types of mice groomed to get rid of the water droplet. In the middle of training (middle graph), all mice groomed in response to the tone and to the water droplet. But by the end of training (bottom graph), only the SAPAP3 knockout mice were still grooming in response to the tone, the normal mice were saving their grooming for the water droplet. This is a model of compulsive behavior, where the SAPAP3 animals are unable to stop responding to the tone, even those the tone itself isn’t the water droplet.

What was the neurophysiology underlying this behavior? The scientists looked at the neuronal firing rates in the normal and the SAPAP3 knockout mice in the both the striatum and the orbitofrontal cortex, both areas important in impulsivity and inhibiting responses, looking to see where the two sets of animals might be different.

(Figure 2A and 2E)

What you can see above is the firing rate of neurons in these two areas, the top in the orbitofrontal cortex and the bottom in the striatum. While the orbitofrontal cortex firing rats were about the same, in the striatum, the SAPAP3 knockout mice showed increased firing rates, especially apparently as the training continued on. The SAPAP3 animals were showing a lack of “tuning” when they learned the task, remaining just as responsive to the tone no matter how long they were trained.

So why this lack of tuning? In this circuit, the signals are coming from the orbitofrontal cortex to the striatum. Neurons in the orbitofrontal cortex can inhibit the medium spiny neurons in the striatum, causing the decrease in firing rate in the wildtype mice. Maybe the SAPAP3 knockouts were suffering from a lack of inhibition, where their medium spiny neurons remained active, and their behavior failed to change.

To look at this, the authors turned to optogenetics. They created mice with a gene for channelrhodopsin, targeted only to the cells in the orbitofrontal cortex of the SAPAP3 mice. The channelrhodopsin is a channel that responds to light, opening and allowing in ions, allowing the neuron in which it is placed to fire. So you can put a channelrhodopsin in a specific type of cell, activate it with a light, and make the cell fire, just when you want it to.

(Figure 3E)

What you can see above is a measure of firing rates in the striatum of the SAPAP3 knockout mouse, where the light to activate the channelrhodopsin in the orbitofrontal cortex is either on (purple) or off (black). You can see that when you use light to ACTIVATE the orbitofrontal cortex, you get a DECREASE in neuron firing in the striatum. Increasing the orbitofrontal activity inhibits the striatal activity. So if you have a striatum that is too active (as the SAPAP3 knockout mice do), you might be able to decrease that activity to normal levels.

But does it normalize the behavior?

(Figure 4B)

Yes, it DOES. When the author stimulated the orbitofrontal cortex as the SAPAP3 mice were trained, the animals learned to groom only to the water droplet, and not to the tone (blue line), whereas, when the laser was off (red line), the animals were grooming to both. Turning on a certain set of neurons helped to turn off a set of behaviors.

While these studies can’t immediately tell us what is wrong in people with OCD (after all, these mice do overgroom, but they don’t technically have OCD, there are a model), it does provide an interesting look at what might be going on in the brain during compulsive behaviors. And the more we understand, the more targets we end up with to try and fight the illness.

Reference:
Burguiere et al. “Optogenetic Stimulation of Lateral Orbitofronto-Striatal Pathway Suppresses Compulsive Behaviors” Science, 2013.
DOI:10.1126/science.1232380

Now, we have ways to take care of this. Combination antibiotics, taken for a year, can cure leprosy, though there are still leper colonies in places like India. And though leprosy is horrid, it’s not as horrid as our Medieval picture, people’s hands and noses didn’t ACTUALLY fall off, and it’s not actually very contagious.

But at the time, and in some places in the world now, lepers were a fact of life, and a terrifying one. At one point in history, its estimated that 1 out of 30 people in Europe was a leper. But then, in about the 1500s…leprosy started to disappear.

Why? There hadn’t been any advances in medicine or anything. No one knew how leprosy spread. There was still leprosy in places like the Middle East and India, but in Europe, it became extremely rare. What changed?

(Source)

Schuenemann et al. “Genome-wide comparison of medieval and modern
Mycobacterium leprae” Science, 2013.

What can cause a disease to suddenly all but disappear? Well, it could be us, or it could be “them”. The disease itself could have changed in some way, becoming less contagious, less virulent, causing less infection in a ways that may it easier for us to fight off. Or it could be us, maybe our immune systems adapted to become more efficient at throwing off leprosy. It could even be another disease, say, plague killing off many people infected with leprosy and thus slowing the spread dramatically.

So which is it? It’s harder to figure out than you might suppose. Leprosy is a very “slimmed down” bacteria (the two species are Mycobacterium leprae and Mycobacterium lepromatosis), it has gotten rid of a lot of the genes that would allow it to grow independently, meaning that it requires a host to proliferate. This also means that you can’t culture leprosy in the lab, it needs a human body to grow in. So you can’t just culture up the bacteria, sequence out the DNA, and take a look. You have to get samples from people who have had leprosy.

The authors of this study took DNA bacterial remnants in 5 medieval leprosy cases (from the UK, Sweden, and Denmark), and sequenced up the DNA, being careful to separate it from the human DNA still present in the skeletons. They then compared it to modern leprosy cases (now treatable, but they still happen), to see if and how the DNA had changed.

(Figure from the paper showing the distribution of the Medieval leprosy samples)

The result? Leprosy is one very stable disease. The genome of the bacteria hadn’t really changed at all over the past 1,000 years. It also showed that leprosy as it exists in the Americas probably traveled over from Europe, and that something similar had taken place in the Middle East, as the bacterial genomes between the American leprosy, Middle Eastern leprosy, and European leprosy were so similar. It’s a safe bet that leprosy traveled from Europe to the Americas (as there are no records or tales of leprosy in the Americas prior to European arrival), but for the Middle East, well, the leprosy could have come from the Middle East, or have been spread to it from Europe.

Leprosy is hardy, it’s barely changed at all. So what caused the decline in leprosy cases in Europe? Well, if its not leprosy, it’s got to be either us, or possibly another disease crowding it out. Perhaps we have evolved resistance to leprosy, making us throw off the bacteria easier when we become infected. Perhaps it was the result of another disease invasion, like the Plague, which could have killed off all the people with weaker immune systems (and more likely to get leprosy, and possibly a lot of the lepers as well), leaving the populace with a much lower prevalence, and stronger immune systems. It could be a combination of both.

This sequencing of leprosy also uncovered something else interesting: leprosy is tough stuff! The scientists were able to get a sequence off 14th century leprosy DNA with very little trouble. This could because the leprosy bacteria has very thick cell walls. This could be very useful, allowing scientists to look at diseases like leprosy much further back in human history, with hopes of finding enough to really make something out of it. This could help us find out where these diseases come from, and how they may have changes the shape of our species.

Reference:

Genome-wide comparison of medieval and modern Mycobacterium leprae, in Science Express, 13-Jun-2013. DOI: 10.1126/science.1238286

Verena J. Schuenemann1*, Pushpendra Singh2*, Thomas A. Mendum3*, Ben Krause-Kyora4*, Günter Jäger5*, Kirsten I. Bos1, Alexander Herbig5, Christos Economou6, Andrej Benjak2, Philippe Busso2, Almut Nebel4, Jesper L. Boldsen7, Anna Kjellström8, Huihai Wu3, Graham R. Stewart3, G. Michael Taylor3, Peter Bauer9, Oona Y.-C. Lee10, Houdini H.T. Wu10, David E. Minnikin10, Gurdyal S. Besra10, Katie Tucker11, Simon Roffey11, Samba O. Sow12, Stewart T. Cole2†, Kay Nieselt5†, and Johannes Krause1†

But what if the memories weren’t all the relaxing the caffeine was doing for me? What if the chronic caffeine consumption was keeping my stressful life at bay?

It’s time to look at adenosine 2A receptors in the hippocampus. Don’t worry, the coffee will be back.

(Source)

Batalha, et al. “Adenosine A2A receptor blockade reverts hippocampal
stress-induced deficits and restores corticosterone circadian oscillation” Molecular Psychiatry, 2012.

First let’s talk about stress. Specifically, childhood stress. In small doses, stress exposure can actually be good for you, but in large, or prolonged, doses, it’s definitely not. There are effects immediately after stress, as well as long term ones. when you suffer strong stressors in development, you can end up with changes all the way into adulthood, from cognitive deficits to predisposition to psychiatric disorders.

Why is stress in development so important? During development, our brains are developing too, particularly our hippocampus. While the hippocampus is best known for its role in memory and spatial navigation, it’s also extremely important in emotional responses. Neuronal growth in the hippocampus can come from enriched environments or chronic antidepressants, and death of those neurons can come from chronic stress. Chronic stress also disrupts the hypothalamic-pituitary-adrenal axis (the HPA axis) And that’s just in adults! During development, animals are very susceptible to stress, and the hippocampus is still developing its connections. And we’re still figuring out what changes occur during early life stress and how they relate to behaviors in adulthood.

In this case, the authors of this study were looking at adenosine 2A receptors. Adenosine is a neurotransmitter, a chemical messenger between neurons, that plays a role in promoting sleep as one of its functions. But the role of adenosine really relies on what receptors it hits, and where those receptors are. In the hippocampus, for example, adenosine 2A receptors can increase transmission of glutamate, another neurotransmitter, and can contribute to disorders and dysfunction. For example, high adenosine 2A receptors can be seen in response to acute stress, or in Alzheimer’s. If adenosine 2A receptors in the hippocampus are altered in acute stress, and the hippocampus is altered by chronic stress in early life, does this mean that adenosine 2A receptors could have anything to do with chronic stress?

To find this out, the authors used a long-established early life stress model called maternal separation. Rat pups get separated from their mother for a period of time every day during development, and this can cause symptoms of chronic stress. You might think that there are poor sad baby rats calling for their mother and left alone and cold for days, but really, it’s only for a three hour stint every day. Still, the animals grow up very differently from controls. They are more anxious and they show cognitive impairment in memory tests like the morris water maze.

But what’s the role of the adenosine 2A receptor in all of this? The authors put a bunch of baby rats through maternal separation, and looked at them in adulthood. The rats showed signs of being chronically stressed.

(Figure 1A from the paper)

What you can see in the figure above is measures of mRNA of the glucocorticoid receptor (GR). This is one of the two receptors which responds to cortisol (corticosterone in rodents), the stress hormone. The GR is the most responsive to stressors, and you can see that it’s been very affected in the stressed rats. The control rats are in black, and you can see in the stressed rats, every brain region they checked (hippocampus, cortex, or striatum), the GR mRNA (messenger RNA which is then translated to the protein receptor) is lower.

Why is this the case? Because the receptors are over stimulated.

(Figure 1E)

Above you can see the plasma corticosterone levels of the control (black bars) and stressed (white bars) rats. You can see that the stressed rats had much higher levels of corticosterone, a hormone released in response to stress. They may only have been stressed as babies, but they remain pretty stressed now.

Do adenosine 2A receptors have anything to do with this?

(Figure 4)

In the stressed rats (white bars), the adenosine 2A receptors were MUCH higher than in the control animals. This could mean that adenosine 2A receptors are important in how the animals respond to stress. To find out what role the receptors play, the authors gave an adenosine 2A receptor antagonist to the rats for a month, effectively blocking the receptors.

(Figure 3A)

What you can see above is a measure of anxiety behavior in rats, the elevated plus maze. Rats like dark, enclosed spaces, so they should like to spend time in the closed arms. But if a rat is curious enough, he’ll explore the open arms of the maze as well. You can see that the stressed rats (white bars) are much more anxious than the control rats (black bars), spending far less time in the open arms of the maze than the controls. BUT when you give them the adenosine 2A antagonist (the light grey bar), they stop being anxious! The adenosine 2A receptor is playing a role in their anxiety.

(Figure 3D)

And not just anxiety! Here we have the data from the morris water maze. The morris water maze is a test of memory in rodents. The rats are placed in a big swim tank that is filled with milky water (or sometimes, just milk), so they can’t see the bottom. They swim around until they find a hidden platform in one quadrant of the maze, where they can stand. After a bit of training, the rats get pretty good, and head straight for the platform.

…unless they don’t. You can see that the stressed rats (white bars) spent less time in the quadrant with the platform during testing, they weren’t as good at remembering where the platform was. But if you gave them an adenosine 2A antagonist (grey bars), they got better.

The adenosine 2A antagonist, in fact, normalized a lot of messed up things in the stressed rats. The stressed rats show less plasticity in their neuronal growth, the antagonist fixed it. It fixed levels of other receptors in the hippocampus. And finally it fixed the stress hormones themselves.

(Figure 5)

Above you can see two sets of corticosterone readings, from the morning and at night. Corticosterone has a circasian rhythm, it’s low in the morning and high at night. But in the stressed rats (white bars) it was higher ALL the time.

Until you gave the adenosine 2A antagonist. When the authors blocked these receptors, they restored the rhythm of the corticoterone, it was now low in the morning (the light grey bar) and high at night.

So it looks like adenosine 2A receptors in the hippocampus are very important in the long term effects of stress. Chronic stress in development increased the 2A receptors in the hippocampus and produced a lot of biological and behavioral changes, but giving a 2A antagonist long-term could set the behaviors and biology right again.

What hit me at once about this paper was the phrase “adenosine 2A antagonist”. There’s a good reason for this, the most well known adenosine 2A antagonist is caffeine! So it makes me wonder if, in some situations, caffeine might be able to help combat the effects of developmental stress. I always knew drinking coffee was relaxing…

Of course, it’s actually going to be far more complicated. Caffeine’s interactions with adenosine 2A receptors are much more varied than another antagonist might be. So caffeine itself may not be the answer. But it DOES open up interesting ideas of new ways to treat chronic stress. Caffeine may not work (though who knows, it might), but could a specialty 2A antagonist help humans with messed up HPA activity? While I showed you high corticosterone in rats, humans with anxiety disorders and depression often have high cortisol levels (the matching human hormone), not to mention the symptoms of depression and anxiety. Could the 2A antagonist (or maybe a little caffeine) end up helping these symptoms in the long term? It seems a little odd, as caffeine in a single dose usually increases anxiety rather than the opposite. But it could be that adenosine 2A antagonists could make a different, maybe long term or in different doses. In the meantime, I’m taking it as just another reason to drink coffee.

Batalha, V., Pego, J., Fontinha, B., Costenla, A., Valadas, J., Baqi, Y., Radjainia, H., Müller, C., Sebastião, A., & Lopes, L. (2012). Adenosine A2A receptor blockade reverts hippocampal stress-induced deficits and restores corticosterone circadian oscillation Molecular Psychiatry, 18 (3), 320-331 DOI: 10.1038/mp.2012.8

Sci is at Neurotic Physiology today for Friday Weird Science! Head over and check it out!

You know you want to check out my Daisy Buchanan voice.

But it’s not like that anymore. Now, ECT is usually done during a light anesthesia as well as a muscle relaxant. The huge seizures don’t happen anymore, though it’s still uncomfortable to watch (that’s a warning for the video below).

So while ECT is no longer horrifying, it’s not something to be taken lightly. Aside from the fact that you’re getting an induced seizure, there are side effects, often people have deficits in working memory for a while afterward. But for people with severe depression who are truly desperate, it’s sometimes their best hope.

But while we know that, in many patients, ECT does work, we still don’t know HOW.

Lanzenberger et al. “Global decrease of serotonin-1A receptor binding after electroconvulsive therapy in major depression measured by PET” Molecular Psychiatry, 2013.

There are lots of antidepressants on the market today. All of them currently act on neurotransmitters, chemical messengers in the brain, and most of them act, at least in part, on serotonin.

But the problem is, antidepressants don’t work for everyone. In fact, about 60% of patients being treated for depression won’t respond to the first drug they are given, and require trials of several different different drugs. And, sadly, 20% of patients don’t respond to all of the drugs tried. That’s a lot of people. And those people, who have often exhausted all of the drug options, sometimes turn to ECT.

Despite the way it looks, ECT is pretty effective in these patients, at least for a while (most of the patients will relapse). Some studies have shown that about 50% of the patients who undergo the treatment respond to ECT.

But we still don’t know WHY it’s effective. Some people hypothesize that it “resets” the neurotransmitters in the brain (though really, what does THAT mean?). Others suggest that it helps with neurogenesis and plasticity, strengthening neuronal connections. But why? How does it do this?

The authors of this study turned to the 5-HT1A receptor. And they’ve got good reason. The 5-HT1A receptor plays an important role in the serotonin system. It’s primarily an autoreceptor, a receptor that sits on the cell bodies of neurons that make serotonin. When it is hit by serotonin, it tends to reduce the action of the cells it is on. So 5-HT1A receptors on serotonin neurons generally reduce the release of serotonin when they are activated. But in issues like major depression, we want to increase the amount of serotonin, so shutting down serotonin-making neurons is no good. In this case, you’d want to reduce the amount of 5-HT1A receptors. If these receptors are reduced, they will have weaker effects on the neurons they are on, and may help increase the amount of serotonin. 5-HT1A receptors can also exist on non-serotonergic neurons (we call these heteroreceptors), and reducing these can also help with symptoms of depression.

There are a number of drugs that have been approved for treatment of depression (either on their own, or in conjunction with other therapies) that are partial agonists of the 5-HT1A receptor, and which can help reduce 5-HT1A receptor concentrations. For example, Vilazodone (Viibryd, currently approved in Europe) is both a selective serotonin reuptake inhibitor AND a partial agonist at 5-HT1A. The hypothesis is that the partial agonists can help decrease 5-HT1A receptors faster than an SSRI might do on its own. If a decrease in 5-HT1A autoreceptors is involved in an eventual antidepressant response, the sooner you can get the levels down, the better. It’s possible that decreasing these levels of 5-HT1A receptors could have antidepressant effects, or at least help other antidepressants work faster. There are other drugs that are also tending this direction, Buspar, Deprax, Abilify, and others.

So for a treatment that is effective as quickly at ECT is, it might be a good idea to look at the 5-HT1A receptor. So the authors of this study looked at patients getting ECT, and use positron emission tomography (PET) to look at the 5-HT1A receptors before and after.

PET imaging uses a radioactive tracer (in very low amounts), which binds to the receptors you are interested in. You can then see the concentrations available. The authors took 12 patients (they started with 18, but several dropped out due to things like fear of the scanner or worries about the procedure), who had never had a drug that targeted 5-HT1A before (an important control), and did PET for 5-HT1A receptors before and after ECT. The ECT was a series of sessions, between 4 and 13 total (depending on the patient).

(Figure 1)

10 of their patients responded to the ECT, but ALL of them showed decreases in 5-HT1A receptors after the treatment. In the figure above you can see three scans, representing the average from all the patients. The top and middle scans are before the ECT, the bottom is after. The color intensity (red being the highest) indicates the concentration of the 5-HT1A receptors. You can see that the bottom picture has far lower color intensity, the authors found that the ECT treatment significantly decreased 5-HT1A concentration, between about 20-30% in various areas throughout the brain.

There’s a lot of variability here, as there is with most human studies. The subjects (and there aren’t very many, ECT subjects are hard to get) got very variable amounts of ECT, between 4 and 13 sessions. Not only that, but the original scans were done while the subjects were on medication, which may have affected the baseline. Of course, you don’t want to take severely depressed patients OFF their medication, so you can’t blame them for going with it.

But they still got a significant effect, and it makes me wonder what role the 5-HT1A receptor is playing here, and HOW ECT makes the concentrations decrease. It’s also interesting that even the people who did not become less depressed after ECT still had the decreases in 5-HT1A. So is the 5-HT1A enough for an antidepressant effect? It doesn’t look like it. What else needs to take place? Is the reduced 5-HT1A receptor concentration part of a mechanism? Or is it a symptom of an alteration in something else? There are ECT studies in animal models of depression, so we may have to turn back to those to find out what role 5-HT1A plays in this treatment. But it’s an interesting finding, from a treatment that is still so mysterious.

Lanzenberger, R., Baldinger, P., Hahn, A., Ungersboeck, J., Mitterhauser, M., Winkler, D., Micskei, Z., Stein, P., Karanikas, G., Wadsak, W., Kasper, S., & Frey, R. (2012). Global decrease of serotonin-1A receptor binding after electroconvulsive therapy in major depression measured by PET Molecular Psychiatry, 18 (1), 93-100 DOI: 10.1038/mp.2012.93

Please welcome this month’s Scicurious Guest Writer, Karissa Milbury!

“This is why our drugs fail. Look at it. How do you treat that?” The professor, speaking to our graduate genetics technology class, was referring to a figure similar to this:

(Figure 1: A graph showing the array of mutations found in a single breast cancer, from a paper by Natrajan and colleagues[1].)

If that seems complicated to you, don’t worry – this is a genetic map of a single breast cancer, with every black line around the edge representing one mutation. When it comes to using data like this, scientists are overwhelmed by it, too.

The field of genetics has flourished with the publishing of the complete human genome in 2001, aided by the advent of fast, affordable sequencing technology. A completed genetic code of a healthy person allows us to compare against the genetics of cancer. With advanced analytical techniques, and decades of research into the characteristics of different forms of this disease, it seemed that it was finally time to pull out the answers from the code itself by looking for the mutations that cause or support the cancer’s growth – the differences between the cancer cell and a normal cell. But when the answers didn’t bubble up from our statistics and reams of data, it became clear that the questions left for us were far more complicated.

Life is messy: our distinctions between different species, different organisms, and different cells are largely arbitrary, because as much as we attempt to separate and define these categories, we always run into exceptions to our rules. As scientists, we seek to investigate the world through observation, classification, and prediction, but every distinction we make dissolves once we look closely enough: even the line between life and non-life is blurred. Even though this is an old problem in biology, cancer geneticists are only now running up against this as we attempt to decipher the specific changes to the genetic code responsible for driving cancer progression. To develop a specific drug for a specific cancer, we need to be able to tell the difference between a cancer cell and a healthy cell in a meaningful way. But it’s not so easy.

Grinding to a halt

Modern medicine has given us many tools for fighting disease, such as antibiotics, antivirals, and advanced diagnostic technology like magnetic resonance imaging (MRI). In the case of cancer, a set of diseases characterized by a population of cells multiplying out of control, most of our modern methods attempt to stop cellular replication. Surgery can remove many of the malignant cells, slowing down the cancer’s frenzied growth, while radiation therapy and chemotherapies such as taxol selectively kill cells that are dividing[2]. If diagnosed early, some cancers, such as certain types of melanoma or prostate cancer, are potentially curable[3,4]. Unfortunately, cancer is often diagnosed later, beyond this sensitive window. A true therapy for cancer must address these dangerous late-stage cases. So far, this has proven extremely difficult, due to the aggressive nature of advanced cancer.

Aside from the benefits we’ve seen from lifestyle shifts (such as reduced cigarette usage) and improved early detection of disease[5], there have been paltry gains in long-term survival from actual cancer therapeutics for most types of cancer[6]. While it’s true that this can be partially attributed to the increasing difficulty of acquiring drug approval[5], there’s another issue that can’t be dismissed as bureaucratic: the drugs we do approve can’t technically cure people, because we’re not able to accurately target a cancer with drugs like we do a virus or bacteria[7]. As well, if the disease returns, it is invariably resistant to the treatment that worked before, and further therapy is almost exclusively palliative[8]. Newer treatments also use sequencing and targeted molecular therapies to attack cancer cells specifically. Many researchers remain certain that each cancer will have a genetic Achilles heel, if only we can identify it. We should be able to find a mutation, or mutated pathway, or some druggable aberration specific to the tumor. I’m not convinced the answer will be that simple.

As an undergraduate professor of mine once said, “I know a thousand ways to cure cancer, it’s just that they all kill the patient, too.” I can kill cancer cells in a dish with bleach, but you obviously can’t give bleach as a treatment. For drugs that are approved, usually cancer cells aren’t the only cells suffering from the effects of treatment. Anyone that has undergone a brutal chemotherapy regimen knows this intimately, as the suffering of their normal cells result in hair loss, nausea, and a host of other side effects. As targeted as they attempt to be, often our treatments are still, first and foremost, poisons.

So what has gone wrong with our plans to target cancer abnormalities, and the ideas that have guided much of cancer research for several decades? The reason for the failure is the same as always: life is messy. A breast cancer is not simply a breast cancer, because it is a particular person’s breast cancer, and is operating from their altered code. Cancer cells can carry different markers, use different growth pathways, expand at different rates, and metastasize in different directions. Each cancer is as different as each patient, with its own code and characteristics[9], making them extremely different to treat with drugs that can only hit a certain range of targets.

But the problem is more complicated still. Following on several studies performed in the pre-genomic era[10,11], researchers began sequencing a number of biopsies from the same tumor, and demonstrated that different sections of a tumor can contain different genetic codes[12]. As the tumor cells divide, they are pressured by the limitation of space and nutrients, and are driven to compete amongst themselves to acquire adequate resources. This pushes the cells to evolve – like a population of microorganisms, the tumor diversifies as it divides. Some lineages will be sensitive to a particular drug; others won’t. Some lineages will be sensitive to radiation, while their dormant cousins remain unaffected. In every cancer, in every person, the cells will act differently. Some studies are now suggesting that chemotherapy may sometimes induce dormant tumor cells to become malignant[13]. All the while, each of these cells is experiencing a dramatically increased mutation rate – they are unstable and may die, but those that don’t die can evolve quickly.

Chaotic data

Dr. Ashutosh Jogalekar, in his blog The Curious Wavefunction, wrote in 2011 about how random events can have profound impacts on the way our world exists today[14]. Because of the importance of these random events, he explains, it would be impossible to predict chemical structures from a knowledge of physics, and similarly, it would be impossible to predict biology from a knowledge of chemistry. This is the essence of evolution: selection acts on random events, and even under similar circumstances, evolution can play out differently because it feeds on randomness. In cancer genetics, we take our knowledge of nucleotides (the building blocks of DNA) and proteins, and our knowledge of their chemical interactions, and use this to explain the actions of the cancer. We quickly run into a diagnostic wall: between these two levels of analysis, selection is acting upon random code changes, which are inherently impossible to predict.

One method for addressing chaos of this sort is to develop a statistical plan. We want to be able to take the information we know, look at the probabilities that certain changes will occur, and use statistics to determine what the cancer will likely do next. This challenge seems insurmountable when you look at the variables we must contend with: rapid evolution, unchecked growth, subtle migration, and so on. Changes that occur in the genome during cancer initiation and progression involve massive genetic rearrangements, damage, and mutation. This makes it difficult to distinguish between causes and consequences of the cancer. It would be far easier if each gene, or even each chromosome, carried out its business without interaction with the rest of the cell. But not only can changes in one area of the genome have profound direct and indirect effects on the expression of genes elsewhere, we now have evidence that cancer cells can send these activity-altering signals to other cells, both tumor-derived and normal[15,16]. This further affects our attempts to analyze the genetic structure of tumors; no longer can we treat a lineage of tumor cells independently. Instead, we must accept the possibility that signals originating in one cell can have effects in others. They are not only diverse: this diversity constantly changes, and the information in one cell is communicated to others in ways we are only just beginning to understand[17].

The image below demonstrates how the composition of a tumor can change over time; imagine the green cell on top as being the first cancerous cell, and every new color indicates a fundamental change in the code as the cell divides down through the figure. If you imagine all of those cells having direct effects on some of their neighbors, you can start to appreciate the difficulty of untangling information about the cancer’s growth from the genetic codes of the resulting tumor.

(Figure 2: Five simplified models for cancer cell lineage growth, adapted from Navin & Hicks (2010)[18].)

Moving forward

How can we develop a cancer therapy sophisticated enough to address such a profoundly complex disease? Currently, we cannot predict cancer progression this way any more accurately than a meteorologist can predict the weather – we have some ideas, we know trends, and we have mountains of data, but otherwise it’s all conjecture. Our predictions are almost always wrong if we try to predict too much.

The answer is not treating cancer like an infection that can be directly targeted or removed. Research in areas beyond traditional targeted molecular drugs, where we have begun treating cancer analysis with a multifaceted approach, is beginning to show promise. Immunology has offered tantalizing hints at what is possible when you take advantage our natural, adaptive defense mechanisms: our immune systems are very good at systematically attacking invaders, and enhancing these cells with genetic technology has shown increased survival for patients with certain types of cancer[19-21]. Scientists are also looking at ways to use viruses to chase down cancer cells specifically without harming healthy cells nearby[22]. Bioinformaticians are moving biological analysis to a new era with algorithms designed to recreate and track the flow of genetic information. This has opened our eyes to signatures of cancer in the genetic code that we never knew were there, but may be useful in diagnosing early-stage disease[23]. These complex analyses are needed if we are to dig down to the complicated roots of cancer, far beyond even the genetic code itself, to decipher the forces driving cancer progression.

I am not the first to write about the changing face of cancer genetics, and I expect to see much more discussion on this subject as we continue pushing the boundaries of cancer therapeutics[24,25]. To face the challenges of cancer we must look to other fields, especially computer science, chemistry, and physics, see what other scientists and mathematicians are doing to work with data this complex. Hopefully this will allow us to make a broad interdisciplinary move to creatively re-think our tactics. I’ve heard it said that the age of scientific genius is over, because the discoveries of today require massive computational efforts and the answers are not intuitive – but maybe we’ve lost sight of the fact that this flavor of genius is bold and imaginative, not conservative and shy. Our intuition about cancer is changing, and we must change as a field to keep up with it, however intimidating that may be. If that means learning more chemistry and math, then it’s time cancer researchers hit the books.

References:
[1]: Natrajan, R. et al. 2012. A whole-genome massively parallel sequencing analysis of BRCA1 mutant oestrogen receptor-negative and -positive breast cancers. J Pathol. 227:29-41.
[2]: Arnal, I. & Wade, R. H. 1995. How Does Taxol Stabilize Microtubules? Curr Biol. 5(8):900-908. doi:10.1016/S0960-9822(95)00180-1
[3]: Carter, H. B. et al. 1999. Influence of Age and Prostate-Specific Antigen on the Change of Curable Prostate Cancer Among Men with Nonpalpable Disease. Urology. 53(1):126-130.
[4]: Jaimes, N. & Marghoob, A. A. 2012. An update on risk factors, prognosis and management of melanoma patients. G Ital Dermatol Venereol. 147(1):1-19.
[5]: de Bono, J. S. & Ashworth, A. 2010. Translating cancer research into targeted therapeutics. Nature. 467:543-549. doi:10.1038/nature09339
[6]: Siegel, R. et al. 2012. Cancer Statistics, 2012. Ca Cancer J Clin. 62:10-29. doi:10.3322/caac.20138.
[7]: Blackwell, T. 2013. “Is the war on cancer an ‘utter failure’?: A sobering look at how billions in research money is spent”. National Post, News. http://news.nationalpost.com/2013/03/15/war-on-cancer/. Retrieved May 17, 2013.
[8]: Steeg, P. S. 2006. Tumor metastasis: mechanistic insights and clinical challenges. Nat Med. 12(8):895-904. doi:10.1038/nm1469
[9]: Nik-Zainal, S. et al. 2012. Mutational Processes Molding the Genomes of 21 Breast Cancers. Cell. 149:979-993.
[10]: Heppner, G. H. et al. 1983. Tumor heterogeneity: biological implications and therapeutic consequences. Cancer Metast Rev. 2:5-23.
[11]: Nicolson, G. L. 1984. Tumor progression, oncogenes and the evolution of metastatic phenotypic diversity. Clin Expl Metastasis. 2(2):85-105.
[12]: Marusyk, A. & Polyak, K. 2010. Tumor heterogeneity: Causes and consequences. Biochim Biophys Acta. 1805:105-117.
[13]: Kreso, A. et al. 2013. Variable Clonal Repopulation Dynamics Influence Chemotherapy Response in Colorectal Cancer. Science. 339(543):543-548.
[14]: Jogalekar, Ashutosh. “Why biology (and chemistry) is not physics”. The Curious Wavefunction. Published on Mon, Aug 29, 2011. http://wavefunction.fieldofscience.com/.
[15]: Camussi, G. et al. 2011. Exosome/microvesicle-mediated epigenetic reprogramming of cells. Am J Cancer Res. 1(1):98-110.
[16]: Deregibus, M.C. et al. 2007. Endothelial progenitor cell-derived microvesicles activate an angiogenic program in endothelial cells by a horizontal transfer of mRNA. Blood. 110:2440-2448. doi:10.1182/blood-2007-03-078709
[17]: Ghose, T. 2011. “Cancer’s Escape Routes”. The Scientist, News & Opinion. http://www.the-scientist.com/?articles.view/articleNo/31454/title/Cancer-s-Escape-Routes/. Retrieved May 17, 2013.
[18]: Navin, N. E. & Hicks, J. 2010. Tracing the tumor lineage. Mol Oncol. 4(3):267-283. doi:10.1016/j.molonc.2010.04.010.
[19]: Rosenberg, S. A. et al. 2008. Adoptive cell transfer: a clinical path to effective cancer immunotherapy. Nature Rev. 8:299-308.
[20]: Vincent, K. et al. 2011. Next-generation leukemia immunotherapy. Blood. 118:2951-2959.
[21]: Coghlan, A. 2012. “Souped-up immune cells force leukaemia into remission”. New Scientist, Health, News. http://www.newscientist.com/article/dn22613-soupedup-immune-cells-force-leukaemia-into-remission.html. Retrieved May 17, 2013.
[22]: Comoli,P. et al. 2005. Cell Therapy of Stage IV Nasopharyngeal Carcinoma With Autologous Epstein-Barr Virus–Targeted Cytotoxic T Lymphocytes. J Clin Oncol. 23(35):8942-8949.
[23]: Kim, Y.-A. et al. 2011. Identifying Causal Genes and Dysregulated Pathways in Complex Diseases. PLoS Comp Biol. 7(3): e1001095. doi:10.1371/journal.pcbi.1001095 [24]: Fessenden, M. 2013. “News from the Front in War on Cancer–Mission Not Accomplished”. Scientific American, Health, News. http://www.scientificamerican.com/article.cfm?id=news-from-cancer-war. Retrieved May 18, 2013. [25]: Chi, K. R. 2010. “The New Face of Cancer”. The Scientist Magazine. http://www.the-scientist.com/?articles.view/articleNo/29013/title/The-New-Face-of-Cancer/. Retrieved May 16, 2013.

Karissa Milbury is a graduate student in the Genome Science & Technology program at the University of British Columbia, in Vancouver, BC, working with Dr. Julian Lum. She received her BSc from Dalhousie University in Halifax, NS. Her scientific interests include genetics, bioinformatics, and immunology, and her research involves using genetic analyses to harness the power of the immune system in order to develop promising new cancer therapies. Beyond the lab, she invests herself in science communication, biking, and earl grey tea. You can visit her blog, Point Mutations, or follow her on Twitter (@Point_Mutation).

My eye was caught last week by a piece in Scientific American proper asking “is ketamine the next big depression drug?” It’s a good piece, and I appreciate the balance in the article, but I was also kind of surprised that…it took so long.

There have been previous media rumblings (and blog) about ketamine through the years, so I’m rather curious as to why the article came out now (maybe there’s another new paper out? I didn’t see any referenced and couldn’t find anything). To be honest, while yes, ketamine has a lot of interesting potential, it’s not really quite as “new” as you might think. The first major clinical reports of ketamine as an effective antidepressant actually date back to 2000. Since then, scientists have been spending a lot of time trying to figure out WHY a drug usually used to knock out horses, or abused for its perception-changing qualities, acts as an antidepressant.

And not just any antidepressant, but an almost miracle drug (maybe), helping people who respond to no other treatment, and with effects of a single dose occurring within hours (currently antidepressants take weeks), and lasting for weeks. And for all that…they don’t know how it works.

So I saw the article, and I wanted to write a bit of follow-up. Because yes, while we don’t know QUITE how ketamine works…we have some ideas. And here is one of them.

Autry et al. “NMDA receptor blockade at rest triggers rapid behavioural antidepressant responses” Nature, 2011.

(Source)

Ketamine isn’t like the current drugs used as antidepressants. Current drugs, like Prozac affect chemical neurotransmitters like serotonin. Neurotransmitters are chemical messengers, released from one neuron and signaling to another. They have have extremely different effects depending on where in the brain you are, what neurotransmitter is released, and what “flavors” of receptors it hits. But while the current drugs hit dopamine, serotonin, and norepinephrine, ketamine interacts with glutamate, the main excitatory neurotransmitter in the brain. And instead of increasing neurotransmitter concentrations (like other antidepressants do to serotonin or dopamine), ketamine acts as an ANTAGONIST at what of the glutamate receptor types, NMDA, preventing glutamate from binding to the receptor. But that’s not all, ketamine is also involved in the opioid system (which has its own receptors), and can inhibit nitric oxide from being synthesized.

This gives ketamine several properties. First, it’s a drug powerful enough to knock out a horse at the right doses. It can also do things like increase blood pressure. And it means that ketamine has the effects for which it is most famous: hallucinations and “dissociation” (which basically results in a feeling of detachment from your surroundings). And now, an antidepressant, too.

The authors of this study were interested in what aspects of ketamine made it an effect antidepressants. Was it the effects of the NMDA receptor? And WHY would those effects help fight depression?

They started by trying to isolate the NMDA receptor effects, working in mice (the C57 strain of mouse, which is usually resistant to normal antidepressants*). Sure enough, ketamine showed antidepressant properties in things like the forced swim test (where antidepressants make mice swim more instead of floating), sucrose preference (where stressed or “depressed” mice drink less sugar water, which can be reversed with antidepressants), and others. These effects could be isolated to the NMDA effects of ketamine, an NMDA blocker alone (with no other properties, unlike ketamine itself), still produced the antidepressant effects, and unlike the reference drugs (normal antidepressants), it only took one dose, and the effects lasted more than 24 hours.

Why did the effects last so long? After all, ketamine itself doesn’t last 24 hours, it only lasts 2-3. It had to be having effects while it was active that persisted after the drug was gone.

The scientists then looked to neuroplasticity, the ability of neurons to grow and change connections. Neuroplasticity has been an active focus of antidepressant research for a while now, long term antidepressant use (3-6 weeks) increases neuroplasticity and the birth of new neurons, and this is on a timeline with its antidepressant effects. Inducing neuroplasticity can help animal models fight off stress, which otherwise can produce depressive behaviors.

But normal drugs take 3-6 weeks. Ketamine takes 3 hours. Was ketamine causing rapid neuroplasticity?

To examine this the authors looked at brain-derived neurotrophic factor (BDNF). This is a protein that can stimulate neuron growth and the formation of new connections. Long term antidepressant treatment with current antidepressants can slower increase BDNF. But as the authors found, ketamine increases BDNF immediately.

(Figure 2 from the paper)

What you can see here is that ketamine and MK-801 (the second and third two bars from the left), an NMDA drug that blocks the channel of the receptor, both increased BDNF only 30 minutes after administration. This effect, and the behavioral antidepressant effects, were completely gone if you genetically knocked out BDNF, showing that the antidepressant effects of ketamine depended on BDNF increasing.

And after ketamine, the authors found that the strength of the neuronal connections in the hippocampus (where neuroplasticity can be incredibly important in mood), was much stronger than before. Again, this depended on BDNF. It appears that the administration of ketamine or an NMDA blocker like MK-801 produces neuroplasticity, resulting in stronger synapses, and that these may be the cause of the antidepressant effects.

This is a new mechanism for antidepressants, and it’s a lot faster than the current drugs on the market. So it could be a new avenue for making new drugs, that act faster than the old ones (though whether they treat all depression or are better in most patients is still up for debate).

There are important things to note here. First is that, yes, ketamine has a new, and different, mechanism of action than the antidepressants currently on the market. But it is NOT the only new mechanism that is being investigated to treat depression. Another mechanism under investigation is the idea of using opiate receptors, specifically kappa, to treat depression. And both of these, I think, will end up being good things for antidepressants. Even if ketamine itself is never approved for use, knowing these other potential mechanisms is a great avenue for developing new drugs, that may be effective in hard-to-treat patients.

Another important thing to note is that, for ketamine (and other drugs like it), it’s going to be very important to conduct very good clinical studies. When you’ve gotten ketamine, you KNOW you’ve gotten ketamine (those dissociative effects kind of give it away), and so the question becomes how do you conduct a study with a placebo that will give people that dissociative feeling, without, you know, being ketamine, in order to accurately compare the drug to the placebo. I don’t doubt the potential effects, but with the complications of knowing you have ketamine or not, it’s tough to tell just HOW big the effects are.

But it’s definitely something worth looking into , both to find newer, and hopefully better, antidepressants…and to learn about mechanism. All previous antidepressants have acted on the monoamine neurotransmitters, like serotonin, norepinephrine, and dopamine. These are, like glutamate, signaling chemicals between neurons. And in the long run, these antidepressants ALSO increase neurogenesis. Are they working through the same mechanism as ketamine, just on a slower time frame? Is there another pathway? What is it? And if the end result is an increase in neurogenesis no matter what…why does ketamine work in patients where nothing else works? What makes the difference? Looking deeper into drugs like ketamine could end up telling us a lot about how antidepressants work, and hopefully, in turn, end up telling us what may cause depression in some people.

*Full disclosure, I have an authorship on that paper, but several other labs have shown similar findings.

Autry, A., Adachi, M., Nosyreva, E., Na, E., Los, M., Cheng, P., Kavalali, E., & Monteggia, L. (2011). NMDA receptor blockade at rest triggers rapid behavioural antidepressant responses Nature, 475 (7354), 91-95 DOI: 10.1038/nature10130

Yeah, neither would this guy. But he DID give slugs to dogs! The dogs never really complained…head over to Neurotic Physiology and check it out!

Dangerous…

Stressful…

Smells like a cat in heat…

…do YOU want to inspire fear in mice? Who doesn’t! LIVE the terror. EXPERIENCE the horror. WEAR…eau d’terror, aka SBT, a potential alarm pheromone released from mice in response to bad situations! Sci is at Neurotic Physiology today, head over and check it out!

If genetics really were the be all and end all of our behavior, you might expect these identical twins to look the same, act the same, speak the same, move the same. They have the same DNA, they should essentially, be the same people.

But they’re not.

I have known several pairs of identical twins. Yeah, the resemblance is more than a bit uncanny, but without a doubt, they are not the same people. They have different interests, different behaviors. One is more outgoing, while one is more reserved. One may be into art while the other is into engineering.

You might then think that the environment had something to do with it. After all, often twins are exposed to different things. There are famous twin studies of identical twins exposed to different situations (often reared separately), which help to understand the interactions between genetics and environment.

But then, most identical twins are reared together, not separately. They go to the same school, know most of the same people, often share the same room. Their environments do not differ all that much. In theory, in terms of environmental influence, many identical twins should have the same exposure.

And they do. But it’s not just the environment that matters, or just the genetics that matter. It’s how you use them.

Freund et al. “Emergence of individuality in genetically identical mice” Science, 2013.

Same face. Different people.

(Source)

The differences between identical twins aren’t limited to humans. As a good example, many scientists who work in animal behavior work with genetically identical mice. And they are VERY identical, inbred over many generations to make one mouse pretty much the same as another. This helps to decrease variability of behaviors and biology, so that when we are looking for differences (say, a response to a drug or treatment), we can better separate them from the noise.

But while these mice are genetically identical…they aren’t behaviorally. Yes, the variability is reduced, but one mouse will usually be more anxious than another, or more active than another, or smarter than another, or fatter than another, even within the same strain. We can get identical genetics, but the behavior and physiological result is never perfect, the mice remain individuals.

And the question becomes: what produces this individuality?

To examine this, Freund et al took a large group of genetically identical female mice (C57 Black mice, the mouse of choice for many studies, in this case the kind from the Charles River supplier, which is something that really can make a difference). Half were used as controls, in control cages with cage mates, and normal food and water available. The other half received a higher enriched environment. While sometimes “enriched” can mean a larger cage with some tubes and chew toys, in this case, the enriched environment was a mouse paradise, with multiple levels, loads of tubes, different water spouts, nesting boxes, the whole  nine yards.

(Figure 1 from Freund et al, 2013)

The enriched mice were also equipped with tags so that their activities could be tracked.

Then, the controls were left, and the enriched mice were left, for 12 weeks. That’s a significant portion of a mousey lifetime.

At the end, the mice were examined for changes in hippocampal neurogenesis. Hippocampal neurogenesis, the birth of new neurons in the hippocampus, occurs throughout adult life, but can be affected by environmental stimuli or drug treatment. Long term antidepressant treatment, for example, can increase hippocampal neurogenesis, while stress can decrease it. Hippocampal neurogenesis can also be increased in response to things like new environments that animals have to learn about. And it is increased in response to things like an enrichment.

So it was no surprise that, in the mice exposed long term to the awesome mouse paradise, hippocampal neurogenesis was increased. But it wasn’t increased to the same degree in all the mice. There were individual differences. And the authors of this study took the opportunity to try and examine WHY those differences in neurogenesis might be occurring.

They used the RFID tags on the enriched mice to track where they had gone during the course of the experiment (3 months of data!). And they found that, while some mice stayed close to “home” in limited territories, others ranged all over the place.

(Figure 2B, 2C, Freund et al, 2013)

You can see on the left the more limited range of a more “cautious” mouse. This mouse had low roaming entropy, it liked its small range and didn’t need to venture out of it. On the right, however, you can see a high roaming entropy mouse. This one ranged all over. And on the bottom graph, you can see that these actions diverged over time, a subset of the enriched mice stuck to their limited range, while the other subset roamed widely.

And this roaming CORRELATED with the amount of hippocampal neurogenesis. Mice that had limited roaming had lower hippocampal neurogenesis than mice that had higher roaming. This shows a solid set of individual differences, in genetically identical mice, exposed to exactly the same environment. It wasn’t the genetics, it wasn’t the environment, instead, it was how the animals experienced the environment they were in.

What’s causing this different environmental experience? The authors put forth several hypotheses. One of them is epigenetics, the modifications made to the “outside” of the genome that determine how it is transcribed, and which can produce differences in gene expression. There are also options like intrauterine position (as mice have litters of pups, often up to 10-14 at a time, the position in the uterus, whether you’re between two boys or a boy and a girl or two girls, for example, could expose you to different hormone levels during development which could then affect behavior (probably through altering epigenetics). And of course there is the distinct possibility of random genetic mutation.

All of these are possible. But it will be interesting to see where the group goes with this work, and how they decide to look at these new, individual mice. Because the enriched environment showed that genetically identical mice, in an identical environment, can become individuals. Like twins, it’s not just the environment, or the genetics, but the combination, and how you experience it.

 
Freund, J., Brandmaier, A., Lewejohann, L., Kirste, I., Kritzler, M., Kruger, A., Sachser, N., Lindenberger, U., & Kempermann, G. (2013). Emergence of Individuality in Genetically Identical Mice Science, 340 (6133), 756-759 DOI: 10.1126/science.1235294

To many people, this might seem like a pretty random process. We used to think of aging this way, as just…well cells get old, which means we get old, too. DNA replication after a while starts making errors in repair, the errors build up, and on the whole body scale the whole thing just kind of goes downhill. It seems random.

But in fact, it’s not. There are specific proteins which can help control this process. And one of these, NF-kB, in one particular brain region, may have a very important role indeed.

Zhang et al. “Hypothalamic programming of systemic ageing involving IKK-b,NF-kBandGnRH” Nature, 2013.

(Source by Chalmers Butterfield)

NF-kB (which stands for nuclear factor kappa-light-chain-enhancer of activated B cells, which is why we use NF-kB) is a protein complex that has a lot of roles to play. It’s an important starting player in the immune system, where it helps to stimulate antibodies. It’s important in memory and stress responses. NF-kB is something called a transcription factor, which helps to control what DNA is transcribed to RNA, and therefore what proteins will eventually be produced. Transcription factors, as you can see, can have a very large number of functions.

But in the hypothalamus, NF-kB may have the added function of helping to control aging. The hypothalamus is an area of many small nuclei (further sub areas of neurons) located at the base of the brain. It’s been coming more and more into vogue lately among neuroscientists. In the past, we were interested in the hypothalamus mostly for its role in controlling hormone release from the dangling pituitary gland before it, but now we are learning that the hypothalamus can play roles in fear, mood, food intake, reproduction, and now…aging.

Zhang et al, from the Albert Einstein College of Medicine, were interested in what role NF-kB played in mouse aging. They looked at mice where NF-kB was labeled with GFP (to may it glow a nice green).

You can see above that as mice become old (the bottom panel), the amount of NF-kB increases drastically. So increases in NF-kB in the hypothalamus are definitely correlated with aging.

Now the question becomes: what does this mean? Is it just a correlation, is NF-kB a side-product of some other aspect of aging, say? Or does NF-kB have a significant role to play in aging itself?

To look at this, the authors used gene transfer (using a harmless virus that inserts a gene into the mouse genome where you inject it), that either specifically increased NF-kB in the hypothalamus, or specifically decreased NF-kB in the hypothalamus of middle-aged mice (you want to use middle-aged mice in this case, so that you avoid potential developmental differences, and can just look at aging). Then they just got simple: they looked at how long the mice ended up living.

What you can see above is a survival graph, something you see a lot of in things like aging study and toxicity. The descending lines are the % of animals still alive at the time point (on the x axis). So you can see that control mice (in red) lived around 1000 days (a little less than three years, old mice!), while if you INCREASE NF-kB (in blue) they don’t live as long. On the other hand, if you DECREASE Nf-kB (in green), the animals lived a bit longer (about 7 months longer, which is a good while in mice).

The mice didn’t just live longer. They also had better memories, stronger muscles, and stronger skin. So while they still aged, they appeared to age slower than normal mice.

And it’s not just NF-kB. If you decrease IKK-beta, which activates NF-kB, you get something similar: longer lived mice.

Here you can see that the normal mice still lived about 1000 days, while the mice without IKK-beta lived longer.

Now the question becomes how this was working. NF-kB, after all, plays a lot of different roles in stress response and inflammation. The authors decided to look at GnRH, gonadotropin releasing hormone, a hormone that many of us scientists usually associate with things like control of ovulation. But GnRH has other functions, and one of them, as the authors of this paper discovered, was in promoting the birth of new neurons in adult mice.

GnRH is released during puberty and adulthood, and is inhibited by high levels of IKK-beta and NF-kB. So it is possible that, by going downstream of IKK-beta and NF-kB and increasing GnRH, you might be able to get the aging effects seen in the other experiments.

And sure enough, injecting GnRH directly into the hypothalamus increased neurogenesis, increased muscle endurance, and increased the memory capacity of the aged mice, very similar to the effects seen with IKK-beta and NF-kB (though they did not check to see if these mice also lived longer).

So it looks like IKK-beta stimulates NF-kB, which inhibits GnRH, and that this pathway could help mediate longevity, as well as the age related changes in mice. It’s a nice way to show a potential mechanism.  This research gives you a question (what is the role of NF-kB in aging?), an answer (higher levels of NF-kB promote aging, and lower appears to slow aging), and then provides a mechanism (how does it work? through GnRH).

Now, this is not the fountain of youth. All the mice eventually died. And all of the peptides involved, NF-kB, IKK-beta, GnRH, have far more things to do in the body than simply controlling aging, so changing levels of these proteins in humans could have effects that are far outside the desired ones. But still, this has lots of implications. For one, it could be important when looking at diseases where premature aging occurs (progeria). I’m also particularly interested in the changes that the authors of this study got in hippocampal neurogenesis (the birth of new neurons they saw when they gave GnRH). As you might know, hippocampal neurogenesis can be very important, not only for memory, but for things like mood. It might be interesting to look at NF-kB and GnRH in people with severe age-related memory loss, for example, and see if new treatments might be possible. It would also be interesting to look at things like age-related depression, and how NF-kB and GnRH might play into it.

While these results are still young (heh), they reveal an interesting new target for research on how, and why we grow old. It will be interesting to see how it ages.

Note: previous excellent coverage of this study via the always perspicacious Ed Yong.

<span title=”ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.jtitle=Nature&rft_id=info%3Adoi%2F10.1038%2Fnature12143&rfr_id=info%3Asid%2Fresearchblogging.org&rft.atitle=Hypothalamic+programming+of+systemic+ageing+involving+IKK-%CE%B2%2C+NF-%CE%BAB+and+GnRH&rft.issn=0028-0836&rft.date=2013&rft.volume=497&rft.issue=7448&rft.spage=211&rft.epage=216&rft.artnum=http%3A%2F%2Fwww.nature.com%2Fdoifinder%2F10.1038%2Fnature12143&rft.au=Zhang%2C+G.&rft.au=Li%2C+J.&rft.au=Purkayastha%2C+S.&rft.au=Tang%2C+Y.&rft.au=Zhang%2C+H.&rft.au=Yin%2C+Y.&rft.au=Li%2C+B.&rft.au=Liu%2C+G.&rft.au=Cai%2C+D.&rfe_dat=bpr3.included=1;bpr3.tags=Neuroscience”>Zhang, G., Li, J., Purkayastha, S., Tang, Y., Zhang, H., Yin, Y., Li, B., Liu, G., & Cai, D. (2013). Hypothalamic programming of systemic ageing involving IKK-β, NF-κB and GnRH <span style=”font-style: italic;”>Nature, 497</span> (7448), 211-216 DOI: <a rev=”review” href=”http://dx.doi.org/10.1038/nature12143″>10.1038/nature12143</a></span>

Hello Internet! Scicurious here. For the past FIVE years now, Sci has brought you the latest and greatest (and sometimes the worst), in neuroscience, physiology, and stuff like poop. Blogging has been a life changing experience for me, I’ve learned so much, and I have also made so many wonderful friends! And of course, I’ve read a lot of VERY weird science.

As many of you probably know, I’m a big fan of being able to use a pseudonym. I think it’s incredibly important that we have the freedom to express ourselves, even when we can’t make those expressions under our real names. While yes, a pseudonym can be a great opportunity for trolling, it can also be an opportunity to develop yourself online. as the person you want to be. Developing your talents, skills, and meeting people you might not be able to otherwise.

And over the years, Scicurious has become almost as much a part of me as my real name. It’s just another name I go by. It has allowed me to build a reputation for myself, independent of where I work, where I have studied, what I look like, or other things that might give people preconceptions about what I have to say. I am proud of (almost) everything that Scicurious has done (except when I’ve gotten stuff wrong), and I hope that she and I will be together a long, long time.

But internet, you’ve been good to me, and because you have, I really think
it’s time that we take our relationship to the next level.

So. Hello, Internet. My name is Bethany Brookshire. I have degrees and things. I have a Bachelors of Science in Biology and Bachelors of Arts in Philosophy from the College of William and Mary, and a Ph.D. in Physiology and Pharmacology from Wake Forest University School of Medicine. I am finishing up a postdoc in Psychiatry at the University of Pennsylvania. I live in Philadelphia with my significant other and my cat. I love running, chocolate, coffee, the internet, and SCIENCE. I am Bethany. And I am also Scicurious. It’s nice to meet you all!

(Yeah, that’s the good picture)

But don’t worry folks! Scicurious continues on. I love to write and I’m going to keep at it! It’s practically a brand name, after all. I will continue to bring you the best and worst of neuroscience and physiology! You’ll just now have a face and a name to peg all of my mistakes on. You can continue to follow me on Twitter at @scicurious, and you can hunt me down on Google + under B Brookshire. I’ve also got a Facebook page (brand new!) and you can see that here.

“Her face was sad and lovely with bright things in it, bright eyes and a bright passionate mouth, but there was an excitement in her voice that men who had cared for her found difficult to forget: a singing compulsion, a whispered “Listen,” a promise that she had done gay, exciting things just a while since and that there were gay, exciting things hovering in the next hour.”

-The Great Gatsby, F. Scott Fitzgerald.

In the great novel The Great Gatsby, Daisy, one of the love interests of the book, has a beautiful voice. She’s described otherwise, but you don’t really remember what she looked like, you remember how she sounded. Fitzgerald describes her voice as musical, running up and down and the scales when she talks. And you know what he’s talking about. You hear that voice in your head: light, breathy, utterly charming. You don’t really know what she looks like, but from imagining her voice, you know she is beautiful.

What is it about this, or any voice, that makes it attractive? Is it the pitch? The tone? The firmness or breathiness of voice? And what is it about that voice, or any voice, that makes you know that someone is beautiful, handsome, masculine, feminine?

Xu et al. “Human Vocal Attractiveness as Signaled by Body Size Projection” PLoS ONE, 2013.

(Source)

The authors of this study wanted to see what makes a voice a VOICE. What acoustic factors make it most attractive to women and to men? To do this, they first took 10 young men, and had them rate the attractiveness of a female voice saying “good luck on your exams”. The voice actor said the phrase without any emotion using three different sound qualities: normal, breathy, and pressed (more of a hard tone). They then took the recording of this voice and modified it up and down, to create the phrase in several different pitches and formats. Specifically, they modified it upward toward what they hypothesized to mean “small body size and happiness” or downward toward what they hypothesized to mean “large body size and anger”.

They showed that while increasing the pitch (higher) did not increase the attractiveness of the voice, lowering it decreased the attractiveness. And increasing the breathiness of the sentence increased attractiveness. The authors believe that this means that lowering the voice, and presumably indicating a larger body size (larger body size in general means the normal voice will be lower), reduced how attractive the men found the voice.

But because the voice they heard was a real person, it was possible that differences in her speech could cause the differences. So from here on out they used only artificial voices. They then created the phrase “I owe you a yoyo” (romantic, no?), in male and female artificial voices, with variations in format, pitch, and breathiness, and presented them to men and women (n = 16 each) and asked them to rate attractiveness.

For the men, it was roughly the same, lower pitches became less attractive, and breathiness was more attractive. In theory, if the attractiveness of a male voice was opposite that of a female, the women surveyed should have judged low voices with no breathiness as the most attractive.

But it didn’t turn out quite that way. While the women rated the lower pitched voices as more attractive, they also preferred the male voices slightly BREATHY. I can’t really even imagine a male voice sounding breathy so I’m a little at a loss for how attractive this may have been. The authors hypothesize that this is due to the fact that a low pitched pressed voice might be seen as too aggressive, and the breathiness is needed to take some of the edge off, so to speak.

When participants were asked how HAPPY they thought the voice was, higher pitches and breathiness were always seen as happier, in males or females, while lower pitches and a flat done rated angry for both sexes.

The authors conclude that men rate as more attractive high-pitched, breathy voices, which indicate small body size, while women rate as more attractive low-pitched, breathy voices, which indicate large body size, but maybe less aggression.

While I’m very interested in the findings themselves, I don’t know that I agree with the hypothesis the authors put forward as to WHY certain voice qualities are more attractive. The authors talk about voice quality being a type of secondary sex characteristic, and hypothesize that high voices mean smaller body size while low voices mean larger. By and large, this is certainly true, but I’m not sure that the voice quality itself is an evolutionarily selected trait. First off, because of the fact that voice arises from physiology (the size of the throat involved), you can’t actually separate the voice from the body size. People with smaller bodies have higher voices. Does that mean that the voice is what we’re after? Or that the SIZE is what we’re after? I feel like the two have to vary concurrently, and while that doesn’t mean the voice isn’t important, it does make the question of size possibly as important.

Secondly, the voice you produce naturally is one thing. The voice you produce CULTURALLY is another. As the authors note, women speak in a higher pitch when talking to a male they find attractive. This isn’t just then, though. Couldn’t culture specify that women speak in higher voices than ‘normal’ ALL the time? After all, if that’s deemed to be ‘feminine’…certainly many cultures in the past and in the present have explicitly or implicitly trained young people in how they should sound, from the bellow on the battlefield to the delicate sighs of a lady. And this may mean that…voices lie. Men may strive for a lower voice that belies a smaller body size, while women seek higher, breathier realms. So while voice is a secondary sexual characteristic, I’m not sure it’s an honest signal. And if it’s not, is it really meaningful?

I also wonder about the artificial voice. The authors admit that maybe this affected their findings. I wonder if you could get rid of the potential variability of a single human voice by doing what is commonly done when looking at facial attractiveness: use a composite. I wonder if a composite of female voices (or male as the case may be) could produce an average sound that wouldn’t be purely artificial (though of course lots of artificial modification would be required).

Finally, while this study looked at whether a voice was “attractive”, and had people rank attractiveness…it never specified what that meant. I would be very interested to see this go the extra mile and have people, say, match the voice they hear with a projected body type or facial type. For example, if higher voices are supposed to be more feminine and are more attractive to men, would men associate them with other signs of attractiveness? I imagine that they would, and then it would be interesting to see how variations on pitch and breathiness affected the pairing.

But the next time you hear an attractive voice, stop to think a minute. What makes it attractive? And before you look, what do you think the person connected to the voice is like?

Xu, Y., Lee, A., Wu, W., Liu, X., & Birkholz, P. (2013). Human Vocal Attractiveness as Signaled by Body Size Projection PLoS ONE, 8 (4) DOI: 10.1371/journal.pone.0062397

Please welcome this month’s Scicurious Guest Writer, Abid Javed! Not only did he write his post, he also drew some of his own art!

Machines can be large and complex. Take a car, for instance. It has an engine that allows it perform the task of driving us humans from one place to another. A single misstep or damage to one of many car parts and the machine would stop working all together. For example, a rusted car engine would prevent the car from starting, let alone moving it forward. Now consider this machine idea in biology. Just like their man-made counterparts, biological machines can be complex and large, and can perform tasks with tremendous power. ATP Synthase, for example, is a large protein machine in cells that functions by rotating itself to power its ATP (energy) molecule production. Similarly, we have ribosomes as the protein-making (translating) machines in cells. Either functioning freely in the cytoplasm or embedded within the membranes in compartments of the cell, these machines work tirelessly to make new proteins. Ribosomes in the cytoplasm of the cell are like cars, driving along the messenger RNA strand (mRNA), trailing the growing protein chain with it until it reach its final destination stop (the stop genetic code on mRNA) to finish protein synthesis (2). Found in all living organisms that make proteins, how does this machine achieve its mighty feat of a role?

Structure of a ribosome

Let us first consider the structural composition of this biological machine. It is assembled from two main body parts – one small, 30S (S is a Svedberg unit used to indicate the weight of molecules) subunit, and one large 50S subunit. Each of these subunits is made up of ribosomal RNA and protein molecules that complement each other in achieving ribosome’s function of protein synthesis. The large, 50S ribosome subunit has a carefully folded RNA molecule (16S ribosomal RNA), interwoven between the 50S ribosomal proteins. On the other hand, the small 30S subunit has two RNA molecules (23S and 5S ribosomal RNA) with its ribosomal proteins encasing it (2), (3). The individual small and large subunit parts are synthesized inside the nucleus of a cell that, once in the cytoplasm, assemble into one 70S ribosome unit (2).

Like a car, a ribosome machine has purposeful sites designated within its structure. Inside many older cars, for example, there are the front driver and passenger seats, with small middle seat sandwiched between the two. Similarly inside the core of the ribosome, you will find three sites where the main action occurs. First, there is the driver A site (aminoacyl-tRNA site), that allows the entry of new tRNA molecules attached to an amino acid. Next to it is the middle passenger P site (peptidyl-tRNA site), that holds a growing protein chain and the intermediate tRNA molecule in place. And finally, the front passenger E site (the exit site) allows the exit of both the used tRNA molecules and the newly made protein chain out of the ribosome. Essentially, the small subunit part of the machine is for recognizing and correctly binding the incoming tRNA molecules and the large part is for facilitating the protein synthesis action (fig. 1).

(Source, by Darrell Sharp)

One of the reasons why the ribosome is so big is because it has a large substrate to accommodate; the tRNA molecule. The tRNA molecule is folded into L-shaped RNA structure from its genetic sequence. On one end of its structure, it carries the attached amino acid with it and on another it exposes a three letter genetic code (anti-codon to mRNA’s codon) that acts as a specific recognition site for binding to the mRNA molecule inside the ribosome. Essentially, a tRNA molecule functions as a cargo-like molecule during the protein translation event by bringing in a new amino acid into the ribosome for it to be incorporated into the growing protein chain (2). Once it has finished its job inside the ribosome, the tRNA molecule recycles itself by scavenging for new amino acids that it can bring back for another round.

Many machines need accessory parts to function and the ribosome is no exception. During the protein synthesis process, the ribosome machinery is aided by three accessory proteins that help the ribosome in translating proteins effectively. EF-Tu (elongation factor thermo unstable) protein allows the ribosome to bind a specific tRNA molecule in it’s A binding site to begin translation (5). EF-G (elongation factor G) proteins help the ribosome shuffle both the tRNA and mRNA along during the protein synthesis step (6). The release factor protein (RF1) eventually helps the ribosome to facilitate the exit of both tRNA and newly synthesised protein chains from the exit site.

The ribosome at work

As soon as the ribosome has assembled all of its individual parts to make a single bodied unit, it comes alive as a machine to makes proteins. It does this by combining the work done by the individual small and large subunits in gathering the tRNA-bound amino acid components from the cytoplasm and putting them together to make extensive protein molecules. There are three stages in this entire process, occurring at the three respective sites inside the machine. After the free ribosome has assembled and is bound to the mRNA strand in the cytoplasm, the first step is to allow specific entry of an incoming tRNA molecule at the A site of the small ribosomal subunit. On this subunit’s ribosomal RNA strand, there are two genetic gatekeeper groups that act as guards, only allowing specific tRNA molecules to bind. This is through their specific chemical interactions with the incoming tRNA molecule that either allows the entry in or simply rejects it. The ribosome certainly cannot allow for non-specific binding as that will disrupt the machines mechanics (4). An engine like a ribosome (comprising of both the substrate binding small subunit and protein-making large subunit) needs fuel to function and this comes in the form of quick-energy releasing molecules called GTP (Guanosine triphosphate). These are carried by the accessory proteins (EF-Tu and G proteins) that eventually fuel the tRNA binding and movement along the A to E site inside the ribosome during the protein synthesis event (5), (6).

Once the correct tRNA (carrying the amino acid) is bound to the mRNA strand inside the ribosome at the A site, the machine gets ready to start putting the protein molecules together by bonding the incoming amino acids inside it. The ribosome machine has a special, flexible ability to move and wrap itself around the bound tRNA molecule once it binds, so that it can make the bonding of the accompanied-amino acids easier by bringing them closer to each other (7), (9). With fixating the tRNA molecule correctly at the A site in the small subunit, the protein synthesis action at the large subunit begins.

Inside the ribosome machine’s large subunit part, the main chemical event of protein synthesis takes place at the A and P site junction known as the peptidyltransferase center, where the ribosome machine chemically bonds the individual amino acids sequentially at the designated ribosome sites so that the process is progressive and doesn’t come to a halt. Along with the accessory proteins (EF-Tu and EF-G and RF1 stated earlier), the tRNA and the amino acids hop from A to P to E site within the two ribosomal parts, allowing the protein chain molecule to grow progressively (8). Once the last tRNA molecule binds to its corresponding stop mRNA code in 30S machine part, the peptidyltransferase centre in the large subunit part realizes that it is time to stop. The protein is consequently terminated and assisted by the RF1 accessory protein and the freshly made chain slithers itself through the ribosome E site exit tunnel and out of the ribosome (3), (9), (10). Within this whole process, the job of the ribosome is not only to act as the ribozyme (to chemically make proteins) but to also act as a filtering machine so that it allows the correct incorporation of the amino acids within the growing protein chain and reduces protein translation error rate (10). Not having this filtering capability would otherwise cause the ribosome to incorporate the wrong amino acids within a protein, which would result in a completely dysfunctional protein being made. Hence, it is important that the ribosome is able to sustain its filtering characteristic.

Targeting the function of the ribosome.

However, with a big machine like ribosome working rigorously as it does, there is inevitably room for errors to occur. As mentioned earlier, a faulty machine part or convolution with its function would amplify the error producing rate of the ribosome. Scientists have considered this as a major step towards treating various pathological diseases by making the ribosome machine dysfuctional in making correct protein molecules. For example, current work is exploiting the machinery of the ribosome to create new drugs by inhibiting protein synthesis in cells, something that could be a powerful tool in stopping things like bacterial infections. Natural antibiotics and chemical inhibitors are a few examples that have shown promising results in inhibiting ribosome function in cells. The inhibitory action of these molecules directly results in the prevention of the large 50S machine part from functioning; weakening the peptidyltransferase centre’s nuts and bolts and therefore preventing protein synthesis in the ribosome. In essence, by inhibiting the protein synthesis process, it directly prevents the growth and survival of the pathological organisms that are being targeted and hence proves a useful strategy at the interface of DNA and protein molecules in the treatment of pathological diseases (10), (11).

All this hard work in understanding the molecular inner workings of this complex machine begs the question of why have we been so keen on it? Protein molecules are at the heart of living biology and understanding their fundamental synthesis process (by the ribosome machine) allows us to get to the bottom of questions relating to how proteins actually fold once they are synthesized, and what leads to them not adapting a correct shape (which can result in protein misfolding, a symptom that characterizes some neurological diseases such as Alzheimers). The sheer hard work on elucidating the ribosome machine’s behaviour was rightfully recognized and awarded the Nobel Prize for Chemistry in 2009 (12). There is still much more work to be done, and important things to be learned from the ribosome presently. But for all the work that remains, it is still easy to see what a remarkable biological machine a ribosome really is.

References.
1. Albertsson A. P., Hanzon V., Toschi G. 1959. Isolation of ribonucleoprotein particles from rat brain microsomes by a liquid two-phase system. Journal of Ultrastructure Research. Vol.2: 366-372.
2. Steitz A. Thomas. 2008. A structural understanding of the dynamic ribosome machine. Nature Reviews Molecular Cell Biology. Vol.9: 242-253.
3. Rodnina V. Marina, BeringerMalte and Wintermeyer Wolfgang. 2007. How ribosomes make peptide bonds. Trends in Biochemical Sciences. Vol.32: 20-26.
4. Wimberly T. Brian, Brodersen E. Ditlev, Clemons M. William, Warren-Morgan J. Robert, Carter P. Andrew, Vonrhein Clemens, Hartsch Thomas &Ramakrishnan V. 2000. Structure of the 30S ribosomal subunit. Nature. Vol. 407: 327-339.
5. Stark Holger, Rodnina V. Marina, Appel-RinkeJutta, Brimacome Richard, Wintermeyer Wolfgang & Heel van Marin. 1997. Visualization of elongation factor Tu on the Escherichia coli ribosome. Nature. Vol. 389: 403-406.
6. Rodnina V. Marina, Savelsbergh, Katunin I. Vladimir &Wintermeyer Wolfgang. 1997. Hydrolysis of GTP by elongation factor G drives tRNA movement on the ribosome. Nature. Vol. 385: 37-41.
7. PapeTillmann, Wintermeyer Wolfgang and Rodnina Marina. 1999. Induced fit in initial selection and proofreading of aminoacyl-tRNA on the ribosome. The EMBO Journal. Vol. 18: 3800-3807.
8. Julian Patricia, Konevega L. Andrey, Scheres W. H. Sjors, Lazaro Melisa, Gil David, Wintermeyer Wolfgang, Rodnina V. Marina and Valle Mikel. 2008. Structure of ratcheted ribosomes with tRNAs in hybrid states. Proceedings of the National Academy of Sciences of the United States of America. Vol. 105: 16924-16927.
9. Valle Mikel, ZavialovAndrey, SenguptaJayati, RawatUrmila, Ehrenberg Mans, Frank Joachim. 2003. Locking and Unlocking of Ribosomal Motions. Cell. Vol. 114: 123-134.
10. Liljas A. 1999. Function is Structure. Science. Vol. 285 (5436): 2077-8.
11. Ochi Kozo, Okamoto Susumu, TozawaYuzuru, Inaoka Takashi, Hosaka Takeshi, Xu Jun, Kurosawa Kazuhiko. 2004. Ribosome Engineering and Secondary Metabolite Production. Advances in Applied Microbiology. Vol.56: Pg 155-184.
12. Venkatraman Ramakrishnan, Thomas A. Steitz, Ada E. Yonath. Nobel Prize for Chemistry, 2009 :- www.nobelprize.org/nobel_prizes/chemistry/laureates/2009/press.html

Abid Javed is an undergraduate Biochemistry student at the University of Manchester. After completing his degree this year , Abid will start his PhD studies with Dr John Christodoulou at UCL to tease apart the inner workings of the ribosome. Someday, he wants to hold an art exhibition to show how structurally beautiful proteins really are.
Website:- www.abidsbrushstrokes.com
Twitter:- twitter.com/sunshine_6