The ageless face of an Aes Sedai: Science Edition

How would the brain process a truly 'ageless' face?

Moraine, an ageless Aes Sedai (source)

I am sure this question has plagued many Wheel of Time fans, but only now has an experiment been designed to test it. Just 4 days ago, Homola et al. (2012) published a paper in PLoS ONE in which they have people guess ages of people in pictures and scan their brains. 

Homola et al. (2012) Figure 1A. (Which one looks most Aes Sedai to you?)

The first interesting thing that they found was that the older the person in the picture (either a real picture of a real person, or a hybrid 'morphed' picture like the ones above), the harder it was to tell how old they were. This isn't really that surprising, as the range of ages that can 'look' a certain age gets wider over the years.

Homola et al., (2012) Figure 2B.

Here they plot the standard deviation in years for people's guesses as to the age.

The authors showed videos of the faces morphing from one age to another to volunteers while they were in the fMRI machine.

As a side note: they found that there was no difference between male and female volunteers. If they had I think a big deal would have been made about it. but since they didn't it's just a tiny sentence in a long paper.

Ok, back to the processing of age. They threw out the results from people who were really really bad at rating age because they 'weren't motivated' and weren't really trying apparently. (This could be a bit of cherry picking or data massaging) Then they compared the areas of the brain that were active for people who were really really good at guessing age, and people who were only average.

Homola et al., (2012) Figure 4D

The basic finding was that the posterior angular gyrus area (pANG) in the left hemisphere was 5 times more active for the expert age guessers than it was for average. Conclusion: pANG is important for age-processing. This on its own is good to know, but not amazingly interesting. What I think is cool is the idea that the authors present as a  follow up experiment in their discussion:

"Even though our study highlights pANG as one key component for age processing, its precise role in this context is still speculative and needs further investigation. Our model, illustrated in Figure 7, gives rise to interesting hypotheses: One testable prediction would be that disruption of left pANG activity using transcranial magnetic stimulation (TMS), for example, should impair numerical age but not gender judgements, and that brain lesion-symptom mapping can eventually dissociate the two. " Homola et al., (2012)

So now we know, the Aes Sedai must have some magic that transcranially impairs pANG in everyone around them so they can't guess their age. That is how to stay truly ageless.

© TheCellularScale


Homola GA, Jbabdi S, Beckmann CF, & Bartsch AJ (2012). A Brain Network Processing the Age of Faces PLoS One DOI: 10.1371

Bomb Dogs or Bomb Cells?

It's about to get really neuro-heavy here at The Cellular Scale because of the impending Society for Neuroscience annual conference. So before that onslaught of neuroinformation, lets step back and talk about two of my other favorite things: smell and beer.

Dogs are good at Smelling Things... (I took this picture)

But could beer yeast eventually be better at it? 

In 2007, researchers genetically engineered Saccharomyces cerevisiae (beer yeast) to express a rat olfactory receptor. But not just any old fully functioning olfactory receptor. Radhika et al. (2007) made a lovely franken-protein by using bits of dna from all over the place. They basically end up with a dna sequence coding for a receptor that expresses GFP (green fluorescence protein) when it is 'triggered.' The key here is that this 'trigger' could be easily changed. 

They first demonstrate that they can replace the trigger part of this receptor, making it sensitive to the scent of vanillin or cintronellal. While yeast that can turn green in response to the relaxing scent of vanilla might make for great advances in home decorating, the authors actually wanted to make a yeast strain that would fluoresce in response to the scent of bombs.

Specifically, they wanted the yeast to respond to DNT, a mimic of TNT. To do this, they had to conduct a large assay on a 'library of cDNA inserts.' That basically means they had to switch in different 'triggers' for the receptor based on known strings of olfactory receptor dna and test each one to see if it responded to DNT. Lucky for them, they happened to find one. 

Radhika et al., 2007 Figure 4A: yeast glows in response to DNT

And voila! a strain of bomb-sniffing yeast!

While I don't think we'll be replacing bomb-sniffing dogs with petri dishes of glowing yeast any time soon, this is a significant step toward cellular level bomb-detection.  More importantly, this study developed a new way to screen dna. They have created a versatile receptor 'cassette' into which they can place a string of dna and test which ligands or odors 'trigger' that section of the protein.

© TheCellularScale

Radhika V, Proikas-Cezanne T, Jayaraman M, Onesime D, Ha JH, & Dhanasekaran DN (2007). Chemical sensing of DNT by engineered olfactory yeast strain. Nature chemical biology, 3 (6), 325-30 PMID: 17486045

Beer Yeast and Zoloft

Beer Sampler (I took this picture)

Yeast is an amazing organism that converts sugar into ethanol, or in other words barley into beer. It is used to ferment beer and is then usually filtered out.  (The leftmost beer sample in the picture above is an unfiltered beer and is cloudy because of the yeast still floating in it). 

Aside from providing proof that god loves us and wants us to be happy, yeast also provides a fascinating model in which scientists can study specific cellular processes. Because it is a simple eukaryote and can be easily cultured and easily mutated, yeast has long been used to test the effects of genetic manipulation on eukaryotic intracellular workings.

(source)

However, it's not often thought that yeast would be a good model for studying processes specific to the brain. But a recent paper published in PLoS One uses yeast to test the cellular actions of anti-depressants.  Specifically, they apply Zoloft to yeast cells.

Zoloft (like Prozac and other highly prescribed anti-depressants) works as a selective serotonin reuptake inhibitor (SSRI), inhibiting the uptake of serotonin after it has been released into the synapse, functionally allowing more serotonin to remain in the synaptic cleft. 

But Yeast don't have serotonin receptors and they don't have synapses. So what on earth could Zoloft do in these cells?

("Zoloft Does Everything" from Hipstercards.com)

Well here's a possibility: maybe Zoloft doesn't just alleviate depression by inhibiting the re-uptake of serotonin.  Maybe it does something else too. And if you are looking for non-serotonergic actions of Zoloft, yeast becomes the perfect organism to experiment on.

One reason to think there might be other (non-serotonergic) effects is that Zoloft takes a while to start 'working'.  That is, when Zoloft is taken, the enhancement of serotonin is almost immediate, but the actual effect on depressive symptoms can take weeks to appear.

So the question is: Are there effects of Zoloft which take a while to appear and do not specifically involve serotonin? 


Well, yes, there are.  Zoloft actually accumulates in the membrane of yeast cells, often killing them.  Which... doesn't sound promising. But Chen et al., 2012 shows that this membrane accumulation doesn't always kill the yeast cell and that in some select situations the membrane accumulation could have a protective effect by triggering cell-repair activities.  At "sub-lethal" doses, Zoloft can partially rescue stunted growth in certain yeast mutants.


This work seems to support the neurotrophic hypothesis of depression which says that neurons die depression, and that anti-depressants actually "reduce neuronal atrophy"
Does this paper show that Zoloft prevents neuronal death? No, not at all. It is investigating yeast, and shows that Zoloft could in some situations trigger cell repair.  But it doesn't say that Zoloft acts this way in neurons.


Obviously a lot more work needs to be done to really understand the actions of Zoloft and other anti-depressants. This study in yeast is a first step, but the findings need to be extended actual neurons before the trophic mechanism of Zoloft becomes anywhere close to as convincing as the SSRI mechanism. 

© TheCellularScale

Chen J, Korostyshevsky D, Lee S, & Perlstein EO (2012). Accumulation of an antidepressant in vesiculogenic membranes of yeast cells triggers autophagy. PloS one, 7 (4) PMID: 22529904

Hot Flashes and Cancer Cells

As I have recently explained, The Cellular Scale wants to weigh the worth of certain claims made by the media, individuals, and scientists. 
To start with, we will investigate the claim that hot flashes can cure cancer. I just heard someone say this the other day, so there is no media or peer-reviewed source to condemn.  However hearing it from a non-scientist in a completely non-scientific context leads me to believe it might be something that is popularly accepted, and therefore merits a good close look.

Here is the exact quote I heard:

"The temperature reached during hot flashes in menopause is exactly the temperature at which cancer cells cannot survive."

There are some obvious problems with this specific claim. If this is true as stated, then no one would have cancer cells remaining in their body after undergoing a hot flash and heating up the body would be the undisputed cure for cancer. 

So lets take a look at a related, but somewhat less drastic claim:

"A new study shows that having symptoms such as hot flashes during menopause appears to be tied to a lower risk of the most common kinds of breast cancer."

This claim is based on an actual paper. The paper suggests that this connection is due to differing estrogen level in women with and without symptoms

"Prior studies indicate that women with menopausal symptoms have lower estrogen levels because they go through menopause as compared with women who do not experience them. Given the central role of hormones in the etiology of breast cancer, a link between menopausal symptoms and breast cancer is plausible. However, no prior studies have evaluated the association between menopausal symptoms and breast cancer risk....This is the first study to report that women who ever experienced menopausal symptoms have a substantially reduced risk of breast cancer, and that severity of hot flushes is also inversely associated with risk." (from the abstract Huang et al., 2011)

The more severe the hot flashes the lower the risk of breast cancer is an exciting and useful finding, but the paper makes no claim that this is because of the heat.  It certainly doesn't make any claims about other forms of cancer, or the viability of already present cancer cells during a hot flash. 
Finally, this is a classic example of correlation vs. causality. The finding that the women with severe hot flashes have a lower risk of breast cancer does not mean that the hot flashes prevent breast cancer.  (Hot flashes might cause the reduction in risk, but the research hasn't shown that yet) In fact, it seems equally, if not more likely that one mechanism causes both severe hot flashes and a reduced risk of breast cancer.

© TheCellularScale
Huang Y, Malone KE, Cushing-Haugen KL, Daling JR, & Li CI (2011). Relationship between menopausal symptoms and risk of postmenopausal breast cancer. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 20 (2), 379-88 PMID: 21212063

Brain Glue: Synapses on and around Glia

Astrocytes, a form of glial cell (source)

Glial cells are non-neurons that populate the nervous system.  The name 'glia' comes from the Greek word for glue, and these cells were originally thought to be 'filler' cells or brain glue (not this kind).

In a sense these cells are 'filler'.  When the brain is damaged, it is glia not new neurons which grow into the void.  (This can sometimes turn cancerous and lead to glioma)

A recent review paper poetically summarizes the traditional role of glia:

"Astroglial cells were long considered to serve merely as the structural and metabolic supporting cast and scenery against which the shining neurones perform their illustrious duties." (Lalo et al., 2011)

This lovely summary is an obvious set up for a paper showing that "actually glia are quite important."

And indeed they are. 

Even though they don't fire action potentials, glial cells have electrical activity and are involved in information processing. 
Glial cells have receptors for neurotransmitters (such as glutamate and GABA).  These are the very same types of receptor that neurons use receive signals from other neurons at the synapse. 


Lalo et al. point out four different ways that these receptors might work on glial cells:

1. Glia might receive direct signals from neurons. (synapse-like connections)
2. Glia might respond to neurotransmitter released for non-synaptic (ectopic) sites.
3. Glia might respond to transmitter released from other glia
4. The receptors on glia might be activated by 'ambient' neurotransmitter.

While it is not clear which of these receptor-activating mechanisms predominates on glia, there is evidence from different brain areas for each type of information transfer. 

No matter how these receptors are stimulated, they can depolarize the glial cells and even induce calcium transients.  Lalo et al. explain that these actions might cause the glial cells to release lactate which is taken up by neurons as an energy source. 

In short, the role of these glial cells might be mainly metabolism control near synapses, and the ionotropic neurotransmitter receptors might be the mechanism that signals when, where, and how much metabolism control is needed. 

© TheCellularScale


Lalo U, Pankratov Y, Parpura V, & Verkhratsky A (2011). Ionotropic receptors in neuronal-astroglial signalling: what is the role of "excitable" molecules in non-excitable cells. Biochimica et biophysica acta, 1813 (5), 992-1002 PMID: 20869992

Plant Neurons? Sensation and action in the Venus Flytrap

Plants are more electric than you might think. 

(Venus Fly Trap by Nick Ford at nickpix2012)

While they don't have neurons in the proper sense, they have sensory receptors, ion channels,  action potentials, and can process information. One of the most remarkable feats of plant information processing occurs in the venus flytrap. 

The venus fly trap is remarkable among plants because it has very fast and very specific information processing capabilities.  It can sense changes in its environment and act upon its sensations quickly. 

Here's how it works, illustrated by 3 fascinating studies. 

1. Sensation: The venus flytrap has 3 little hair-like protrusions (see above photo) on each side of the 'mouth'.  These 'trigger hairs' contain mechanosensory cells which activate when the hair is moved.

Benolken and Jacobson, 1970 Figure 1. A trigger hair set up for electrophysiological recordings.

In order to see what these cells are actually like, Benolken and Jacobson used electrophysiology methods (similar to the ones used in animal neurons) to record the cells' electrical signals during mechanical stimulation. 

up close image of a trigger hair.
The sensory cells are at the indentation
point near the bottom. (source)

They found that the primary sensory cells were right near the base of the hair where there is a distinct indentation.  In other words right where the trigger hair would bend if a delicious fly bumped into it.

They also measured the force needed to stimulate these cells.  Interestingly in more mature plants, the trigger hairs are stiffer and require more force to activate the mechanosensory cells. (Why this would be, I am not sure. Maybe larger prey hits the trigger hairs harder, and the larger older traps don't want to bother with puny meals.)

When enough of these sensory cells are activated, they trigger the second step in the process, the action potential.

2. Information travels from the base of the trap through the two sides of the 'mouth' and makes it close.

Here is where it gets tricky.  I couldn't find any detailed information on how the sensory cells are connected to the base of the trap (also called the midrib).  These sensory cells must 'project' to the midrib, but how these projections are shaped, where they converge, and how they relay their signal to other cells is a mystery to me.  (If you know or have a reference please post it, I am curious about this missing step.)

Electrodes implanted in the lobes and
midrib of the venus flytrap.
Volkov et al., 2007 Figure 6

However, once the information gets to the base of the trap, it takes on the form of an action potential which travels through each side of the leaf.  To investigate this action potential, Volkov et al. (2007) placed silver-chloride wires directly into the midrib and lobes of a trap and record the electrical transients.  They find that when they stimulate the hairs, an action potential can be recorded through these wires. They also find that by putting current directly through the wires, they can cause the trap to close even without stimulating the hairs.

Action potential from a Venus Flytrap (Volkov et al., 2007)

They are able to inhibit this action potential by applying ion channel blockers to the soil of the plant 2+ days before the experiment.  The application of TEACl (a potassium channel blocker) prevented the trap from closing when the hairs were triggered and when the electrodes were directly stimulated.  The application of calcium channel blockers caused the trap closure to be much slower.  So the information traveling step requires potassium and is pretty reliant on calcium too.  So once the action potential is triggered, how does that actually make the trap close?

3. Trap Closure 
There are several theories about how the trap actually closes. Some involve actual cell growth, but Foterre et al., (2005) show that the trap can close based mainly on mechanical principles. 

Foterre et al., 2005 Figure 1b. UV sensitive dots all over flytrap surface

To visualize the specifics of trap closure, Foterre et al. paint uv-sensitive dots in a grid on each trap lobe. This technique is surprisingly like what this person did using white-out to test their own garden of flytraps.  Basically Foterre et al. show that there is a biophysical trigger that changes the curvature of one part of the plant, but that as soon as that curvature is changed the rest of the process is a passive response based on mechanical principles.  That is, the 'snapping' closed has a passive component that can be modeled computationally as a thin elastic sheet.  They summarize it nicely in their last paragraph:

"Upon stimulation, the plant 'actively' changes one of its principal natural curvatures, _xn, the microscopic mechanism for which remains poorly understood. Once this change occurs, the geometry of the doubly-curved leaf provides the mechanism by which elastic energy is both stored and released, and the hydrated nature of the leaf induces the rapid damping that is equally crucial for efficient prey capture. A single geometrical parameter () determines the nature of closure: if _c 0.8, the leaf closes smoothly, and if > _c, the leaf snaps rapidly. This ingenious solution to the problem of scaling up movements and speed from the cellular to the organ level in plants, nature's consummate hydraulic engineers, shows how controlling elastic instabilities in geometrically slender objects provides an alternative to the more common muscle-powered movements in animals."

So there you have it, everything you ever wanted to know about the Venus Flytrap and then some. Even though the flytrap electrical properties have been studied for hundreds of years, there is so much that is not known. I dare say we know more about the neurons of the mouse brain than we know about the sensory cells in the Venus Flytrap. 


And of course, no scientific phenomenon is complete until someone makes a robot out of it. 

© TheCellularScale

Benolken RM, & Jacobson SL (1970). Response properties of a sensory hair excised from Venus's flytrap. The Journal of general physiology, 56 (1), 64-82 PMID: 5514161

Volkov AG, Adesina T, & Jovanov E (2007). Closing of venus flytrap by electrical stimulation of motor cells. Plant signaling & behavior, 2 (3), 139-45 PMID: 19516982  

Forterre Y, Skotheim JM, Dumais J, & Mahadevan L (2005). How the Venus flytrap snaps. Nature, 433 (7024), 421-5 PMID: 15674293