New lab webpage

The blog remains a the good old wordpress location, but I have moved the info about my lab and research to a new webpage. The main reason behind is that I wanted to have better control over publishing code from research and teaching projects.

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Phylogenetic trees in R

I wrote this little tutorial as an introductory chapter for the NESCent Academy on Macroevolution back in July 2014. It’s meant to provide a brief overview of the basic structure of tree objects in R and illustrate some of the tree manipulation and visualization options.

Given that I’ll be teaching a module on comparative methods in our ‘Research Methods in Organismal Biology’ course soon, I figured I may as well make all scripts and exercises accessible on this blog. Once you have mastered this, explore many more options in Emmanuel Paradis’ book (Analysis of Phylogenetics and Evolution with R) and Liam Revell’s blog.

Let’s first load some libraries that we need for this exercise.

    library(ape)
    library(geiger)

Trees as “phylo” objects

Let’s begin by simulating a tree. There are many options for doing this. We’ll use the rtree() function of the ‘ape’ package. The tree is stored as an object called “phy”.

    phy <- rtree(n=10) 
    # n specifies the number of tips we want.

How does the tree look like? We can plot it with the following line.

    plot(phy)

 

index01So, now we wonder how the phylogenetic information is encoded. And no, it’s not a black box. By typing “phy” in the command line we can retrieve some basic information about the object.

    phy 
## 
## Phylogenetic tree with 10 tips and 9 internal nodes.
## 
## Tip labels:
## t3, t8, t5, t4, t7, t2, ...
## 
## Rooted; includes branch lengths.
# A tree with n tips and (n-1) nodes.
# There are tip labels, the tree is rooted, and we have branch lengths.

But how is this information organized within the phylo object? We can find out with the str() function, which displays the structure of an R object.

    str(phy)
## List of 4
##  $ edge       : int [1:18, 1:2] 11 12 13 13 12 14 15 15 14 16 ...
##  $ tip.label  : chr [1:10] "t3" "t8" "t5" "t4" ...
##  $ edge.length: num [1:18] 0.944 0.996 0.842 0.542 0.627 ...
##  $ Nnode      : int 9
##  - attr(*, "class")= chr "phylo"
##  - attr(*, "order")= chr "cladewise"

The output tells us that a phylo object is a list of four components (+ two attributes): edge, tip.label, edge.length, and Nnode. We can display these items separately.

    phy$edge
##       [,1] [,2]
##  [1,]   11   12
##  [2,]   12   13
##  [3,]   13    1
##  [4,]   13    2
##  [5,]   12   14
##  [6,]   14   15
##  [7,]   15    3
##  [8,]   15    4
##  [9,]   14   16
## [10,]   16    5
## [11,]   16   17
## [12,]   17   18
## [13,]   18    6
## [14,]   18    7
## [15,]   17    8
## [16,]   11   19
## [17,]   19    9
## [18,]   19   10
    phy$tip.label
##  [1] "t3"  "t8"  "t5"  "t4"  "t7"  "t2"  "t10" "t1"  "t9"  "t6"
    phy$edge.length
##  [1] 0.9437 0.9960 0.8423 0.5422 0.6265 0.6445 0.1634 0.7275 0.8058 0.8304
## [11] 0.9346 0.5120 0.9839 0.7717 0.2805 0.1774 0.8578 0.3754
    phy$Nnode
## [1] 9

And of course, we can also store these components as new objects with names of your choice.

    branches <- phy$edge
    species <- phy$tip.label
    brlength <- phy$edge.length
    nodes <- phy$Nnode

But what does this all mean? Let’s explore this with a hand-written tree. Assume you have a tree with 6 species (A through F). The phylogenetic relationships of species A:F can be described in bracket form (“parenthetic format”, or Newick).

    mini.phy <- read.tree(text = "((((A,B), C), (D,E)),F);")
    plot(mini.phy)

index02The structure of the object contains only three components this time because we didn’t provide branch length:

    str(mini.phy)
## List of 3
##  $ edge     : int [1:10, 1:2] 7 8 9 10 10 9 8 11 11 7 ...
##  $ tip.label: chr [1:6] "A" "B" "C" "D" ...
##  $ Nnode    : int 5
##  - attr(*, "class")= chr "phylo"
##  - attr(*, "order")= chr "cladewise"

But how are the edges defined? When we type…

    mini.phy$edge
##       [,1] [,2]
##  [1,]    7    8
##  [2,]    8    9
##  [3,]    9   10
##  [4,]   10    1
##  [5,]   10    2
##  [6,]    9    3
##  [7,]    8   11
##  [8,]   11    4
##  [9,]   11    5
## [10,]    7    6

… we see a matrix of 10 rows and 2 columns. This matrix represents unique combinations of node- and tip numbers, defining each branch segment of the tree.

    plot(mini.phy, label.offset=0.2)  
    # the label.offset argument moves the names a bit to the right
    nodelabels() # add node numbers
    tiplabels()  # add tip numbers

 

index03For example, the branch (or edge) leading to species “C” is identified by row 6: 9, 3. Both nodelabel() and tiplabel() function are quite flexible and afford the opportunity to visually anhance your tree plot. Here’s an example for mini.phy object. We begin by creating an vector of different colors, with the same length as number of species in the tree.

    mycol<-c("blue", "blue", "blue", "red", "red", "red")

Now we plot the tree, moving the taxon names a bit to the right, and add the tiplabels without text, using symbols instead.

    plot(mini.phy, adj=0, label.offset=0.75, lwd=2)
    tiplabels(pch=21, col="black", adj=1, bg=mycol, cex=2)

 

index04Swapping sisterclades, identifying clades/tips, dropping tips

It sometimes may be useful to rotate the tree about a specific node, i.e. swap sister clades. This can be carried out with the rotate() function. Let’s continue to work with mini.phy:

    plot(mini.phy, label.offset=0.2)  
    nodelabels()                      
    tiplabels()                       

 

index05How about we swap clades (D, E) and (A, B, C)? Their most recent common ancestor is found at node 8.

    rot.phy <- rotate(mini.phy, node=8)

And now let’s see what happenend:

    plot(rot.phy, label.offset=0.2)   
    nodelabels()                      
    tiplabels()                       

 

index06It will also be very helpful to select all tips in a given clade. This is implemented in the ‘geiger’ package; the tips() function finds all descendants of a node.

    cladeABC <- tips(rot.phy, node=9) 
    # node 9 defines the clade composed of (A, B, C)
    
    cladeABC
## [1] "A" "B" "C"

Another helpful command allows for tree pruning, i.e. cutting of tips or entire clades. For example, we can delete the entire cladeABC group:

    pruned.phy <- drop.tip(rot.phy, cladeABC)
    plot(pruned.phy, label.offset=0.2)    
    nodelabels()                          
    tiplabels()                          

 

index07Or we can drop tips (1 or multiple) randomly. Liam Revell explained how to do this nicely on his blog. To prune tips, say m=2 random tips, enter:

    m=2
    pruned.phy2 <- drop.tip(rot.phy, sample(rot.phy$tip.label)[1:m]) 
    
    # m=1 drops 1 single tip, of course!
    
    plot(pruned.phy2, label.offset=0.2)   

index08It may also be useful to select all branches in a specific group of tips. This is implemented in the ‘ape’ package; the which.edge() function finds all edges in a specified group. For example, let’s identify all branches of the cladeABC group as defined above.

    cladeABCbranches <- which.edge(rot.phy, cladeABC) 
    
    # cladeABC was defined earlier, using the tips() function
    
    cladeABCbranches 
## [1] 6 7 8 9
    # this should be a numerical vector containing 6, 7, 8, 9

And as we can see, rows 6-9 of the edge.length matrix represent the expected branches. Let’s first plot the tree, and then look at the edge matrix for cross-checking.

    plot(rot.phy, label.offset=0.2)   
    nodelabels()                      
    tiplabels()                       

index09

    rot.phy$edge # compare edge matrix and tree plot
##       [,1] [,2]
##  [1,]    7    8
##  [2,]    8   11
##  [3,]   11    4
##  [4,]   11    5
##  [5,]    8    9
##  [6,]    9   10
##  [7,]   10    1
##  [8,]   10    2
##  [9,]    9    3
## [10,]    7    6

Here would be a good opportunity to show you how to assign different branch colors. For example, how can we emphasize the branches of the clade formed by A, B, and C? We first create a color vector, only consisting of grey colors. Then we’ll assign black to all branches of clade ABC.

    clcolr <- rep("darkgrey", dim(rot.phy$edge)[1]) 
    clcolr[cladeABCbranches] <- "black"
    plot(rot.phy, lwd=3, edge.color=clcolr)

 

index10Let’s conclude this section with one last exercise: combining trees. Assume you have two different phylogenies, with two different sets of taxa (no overlap). Another assumption is that you have knowledge how the trees may fit together. Then the bind.tree() function of ‘ape’ package can help. The function takes in two phylo-objects. The position of where the trees are bound is defined by tip- or node number within the first tree. Note that you can also specify the “root” as binding position.

    tree1 <- rtree(n=10); plot(tree1); nodelabels(); tiplabels()

index11

    tree2 <- rtree(n=10); plot(tree2)

index12

    combined.tree <- bind.tree(tree1, tree2, where=1) 
    plot(combined.tree)

index13

Fully resolved and polytomous trees

All tree examples so far were fully resolved, i.e. each tree was fully binary. It’s very easy to access visually for small trees, but we can also do this more formally:

    is.binary.tree(mini.phy)
## [1] TRUE

Let’s make a tree that is not fully resolved.

    poly.phy <- read.tree(text = "(((A,B,C),(D,E)),F);")
    plot(poly.phy)

index14

  # Is this tree binary?

    is.binary.tree(poly.phy)
## [1] FALSE

OK. Many comparative methods require fully resolved trees. But what to do if that’s not the case? The multi2di() function can resolve polytomies, either randomly or in the order in which they appear in the tree. The default setting is to resolve polytomies randomly.

    resolved.phy <- multi2di(poly.phy)
    is.binary.tree(resolved.phy)
## [1] TRUE
    plot(resolved.phy) # visual inspection.

index15If you repeat the above lines a few times you will see the effect of randomly resolving the polytomy.

Modifying tree shape and other plotting options

There are many options for formatting and beautifying trees in R. Here are some basics. Let’s begin by simulating a tree once more.

    phy <- rtree(n=10) # n specifies the number of tips we want.

    # The default plot produces a rightwards tree

    plot(phy)

index16 The tree orientation can be changed by modifying the “direction”- argument. Try it out!

    plot(phy, direction="upwards") 

index17

    # other options are "rightwards" (default), "leftwards", and "downwards".

The font size of the tip labels (species names) can be changed with the cex argument.

    plot(phy, 
         direction="upwards", 
         cex=2.5) 

index18Try a few different settings! If we don’t want the species names displayed, you can do the following:

    plot(phy, 
         direction="upwards", 
         show.tip.label=FALSE)

index19And if you would like thicker branches, do this:

    plot(phy, 
         direction="upwards", 
         show.tip.label=FALSE,
         edge.width=3) 

index20Color of the branches can be controlled by the edge.color argument:

    plot(phy, 
         direction="upwards", 
         show.tip.label=FALSE, 
         edge.width=3, 
         edge.color="blue") 

index21Try a few different settings! There are many other options… Type ?plot.phylo in the console to read more.

Another useful command I’d like to introduce is the ladderize() function. It reorganizes the tree structure, normally yielding much more readable trees. Let’s make a bigger tree to really visualize the ladderize-effect.

    big.phy <- rtree(n=50)
    ladderized.phy <- ladderize(big.phy)

Let’s create a plot with two panels, contrasting ‘before” and ’after’. With par() we can include the mfrow option, which specifies number of rows and columns in the plot matrix.

    par(mfrow=(c(1,2))) # 1 row but 2 plot panels

    plot(big.phy, direction="upwards", 
         show.tip.label=FALSE,
         edge.width=1,
         edge.color="blue")

    plot(ladderized.phy, direction="upwards", 
         show.tip.label=FALSE,
         edge.width=1,
         edge.color="blue")

index22

    par(mfrow=c(1,1)) #for subsequent single-panel plots

Finally, let’s change the type of the tree. This is done by modifying the “type” argument. The default is set to “phylogram.” Other options include “cladogram”, “fan”, “unrooted”, and “radial”. Try all of them!

    plot(ladderized.phy, type="fan", 
         show.tip.label=FALSE,
         edge.width=3,
         edge.color="blue")

index23

Tons of options! And kind of fun.

Posted in R | Tagged , , , , ,

Blind cavefish: social butterfly or awkward turtle?

By Nour Bundogji (Pitzer College), Niti Nagar (Claremont McKenna College), and Sachin Shah (Claremont McKenna College) [Edited by Lars Schmitz, as part of BIOL 167 "Sensory Evolution", an upper division class at the W.M. Keck Science Department. Written for educational purposes only].

Would you have ever guessed that the evolution of sensory systems may dictate your social behaviors? Well, this certainly seems to be the case for cavefish, Astyanax mexicanus. Many studies have shown that fish are quite social creatures. In fact, Anna Greenwood, a scientist in the Human Biology Division at Fred Hutchinson Cancer Research Center said, “the motivation to be social is common among fish and humans, where some of the same brain regions and neurological chemicals that control human social behavior are probably involved in fish social behavior.” The two types of social behaviors that A. mexicanus exhibit are schooling and shoaling. As part 1 of an amazing twin paper in Current Biology, Kowalko and colleagues (2013) distinguished between these two behaviors by defining schooling as the tendency of fish to synchronize their behavior and swim together, while shoaling is the tendency of fish to group with other fish of the same species.  Both these social behaviors provide a number of advantages such as deterring attacks from predators and foraging for food.

Figure 1. Beautiful Mexican blind cavefish. [By H. Zell (Own work) [GFDL (http://www.gnu.org/copyleft/fdl.html) or CC-BY-SA-3.0 (http://creativecommons.org/licenses/by-sa/3.0)], via Wikimedia Commons]

Figure 1. Beautiful Mexican blind cavefish. [By H. Zell (Own work) [GFDL (http://www.gnu.org/copyleft/fdl.html) or CC-BY-SA-3.0 (http://creativecommons.org/licenses/by-sa/3.0)%5D, via Wikimedia Commons]

In this study, several populations of A. mexicanus (Figure 1) were used to investigate the mechanism responsible for loss of schooling and shoaling behaviors: the sighted surface dwelling and three independently evolved blind cave-dwelling fish (Tinaja, Pachon, and Molino populations).

So, what exactly are the reasons behind the absence of schooling and shoaling behavior in cavefish? Kowalko and colleagues proposed four hypotheses behind this loss. For example, it may be due to the absence of large predators in caves, which minimizes the need for fish to travel close together in schools. In addition, food is fairly scarce in many caves, which may make these behaviors unfavorable. This loss could otherwise be due to the fact that cavefish have lateral line systems that are different from their surface dwelling counterparts. Lastly, the disappearance of this behavior may be due to genetic components that are independent of vision loss.

Kowalko and her fellow researchers first investigated the differences in schooling and shoaling behaviors in the two types of A. mexicanus. They measured schooling by recording the tendency of fish to follow a model school of plastic fish (Figure 2).  The surface fish followed the model school and the three independently evolved cavefish did not display any schooling behaviors.

Figure 2. Kowalko et al. designed this apparatus shown above to measure schooling behavior [from Kowalko et al., 2013]

Figure 2. Kowalko et al. designed this apparatus shown above to measure schooling behavior [from Kowalko et al., 2013]

Tests for shoaling were integrated in the tests for schooling where the researchers measured the average distance to the nearest fish. They found that surface fish swam closer together than any other cavefish population (Figure 3).

Figure 3. Shoaling was measured by quantifying the average nearest neighbor distance and average inter-individual distance. Results show surface swam closer together than any other cavefish. Thus, cavefish have lost the tendency to school or shoal. [from Kowalko et al., 2013]

Figure 3. Shoaling was measured by quantifying the average nearest neighbor distance and average inter-individual distance. Results show surface swam closer together than any other cavefish. Thus, cavefish have lost the tendency to school or shoal. [from Kowalko et al., 2013]

Surface fish show aggregating behavior even when they are raised in isolation, so there must be some kind of genetic determination of this behavior. When looking at the genetics of schooling behavior, the Kowalko team crossed surface fish with Tinaja cavefish to determine whether this trait was dominant or recessive. The F1 generation of fish portrayed that schooling is a dominant behavior. However, the F2 generation revealed fish with schooling and non-schooling behaviors indicating social behavior has a polygenic basis.

Cavefish have larger number and size of cranial neuromasts in comparison to surface fish (Yoshizawa et al., 2014). Kowalko and his fellow researchers used a similar reasoning to test their lateral line hypothesis by comparing the number of neuromasts to the proportion of time spent schooling and shoaling for both surface and cavefish. However, they failed to find a relationship between the two, concluding that cave environments do not have an effect on the evolutionary loss of these behaviors.

So, what is responsible for the loss of schooling and shoaling? To determine this, they tested whether loss of schooling would be seen in cavefish that lost visual function during development. Removing one lens from surface fish larvae produced a significant effect on shoaling behavior such that fish swam farther away from one another. When surface fish had both lenses removed, they schooled significantly less from those with one lens removed. Thus, it was concluded that vision is essential for schooling and shoaling behaviors (Figure 4).

Figure 4. Variation of lenses in surface fish (C-E) compared to cavefish (F). Lens removal resulted in less social behavior (G,H). [from Kowalko et al., 2013]

Figure 4. Variation of lenses in surface fish (C-E) compared to cavefish (F). Lens removal resulted in less social behavior (G,H). [from Kowalko et al., 2013]

To further investigate this finding, these researchers looked at visual function, defined as the ability to sense light. This was compared between the F1 generation, cavefish, and surface fish. The surface fish and the F1hybrids spent most of their time in the dark (avoiding the light), indicating their ability to detect light. On the other hand, the cavefish did not indicate a preference for the light or dark environment, indicating their inability to detect light. Many non-schooling fish have little visual function, as they display no light preference. However, some light detecting fish do not show schooling behavior, suggesting that there may be a visually independent component to schooling and shoaling.

Overall, vision seems to be the determining factor responsible for these behaviors in fish. This indicated that the evolution of sensory systems can contribute to the complexity of social behaviors in vertebrates. In this study, it was further suggested that two eyes are responsible for following the haphazard movement of fish, which is important for maintaining normal schooling and shoaling behaviors.

Although the researchers were effective in showing the importance of vision-dependent mechanisms in social behavior, there were some minor shortcomings to their investigation, leaving room for future studies. Previous work has shown that experimentally blinded surface fish exhibit schooling behaviors, which contradicts results found in this study (Partridge and Pitcher, 1980). This discrepancy suggests that further studies investigating visual-independent factors should be conducted to confirm either finding. For example, this study did not account for discrepancies in habitats or other selective mechanisms, such as the presence of predators or the scarcity of food. Furthermore, only Tinaja cavefish were tested when examining the effect of the lateral line system. It was also found last year that benthic sticklebacks (another really important species for understanding evolution; see Greenwood et al. 2013 — part 2 of the twin paper!) displayed differences in social behavior dependent on the evolution of the lateral line system, which wasn’t the case for A. mexicanus. Thus, it would be advantageous to conduct the same lateral line system test for more cavefish populations and other species.

References

Greenwood, A. K., Wark, A. R., Yoshida, K., Peichel C. L. 2013. Genetic and neural modularity underlie the evolution of schooling behavior in Threespine Sticklebacks. Current Biology 23: 1884-1888. (DOI: 10.1016/j.cub.2013.07.058)

Kowalko, J.E., Rohner, N., Rompani, S. B., Peterson, B. K., Linden, T. A., Yoshizawa, M., Kay, E. H., Weber, J., Hoekstra, H. E., Jeffery, W. R., Borowsky, R., T., Clifford J. 2013. Loss of schooling behavior in cavefish through sight-dependent and sight-independent mechanisms. Current Biology 23, 1874-1883. (DOI: 10.1016/j.cub.2013.07.056).

Partridge, B. L., Pitcher, T. J. 1980. The sensory basis of fish schools: roles of lateral line and vision. Journal of Comparative Physiology 135, 315-325. (DOI: 10.1007/BF00657647).

Yoshizawa, M., Jeffery, W. R., van Netten, S. M., McHenry, M. J. 2004. The sensitivity of lateral line receptors and their role in the behavior of Mexican blind cavefish (Astyanax mexicanus). Journal of Experimental Biology 217: 886. (DOI: 10.1242/​jeb.094599).

Posted in mechanosensation, sensory evolution blogs, vision | Tagged , , , , , , | Leave a comment

One fish, two fish, red fish, blind fish

By Nour Bundogji (Pitzer College), Niti Nagar (Claremont McKenna College), and Sachin Shah (Claremont McKenna College) [Edited by Lars Schmitz, as part of BIOL 167 "Sensory Evolution", an upper division class at the W.M. Keck Science Department. Written for educational purposes only].

Eyes are essential organs for detecting predators, foraging for food, finding shelter, mating, and other visually guided behaviors. But, how is it possible that evolution sometimes selects for the loss of eyes? More specifically, in what situations would this occur? The eyeless cavefish, Astyanax mexicanus, develop eyes as embryos but lose them as they mature (Jeffery, 2001). They serve as an ideal species to study because they are one of the few cave animals that have living surface-dwelling ancestors, which allows for a natural and direct comparison. The river surface provides a light environment where bigger eyes are more helpful than smaller eyes. However, in the cave, big eyes have no benefit because it is completely dark. For cave-dwelling fish, not having eyes may have a number of advantages. Energy spent on maintaining eyes can instead be conserved and invested in traits, such as expanded taste buds and cranial neuromast modules that are useful in a dark environment (Franz-Odendaal & Hall, 2003). Thus, it was adaptively advantageous for fish with smaller eyes to be selected.

Figure 1.  Blind Mexican cavefish [image from "Grand-Duc, Wikipedia, http://en.wikipedia.org/wiki/User:Grand-Duc"]

Figure 1. Blind Mexican cavefish [image from "Grand-Duc, Wikipedia, http://en.wikipedia.org/wiki/User:Grand-Duc"]

To answer how this evolutionary change occurred, it is required to take a closer look into the extent to which our biological programming can be altered by environmental influences. Unlike Darwin’s emphasis on mutation and subsequent natural selection, Conrad Waddington proposed evolution by pre-existing genes that could be turned on (or off) depending on environmental conditions. Although these pre-existing genes were usually hidden, they could be activated by particularly stressful environmental factors. In a landmark study, Waddington exposed fruit fly pupae to stress using bursts of heat or chemicals which resulted in an array of abnormal features, such as extra body segments in adult flies (Waddington, 1953). By applying heat or chemicals, Waddington lifted a repression mechanism that allowed for hidden genetic variation to manifest as physical variations.

Figure 2.  Waddington showed how developmental mechanisms could be studied through the analysis of mutations of the Drosophila wing [Photograph from Robertson, A. Conrad Hal Waddington 8 November 1905–26 September 1975. Biogr. Mem. Fellows R. Soc. 23, 575–622 (1977) © The Royal Society]

Figure 2. Waddington showed how developmental mechanisms could be studied through the analysis of mutations of the Drosophila wing [Photograph from Robertson, A. Conrad Hal Waddington 8 November 1905–26 September 1975. Biogr. Mem. Fellows R. Soc. 23, 575–622 (1977) © The Royal Society]

Under certain stressful environmental conditions, biological processes that mask genes can be destabilized, allowing for variance in genes and traits. When exposed to stress, variants that improve the animal’s ability to adapt to a new environment can then be selected for, and passed on to future generations. Thus, repression mechanisms allow certain genes to be masked resulting in unvarying traits within a population despite underlying genetic variation.

In accordance with Waddington’s idea, Rohner and his team of researchers may have found evidence for a mechanism of evolutionary change that is not due to natural selection of a spontaneous mutation for eye loss in A. mexicanus. The team demonstrated how “cryptic” or existing genetic variations in cavefish, which have been inherited from prior generations without causing any physical changes, can be “unmasked” by the shock of entering a new environment. Scientists hypothesized the environmental shock that induced the loss of eyes in cavefish occurred when a group of cavefish colonized deep and dark underwater caves. The cave presented an unfamiliar environment, where the water was purer and less conductive of electricity than the river water inhabited by surface fish.

It has been previously shown that the “heat shock” protein called HSP90 functions to buffer genetic variation and morphological changes. HSP90 helps other proteins fold properly, stabilizes proteins against heat stress, and aids in protein degradation.

Figure 3. HSP90 is activated under stressful conditions [Rohner et al., 2013]

Figure 3. HSP90 is activated under stressful conditions [Rohner et al., 2013]

Under stressful conditions, HSP90 can be depleted which allows for changes in protein folding that could ultimately lead to changes in phenotypes (Taipale et al., 2012). Under typical circumstances, cavefish show little variation in eye size. However, when treated with radicicol, a chemical that mimics stress by inhibiting HSP90, increased variation in eye size was seen in both surface and cavefish (Figure 4). HSP90 inhibition unmasked cryptic genetic variations, allowing for a larger range of eye sizes to be expressed in individuals. Interestingly, cavefish showed a significant decrease in orbit size (Figure 5). This difference suggests cavefish may have eliminated the upper range genetic variation for eye size and that genetic variation for eye loss is sensitive to HSP90.

Figure 4. HSP90 inhibition in F2 hybrids treated with radicicol reveal significant increase in SD of eye size, but not average eye size. Here are examples of eye size variation in F2 population hybrid  [Rohner et al., 2013]

Figure 4. HSP90 inhibition in F2 hybrids treated with radicicol reveal significant increase in SD of eye size, but not average eye size. Here are examples of eye size variation in F2 population hybrid [Rohner et al., 2013]

Figure 5. HSP90 inhibition in cavefish show increased variation and a lower average eye size [Rohner et al., 2013]

Figure 5. HSP90 inhibition in cavefish show increased variation and a lower average eye size [Rohner et al., 2013]

Secondly, experimenters found that the reduced conductivity of the cave environment can elicit a stress response in fish that causes the up-regulation of the “heat shock” protein called HSP90. Surface fish embryos raised in low-conductivity conditions of the cave up-regulated HSP90, showing that they are indeed in a state of physiological stress response. Moreover, they activated the same heat shock response gene with HSP90 inhibition by radicicol and similarly showed a significant increase in eye variation (Figure 6). Thus, the environment encountered by these fish during their evolutionary transition from surface to cave stressed the protein homeostasis mechanisms of the organism in a manner similar to the inhibition of HSP90’s chaperone activities. This demonstrates that a cave-specific environmental stress can inhibit biological processes resulting in the activation of genes for smaller eyes. This means genetic material that coded for proteins that produce smaller eyes already existed but wasn’t activated until stress was present.

Figure 6. Environmentally stressful conditions inhibit HSP90 and unmask genes which allows for greater genetic variation. [Rohner et al., 2013]

Figure 6. Environmentally stressful conditions inhibit HSP90 and unmask genes which allows for greater genetic variation. [Rohner et al., 2013]

Finally, the team investigated whether these developmentally induced changes could be genetically assimilated, meaning could the phenotype for eye loss become genetically encoded via natural selection. To test for genetic assimilation, Rohner and his team treated surface fish with radicicol and interbred individuals with the smallest eyes. Even when raised without radicicol, the stress stimuli, offspring had significantly smaller eyes than the untreated adult population (Figure 7). Therefore, once genes for small eyes were activated and manifested as physical traits, they could be passed down to future generations despite the presence of environmental stress.

Figure 7. F2 generations of radicicol treated offspring have significantly smaller eye sizes [Rohner, et al., 2013]

Figure 7. F2 generations of radicicol treated offspring have significantly smaller eye sizes [Rohner, et al., 2013]

Although there is convincing evidence that eye development is buffered by HSP90 and stress responses result in increased heritable eye size variation, we think the argument of genetic assimilation is somewhat lacking. The experiment conducted by Rohner and his team does not have a control group of untreated surface fish. If the F2 generation of untreated surface fish produced similar results, a more compelling explanation is needed. Alternatively, if there is very little response to selection, the results become much more convincing.

Nevertheless, Rohner and colleagues’ findings give us great insight on evolutionary mechanisms and confirms Waddington’s hypothesis that genetic variation can be exposed by disruptions in development mechanisms caused by environmental stress. Yet, these findings also open a door for further investigation such as determining if HSP90 controls the evolution of many species or a few. Furthermore, the process relies on sudden environmental changes that usually occur at microscopic scales whereas larger organisms experience evolution in gradually changing environments. So, are blind cavefish an exception to the norm or the first example of a common phenomenon? This poses the question if natural selection by pre-existing genetic variation is more common in smaller than larger organisms and to what extent does environmental stress play a role in dictating traits of other organisms. Additionally, taking a closer into the neuronal visual processing function in cavefish would be interesting. Do these cavefish essentially lose their ability to visually process and if so, what other type of processing replaces it?

This style of evolution is no different from what Darwin envisioned. Variation is still passed down to generations and produces traits that affect the fitness of individuals. The only difference is that the variation is not caused by spontaneous mutations, but already exists just in a hidden form. This variance still provides a platform on which natural selection can run its course–it’s just faster.

References

Franz-Odendaal, T., Hall B. K. 2006. Modularity and sense organs in the blind cavefish, Astyanax mexicanus. Evolution & Development 8: 94-100. (DOI: 10.1111/j.1525-142X.2006.05078.x)

Jeffery, W. R. 2001. Cavefish as a model system in evolutionary developmental biology. Developmental Biology 231: 1-12. (DOI: 10.1006/dbio.2000.0121)

Rohner H., Jarosz D. F., Kowalko J. E., Yoshizawa M., Jeffery W. R., Borowsky R. L., Lindquist, S., Tabin, C. J. 2013. Cryptic variation in morphological evolution: HSP90 as a    capacitor for loss of eyes in a cavefish. Science 342: 1372-1375. (DOI: 10.1126/science.1240276)

Taipale, M., Krykbaeva, I., Koeva, M., Kayatekin, C., Westover, K. D., Karras, G. I., Lindquist, S. 2012. Quantitative analysis of HSP90-client interactions reveals principles of substrate recognition. Cell 150: 987–1001. (DOI: 10.1016/j.cell.2012.06.047)

Waddington, C. H. 1953. Genetic assimilation of an acquired character. Evolution 7: 118.

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Neuronal nonlinearity: explaining a visual conundrum

By Madison Knaub (Claremont McKenna College) [Edited by Lars Schmitz, as part of BIOL 167 "Sensory Evolution", an upper division class at the W.M. Keck Science Department. Written for educational purposes only].

How it all began: From Galileo to the big leagues

When looking at the planets with the naked eye it often seems like Venus is larger than Jupiter. Of course, anyone who has taken rudimentary astronomy knows that Jupiter is in fact 11.7 times larger than Venus (for a cool visual representation of planet size click here). Galileo was one of the first people to observe, and document, this visual spatial discrepancy. He believed that this strange visual phenomenon was due to a light-formed optical illusion that caused Venus to appear larger.

Figure 1. Venus, the bright mark to the right, appears larger than Jupiter, the bright mark to the left. [http://commons.wikimedia.org/wiki/File:Jupiter_and_Venus_12-03-13_conjunction.JPG]

Figure 1. Venus, the bright mark to the right, appears larger than Jupiter, the bright mark to the left. [http://commons.wikimedia.org/wiki/File:Jupiter_and_Venus_12-03-13_conjunction.JPG]

This visual conundrum was later termed the “irradiation illusion” by German physicist Hermann von Helmholtz, who first began to question the science behind Galileo’s observation. But while many scientists and researchers have confirmed that there is indeed a glitch in the visual system causing visual processes to inaccurately perceive illuminated object size (Ts’o et al. 1990) no one ever really understood why. That is, until now. In 2014, a team of scientists around Jens Kremkow and Jose-Manuel Alonso at the State University of New York found neurophysiological processes within the visual system that are responsible for this mysterious visual conundrum.

Some background (because who really remembers what they learned in intro BIO or NEURO?)

Light is processed within the eye through two separate mechanisms. These mechanisms are referred to as ON and OFF channels. The ON cells function in response to positive contrast, which is a light stimulus on a dark background. Conversely, the OFF cells function in response to negative contrast which is dark stimulus on a lighter background, and so far it had been assumed that the neuronal response to positive and negative contrasts was about equal. (For a more in-depth look at ON and OFF channels check out this cool slideshow by Peter H. Schiller at MIT!) So when you look at the night sky what you are seeing is the positive contrast of the bright stars against the dark sky. How is it that the spatial resolution of dark objects (Jupiter) appears better than that of bright objects (Venus), which appear as a big, poorly defined light spot?

Figure 2. The box to the left is an example of positive contrast, light stimulus on a dark background. The box to the right is an example of negative contrast, dark stimulus on a light background. [http://commons.wikimedia.org/wiki/File:Simultaneous_Contrast.jpg]

Figure 2. The box to the left is an example of positive contrast, light stimulus on a dark background. The box to the right is an example of negative contrast, dark stimulus on a light background. [http://commons.wikimedia.org/wiki/File:Simultaneous_Contrast.jpg]

Cats, brains, and neurons. Oh my!

In order to study the neuronal mechanism responsible for the “irradiation illusion” the scientists at the State University of New York looked at how ON/OFF channels responded to different variations of positive and negative contrast. In the first experiment they performed, neuronal activity was recorded in anesthetized cats as the animals were exposed to different variations of light stimuli, dark stimuli, and different backgrounds to increase or decrease contrast. Researchers focused on neuronal activity in parts of the brain responsible for vision, the primary visual cortex and the lateral geniculate nucleus in the thalamus. (For a video showing how vision works with the brain click here).

When the cats were exposed to positive contrast, light stimuli on a dark background, their ON channels were activated and the researchers were able to measure the strength on the neuronal response within the brain. Here comes the interesting part. When the researchers exposed the cats to negative contrast, dark stimuli on a light background, they noticed that strength of response for the OFF channels was linearly related to the luminance decrements, for both white and gray backgrounds. A different pattern was observed for the ON channels. Especially when seen against a dark background, the ON channels saturated much more quickly with luminance decrements, or in other words even the faintest bright signal on a dark background elicits a strong neuronal response (Figure 3). It seems that functions resulting from bright signals on dark backgrounds (ON functions) are highly compressed compared to OFF functions, as seen in the much more linear, gradual response increase. Hence, bright signals on dark background produce somewhat of a ”neuronal blur”.

Figure 3. Response to negative (A and B) and positive (C and D) contrast with increasing luminance and different background. Note the linearity of the OFF channels (A and B). [originally Figure 3 A_D of Kremkow et al. 2014].

Figure 3. Response to negative (A and B) and positive (C and D) contrast with increasing luminance and different background. Note the linearity of the OFF channels (A and B). [originally Figure 3 A_D of Kremkow et al. 2014].

While to us this may seem like nothing special, these findings contradict many preexisting assumptions held about visual processes. For quite some time it had been believed that ON and OFF channels in the brain are roughly equal in presence and function. But, these new finding imply that OFF functions are actually predominate in the visual process, while the ON functions are much more compressed.

How does this tie in with the irradiation illusion? With a lesser amount of ON functions, the brain has a weakened ability to process and resolve the spatial resolution for light stimuli and positive contrast. So when looking at objects like Venus and Jupiter the brain is unable to accurately perceive the planet’s actual size. Venus is very bright compared to Jupiter and stands out against a dark background. The compressed signal from negative contrasts, combined with neuronal blur causes the brain to perceive light stimuli, like Venus, as larger in appearance than its actual physical size.

What it all means: evolution and beyond

These findings have interesting implications for our understanding of visual evolution. The researchers concluded that the nonlinear response of ON channels may have originated as early as the photoreceptor (Kremkow et al. 2014). This implies that the neural nonlinearity of ON channels evolved with an eyes ability to detect light. So why would an eye evolve to have greater spatial resolution for dark stimuli and poorer spatial resolution for light stimuli? One possible answer put forward in the paper was that this evolutionary puzzle was due to a greater prevalence of dark stimuli in nature (Kremkow et al. 2014). But, of course, there are many possible answers to this question of “why” eyes are better at resolving dark images. Perhaps this visual process evolved in response to the light conditions of predator favored environments. Since many predatory species favor dark conditions for hunting, better spatial resolution of dark images would give prey an advantage at detecting hidden predators like the black jaguar shown below. It would be interesting to do further research on known predators and prey species to see the differences in their spatial resolution for dark and light stimuli. As my sensory evolution Professor, Lars Schmitz, would say, “I smell a paper.”

In conclusion, these findings on the discrepancies between neuronal functioning in the ON and OFF channels finally provide some much needed details for the understanding of Galileo’s strange, starry observation. So the next time you step outside on a clear night take a look at Jupiter and Venus. Which looks bigger? If Venus catches your eye, you now know that this spatial discrepancy is not the work or a visual illusion but rather your brain and visual processes creating your very own visual conundrum.

 

References

Kremkow, J., Jin, J., Komban, S.J., Wang, Y., Lashgari, R., Jansen, M., Li, X., Zaidi, Q, Alonso, J.-M. 2014. Neuronal nonlinearity explains greater visual spatial resolution for darks than lights. PNAS, published online. (DOI: 10.1073/pnas.1310442111).

Ts’o, D., Frostig, R., Lieke, E., Grinvald, A. 1990. Functional organization of primate visual cortex revealed by high resolution optical imaging. Science, 249: 417-420. (DOI: 10.1126/science.2165630)

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What genes tell us about pinhole- and camera-eye evolution in cephalopods

By Nolan Lassiter (Pitzer College) and Ryan Madden (Pitzer College) [Edited by Lars Schmitz, as part of BIOL 167 "Sensory Evolution", an upper division class at the W.M. Keck Science Department. Written for educational purposes only].

Imagine how difficult it would be to climb the evolutionary ladder without being able to see! Eyes are often considered to have first begun to evolve from organisms with single photosensitive cells during the “Cambrian explosion” of life. In studying the rise and evolution of eyes, we often see a strong evolutionary selection for complex eyes with stronger acuity and clarity, especially among predators. This is because better eyes presumably allow the predator a greater chance to catch prey and may result in better fitness. Thus, it is always curious to see when a specific type of predator in a large predatory clade has seemingly weaker and morphologically different eyes than the others.

Figure 1. The Nautilus has a remarkable pinhole eye. Seawater can enter the eye chamber because there is no cornea or lens (!). [from http://species.wikimedia.org/wiki/File:Nautilus_pompilius_%28head%29.jpg ]

Figure 1. The Nautilus has a remarkable pinhole eye. Seawater can enter the eye chamber because there is no cornea or lens (!). [from http://species.wikimedia.org/wiki/File:Nautilus_pompilius_%28head%29.jpg ]

Figure 2. Schematic cross section of a pinhole eye. The aperture must remain small in order to generate an image of reasonable quality. [from http://upload.wikimedia.org/wikipedia/commons/thumb/a/a3/Pinhole_eye.svg/500px-Pinhole_eye.svg.png ]

Figure 2. Schematic cross section of a pinhole eye. The aperture must remain small in order to generate an image of reasonable quality. [from http://upload.wikimedia.org/wikipedia/commons/thumb/a/a3/Pinhole_eye.svg/500px-Pinhole_eye.svg.png ]

The Nautilus is the only cephalopod that does not have a lens-bearing camera-type eye (Figure 1). It is, however, still a predator that forages and feeds on smaller organisms in the sand/dirt in the bottom of the ocean. It utilizes a pinhole eye (Figure 2) that doesn’t protect the retina from the water of its environment and lacks a refracting apparatus, and as a result is not considered to have strong vision. While it is able to differentiate basic objects, the Nautilus does not have as strong of eyesight as its cousin’s, the squids, which have lens-bearing camera-like eyes (Figure 3). How did the eyes of cephalopods evolve?

Figure 3. Cephalopod eye anatomy (Bathyteuthis). Cephalopds have eyes that are convergent to vertebrate eyes --- both have camera-type eyes with a single lens. [from http://en.wikipedia.org/wiki/File:Eye_squid.jpg ]

Figure 3. Cephalopod eye anatomy (Bathyteuthis). Cephalopds have eyes that are convergent to vertebrate eyes — both have camera-type eyes with a single lens. [from http://en.wikipedia.org/wiki/File:Eye_squid.jpg ]

To explore the genetic control of development of pinhole and camera-type eye in cephalopods, Sousounis et al. (2013) utilized next-generation RNA sequencing, targeting Nautilus and the Pygmy squid. RNA sequencing has revolutionized the exploration of gene analysis and its application in the study of evolution, by allowing researchers to examine phylogenetic relationships at a molecular level. Previous research had demonstrated that there are several common genes related to lens and photoreceptor development found in both Drosophila and human genomes (Halder et al. 1995) that potentially could also be similar to those found in molluscs. Essentially, by identifying genes responsible for eye development one can reconstruct the complex evolutionary path to vertebrate and invertebrate eyes.

Sousounis et al. examined the RNA transcriptomes that were “marked” in developing Nautilus and Pygmy squid eyes. Then the contigs, or overlaps between sliced RNA fragments of various length, were compared to examine what genes are responsible for developing the Nautilus pinhole-type eye as opposed to a camera-type eye in squid. Basically, the researchers were able to enrich the RNA and examine exactly what sections of the genome specifically guide the sequencing and assembly of the eye. This examination also allowed for comparison of the contigs to that of Drosophila and human eyes. There were many interesting findings in the study that led to a more directed hypothesis and remained in line with previous discoveries regarding the differences in vertebrate and invertebrate photoreceptors (Fernald, 2006).

Genes involved in nucleic-acid binding proteins were overexpressed in squid and not Nautilus. Genes involved in metabolic and catalytic function were overexpressed in Nautilus early eye development (Figure 4). Sousounis et al. think that this is a consequence of the faster and more complex morphogenesis of the squid eye.

Figure 4. This figure displays the genetic components that are over-expressed in Nautilus eyes compared to that of the Pygmy Squid. [Figure 2 of Sousounis et al. 2013]

Figure 4. This figure displays the genetic components that are over-expressed in Nautilus eyes compared to that of the Pygmy Squid. [Figure 2 of Sousounis et al. 2013]

Many genes that are used in assembling the cephalopod eyes are similar to human homologues, whereas genes that are part of photoreceptor assembly show a mix of similarities to both humans and flies. Interestingly, the Sousounis team observed a lot of crossover in the genes involved in eye development between all four species, suggesting that eye morphology is largely conserved in beginning stages of development, but then has a few very important genes that help in differentiation and ultimately give rise to the diversity of eye types we have today.

The Sousounis team may also have identified a gene that is centrally important for developing the lens (Figure 5 shows their general approach to this problem). Pygmy squid have CAP1 gene while the Nautilus expresses a slightly different version, called capt. Capt has been found to be involved in morphogenesis in Drosophila, an organism with a compound eye (but not with a single, big lens like in vertebrates and squid). One hypothesis is that the presence and absence of capt and CAP1 might help determining if the eye will have a lens or not. Another gene that may potentially be involved in lens formation is NF1/Nf1, which shows similarities to both human and fly genes.

Figure 5. Nautilus and Pygmy Squid transcriptomes were compared with human and Drosophila proteomes in order to find candidate genes for lens development. Contigs of Nautilus and Pygmy Squid that had a homologue in either human or fly were further examined. [Figure 4 of Sousounis et al. 2013]

Figure 5. Nautilus and Pygmy Squid transcriptomes were compared with human and Drosophila proteomes in order to find candidate genes for lens development. Contigs of Nautilus and Pygmy Squid that had a homologue in either human or fly were further examined. [Figure 4 of Sousounis et al. 2013]

For future investigations, it would be very interesting to utilize either gene add-in or gene knockout methodology to further explore gene functions in the morphogenesis of eyes. For example, one could inhibit or alter the function of the NF1/Nf1 gene, thought to be involved in lens development. Through this analysis the development and exact role of the NF1/Nf1 gene could be further understood. For more insight into early lens development the same approach could be used but instead inhibit the CAP1 gene. Sousounis et al. provide an important piece to the puzzle of eye evolution, but many more pieces remain to be discovered.

References

Fernald, R. D. 2006. Casting a genetic light on the evolution of eyes. Science 313: 1914-1918. (DOI:10.1126/science.1127889).

Halder, G., Callaerts, P., Gehring, W. J. 1995. Induction of ectopic eyes by targeted expression of the eyeless gene in DrosophilaScience 267: 1788-1792. (DOI: 10.1126/science.7892602).

Halder, G., Callaerts, P., Gehring, W. J. 1995. New perspectives on eye evolution. Current Opinion in Genetics & Development 5: 602-609. (DOI: 10.1016/0959-437X(95)80029-8).

Sousounis, K., Ogura, A., Tsonis, P.A. 2013. Transcriptome analysis of Nautilus and Pygmy squid developing eye provides insights in lens and eye evolution. PLoS ONE 8: E78054. (DOI: DOI: 10.1371/journal.pone.0078054).

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Ultraviolet light sensing capabilities in mammals

By Amanda Jacobs (Scripps College) and Grace Rodriguez (Claremont McKenna College) [Edited by Lars Schmitz, as part of BIOL 167 "Sensory Evolution", an upper division class at the W.M. Keck Science Department. Written for educational purposes only].

Scientists have always been aware of animals’ ability to detect ultraviolet (UV) light. While this is a common feature in organisms such as fish, insects, and birds, a study published in January 2014 by Glen Jeffery, a neuroscience professor at London University College; and Ronald Douglas, a biology professor at City University London, decided to research this phenomenon in an overarching study of mammals.

The range of wavelengths that different animals can perceive is dependent on the absorption capabilities of the visual pigments within the retina and the wavelengths of environmental light. Humans can see a spectrum of wavelengths between approximately 400-700 nanometers using three pigments that act as transducers, which convert light into chemical energy that stimulate impulses in neurons. Previously scientists assumed that animals who could detect UV light had types of visual pigments specially adapted to picking up shorter wavelengths and that the only way to estimate such capabilities was to study visual pigments.

Not all visual pigments are alike and can vary between different species as well as within the same species. For example, color blindness in humans is the result of one of the 3 pigments in the cones being defective. Although humans do not have specialized pigments, it has been documented that aphakic individuals (individuals who have defective lenses or have had their lenses removed via surgery) have been able to detect UV light. It is well known that the human lens actually blocks shorter wavelengths, preventing UV light from even reaching the retina where the photoreceptor pigments are. Furthermore, the cornea has also been documented to block wavelengths based on the chemical makeup and thickness, although to a lesser extent than the lens. For the first time, Jeffrey and Douglas focused on what types of light actually reach the retina by focusing on the amount of UV penetration through the lens across a wide variety of mammals.

Jeffrey and Douglas collected the eyes of 38 different mammalian species from zoos, veterinary practices, and other scientific establishments; these species encompassed 25 families in nine different orders. After removing the lenses from the eye, they were mounted in a Shimadzu 2101 UV-PC spectrophotometer for data collection. Light was applied to each lens at 1 nanometer intervals between the range of 300 to 700 nanometers. The percent transmission of each lens at each interval was measured (Percent transmission is a proportion of light intensity entering the sample to light intensity leaving the sample.) Variable numbers of eyes were obtained for each species, and for instances in which many specimens were suitable for study, Jeffrey and Douglas also compared the differences in age and size within the species. All lenses were harvested and studied immediately after death, but in some cases the specimens needed to be temporarily frozen dry, and then thawed for later study. To determine if this preservation process has any effect on light transmission, Douglas and Jeffrey also measured transmission levels before and after flash-freezing; no significant effect was determined (Figure 1) and therefore data collected from both fresh and frozen lenses are comparable.

Figure 1. Average spectral transmission of three bovine lenses before (solid line) and after (dashed line) four days of freezing (Originally Figure 2 from Douglas and Jeffrey, 2014, http://rspb.royalsocietypublishing.org/content/281/1780/20132995/F2.expansion.html)

Figure 1. Average spectral transmission of three bovine lenses before (solid line) and after (dashed line) four days of freezing (Originally Figure 2 from Douglas and Jeffrey, 2014, http://rspb.royalsocietypublishing.org/content/281/1780/20132995/F2.expansion.html)

Between the 38 studied species, the most extreme spectral ranges lie between 50% transmission at 310-320 nm in young murid rodents to 50% transmission at 424-465 nm in primates, sciurid rodents, meerkats, and tree shrews; all other mammals’ spectral capabilities fell between these two extremes. From the 38 studied species, Douglas and Jeffrey selected ten representative lenses that best captured the spectral transmission of all examined specimens (Figure 2).

Figure 2. Average spectral transmission curves at short wavelengths of the lenses of 10 representative mammalian species. From left to right, each curve indicates transmission of  young black rats, cat, okapi, cattle, rabbit, Arabian oryx, squirrel monkey, Alaotran gentle lemur, adult meerkat, and prairie dog. (Originally Figure 3 from Douglas and Jeffrey, 2014, http://rspb.royalsocietypublishing.org/content/281/1780/20132995/F3.expansion.html)

Figure 2. Average spectral transmission curves at short wavelengths of the lenses of 10 representative mammalian species. From left to right, each curve indicates transmission of young black rats, cat, okapi, cattle, rabbit, Arabian oryx, squirrel monkey, Alaotran gentle lemur, adult meerkat, and prairie dog. (Originally Figure 3 from Douglas and Jeffrey, 2014, http://rspb.royalsocietypublishing.org/content/281/1780/20132995/F3.expansion.html)

As expected, the transmission of short wavelengths was highly variable between mammalian species. Young murid rodents’ lenses experience maximum transmission of all light wavelengths in the UVA range (320-400 nm) , while mature diurnal animals such as meerkats and primates have lenses that maximally transmit light in the longer and visible spectrum of wavelengths. Generally, animals that were at least partially nocturnal possessed UV-permissible lenses while the diurnal species did not; the lenses of diurnal species were also observed to have an obvious yellow-coloration. However, this trend is by no means definitive and some exceptions were found, including the okapi which is exposed to large amounts of UV radiation during the day but still possesses a relatively UV-permissible lens.

For species in which multiple specimens were suitable for study, Douglas and Jeffrey also searched for a relationship between short wavelength transmission and the age and size of the animal. In all four species studied (Livingston’s fruit bats, black rats, house mice, and meerkats), short wavelength transmission decreased with both increased age and increased size of the animal (Figure 3).

Figure 3. Lens transmission as a function of lens size/age in rodents. (a) Spectral transmission of 11 black rat (Rattus rattus) lenses ranging in axial length between 3.7 and 5.2 nm. (b) Wavelength of 50% transmission as a function of lens size for all the lenses show in (a). The dotted length is an approximation of the expected relationship if pathlength were the sole factor affecting transmission. (c ) Average wavelength of 50% lens transmission of mice (Mus musculus) of known age. From left to right: 40 (n=3), 70 (n=8), 265 (n=4), and 564 (n=6) days. (Originally Figure 4 from Douglas and Jeffrey, 2014, http://rspb.royalsocietypublishing.org/content/281/1780/20132995/F4.expansion.html)

Figure 3. Lens transmission as a function of lens size/age in rodents. (a) Spectral transmission of 11 black rat (Rattus rattus) lenses ranging in axial length between 3.7 and 5.2 nm. (b) Wavelength of 50% transmission as a function of lens size for all the lenses show in (a). The dotted length is an approximation of the expected relationship if pathlength were the sole factor affecting transmission. (c ) Average wavelength of 50% lens transmission of mice (Mus musculus) of known age. From left to right: 40 (n=3), 70 (n=8), 265 (n=4), and 564 (n=6) days. (Originally Figure 4 from Douglas and Jeffrey, 2014, http://rspb.royalsocietypublishing.org/content/281/1780/20132995/F4.expansion.html)

Previous studies have confirmed that short wavelength UV light can be more damaging to the retina than longer wavelength light (Van Norren & Gorgels, 2011). In conjunction with this previous knowledge, the novel finding that UV-light transmission decreases with age and size of the animal logically makes sense. Blocking UV-light in longer-lived species could protect the animal from these more damaging short wavelengths and protect the retinas during these longer lifespans by slow the degradation of photoreceptors.

The knowledge that some mammals possess the capabilities to detect short wavelengths is not a novel concept (Figure 4), but this paper suggests that UV light sensitivity is much more widespread than previously thought. Other studies have looked as single or a few species, but this is the first one that compares UV sensitivity between many different species using UV penetration of lenses.

Figure 4. The absorption spectra of the visual pigments of only a ferret and the spectral transmission of its lens. As all the visual pigments absorb significant amounts of UV radiation and the lens permits these wavelengths, it is expected that the ferret is likely to be able to see these short wavelengths. (Originally Figure 1 from Douglas and Jeffrey, 2014, http://rspb.royalsocietypublishing.org/content/281/1780/20132995/F1.expansion.html)

Figure 4. The absorption spectra of the visual pigments of only a ferret and the spectral transmission of its lens. As all the visual pigments absorb significant amounts of UV radiation and the lens permits these wavelengths, it is expected that the ferret is likely to be able to see these short wavelengths. (Originally Figure 1 from Douglas and Jeffrey, 2014, http://rspb.royalsocietypublishing.org/content/281/1780/20132995/F1.expansion.html)

To improve this study, more research needs to be done on the yellow pigmentation that was observed in several of the strong UV blocking lenses by identifying the compound(s) that make the lens this color. Furthermore, now that there is data on the UV sensing capabilities across many different clades, one could perform ancestral state reconstructions to determine if UV blocking lenses are independent convergent evolutions or if there is a common ancestor with the trait with multiple losses later on.

References:

Douglas, R.H. & Jeffrey, G. (2014). The spectral transmission of ocular media suggests ultraviolet sensitivity is widespread among mammals. Proc R Soc 281: 20132995. (DOI 10.1098/rspb.2013.2995)

Van Norren D. & Gorgels T.G. (2011). The action spectrum of photochemical damage to the retina: a review of monochromatic threshold data. Photochem. Photobiol. 87, 747–753. (DOI:10.1111/j.1751-1097.2011.00921.x)

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