Physics of biomolecules and cells

Fig. 11. Zebra finches and canaries. Courtesy of A. Alvarez-Buylla.

3.1 The study of songbirds

We intuit as one of the basic features of human beings that we are talking animals. We communicate vocally (the means through which this lecture was originally delivered) and so much of our culture goes through this channel that we hardly think about it as special, or as any more special than an opposable thumb. Curiously, it actually is, for within the animal kingdom there’s precious few species which learn to vocalize. Lions don’t learn to roar from their daddys, nor do dogs learn to bark from mom. Within the great apes there’s no other talker than us, and even within mammals, there’s hardly more than some marmosets and some species of whales and dolphins. These are not exactly “lab” animals, I dare say. Needless to say, vocal learning is even rarer outside of mammals, with one noticeable exception: three orders of songbirds. There’s several dozen species, from canaries, finches, silver sparrows to hummingbirds (which vocalize in the near ultrasound range) which learn vocalizations which get to be extremely elaborate.

So we are left with songbirds as the only viable experimental animal for the study of vocal learning. As is well known, songbirds like canaries are easily bred in captivity and also easily kept and cared for–much easier than whales. As a laboratory animal they are so much nicer than rats that one cannot but feel privileged: they smell much nicer than rats, they sing rather than bite, and are a colorful and cheerful presence in the otherwise drab surroundings of a lab. I shall henceforth talk mostly about canaries,

though some of the pioneering studies in gene expression were carried out in several other species, such as zebra finches.

3.2 Canary song

The first thing that has to be understood about songbirds is that their song is as vital to them as the spoken word is to us. They live and die by song: they warn others of danger, recognize one another, call their children. If they don’t sing well, they don’t get any sex, so they spend most of their spare time practicing. Courtship may be more exhausting than singing the Nibelungen: in some species a male may need to sing for several hours straight to the female before she will accept him.

The song of a songbird satisfies several of the most desirable requirements in a stimulus to be used in neurophysiological studies: it is behaviourally and ecologically relevant, easy to record, and easy to play back to an awake and attentive animal. You may compare with visual stimuli, which while simple to play back to an anesthetized animal, they are hard to play back to an awake and unrestrained animal, for it may easily turn away its gaze. And then what would be an ecologically and behaviourally relevant sound or visual stimulus to play to a rat? On the other hand, we do not yet understand the space of song well enough to generate songs ourselves that may pass as “natural” to a canary–not yet at any rate, though the subject is an active area of study, from the basic features of the vocal production [33] to the statistics of the song ensembles. The di erence between natural and artificial stimuli is quite relevant, as you’ll see shortly.

Canary song is composed of repetitions of the same syllable, strung together in phrases. A common way to depict the song is via a sonogram, a moving-window Fourier transform that displays energy content as a function of frequency, across time. This two-dimensional plot is a coordinate system not unlike a musical score, in which time is the horizontal axis and frequency the vertical [32]–the main di erence being the homogeneity of the sonogram’s coordinates. See Figure 12. Canaries have several dozen syllables in their repertoire, though particular strains or social groups may use a fraction only. A most prominent syllable in the strain in the lab of our collaborators, is a whistle, a flute-like sound in which the second and higher harmonics have been deliberately muted. (It’s known that it is “deliberate” since letting the canaries breath a mixture of air and helium changes the frequency of their whistles–and makes the second harmonic prominent, so it had been filtered in the first place, see [31].)

Fig. 12. A sonogram. Thanks to Tim Gardner.

3.3 ZENK

The third character in our story is a gene: a transcriptional regulator of the immediate-early gene (IEG) family. It is variously known by incomprehensibly di erent names in di erent species from mice to human: zif268, EGR-1, NGFI-A and krox24. The avian version was named by Claudio Mello by the acronym of the previous names: ZENK. I will refer to it as ZENK henceforth, though everyone calls it by the proper name in their own favorite animals.

ZENK is a transcriptional regulator of the zinc finger family: this means its job is to bind to DNA, using a zinc atom in the joint, and once bound, cause or impede assembly of the transcriptional machinery as described in the previous lecture. (Yes, the Z in ZENK comes from the z in zif268, which is, as you may imagine, “zinc finger 268”... so ultimately from z as in zinc).

In every cell type and tissue where it has been studied, ZENK plays a role mediating plastic changes. The general pattern is that ZENK is not transcribed while conditions are constant, and then upon some sudden change bearing directly on the cell’s function, ZENK is rapidly induced for some period, and then turned o . Osteocytes, (bone cells), which strengthen the bone along stress lines, transcribe ZENK when the bone is subject to perpendicular stresses. Endothelial cells, the inner lining of capillary arteries, transcribe ZENK after an injury–when the vascularization has to be remapped around the wound. Lymphocytes (white blood cells) transcribe it upon their Þrst encounter with an antigen–when the acquisition of immunity is made–but not during subsequent immune responses. ZENK figures prominently among the response factors activated after lesions to the liver and kidney, during muscle formation, etc. And in every tissue type, ZENK is what is called a “passive tumor suppressant”: since ZENK activity signals di erentiation, and tumor formation involves de-evolving into less di erentiated stages, ZENK has to be inactivated in order for tumors to be able to grow.

But nowhere is the role of ZENK as visible as in neurons, for neurons are in continuous and subtle states of di erentiation, and ZENK is one

gateway to visualizing their physiology. When rats are placed in “complex environment” (meaning a really ugly mess of a maze, where they may have to walk a tightrope over a chasm filled with burning coals to get their food– you get the picture), they have to develop complex topographical maps of their environment to avoid electric shocks and the like. These maps are believed to be stored in the hypocampus. Some hypocampal neurons exhibit profuse arborization and sprouting of new synapses and connections during the exploration of the “complex environment”–well, the same neurons express ZENK at the same time. But, most meaningfully, ZENK is the only immediate-early gene which is activated by the long-term potentiation (LTP) protocol. LTP is a long-lasting increase in synaptic strength which follows vigorous stimulation of both the preand post-synaptic neuron simultaneously. Since LTP is the closest thing anyone has found in neuroscience to the famous Hebb rule of neural nets, it is widely believed (mostly on ideological grounds, I should admit) to be the process underlying memory [30]. Briefly after the discovery that the electrical stimulation that induces LTP also induced ZENK (and ZENK alone of all IEGs), Claudio Mello decided that if he could clone the avian homolog of ZENK, play a song to a bird and then stain its brain for the ZENK protein, he might have a standing chance at finding where on earth do canary brains store the memories of song. This story we shall develop in some more detail in the rest of the lecture. Most noteworthy after this work was the tour de force by Sabrina Davis’ group that ZENK induction is required for long-term memory: a controlled ZENK knockout is long-term-memory impaired [41].

ZENK is in the midst of a high-connectivity area of the genetic and enzymatic circuits of the cell, and thus teasing apart its local net of interactions has proved quite di cult. ZENK is an immediate-early response gene: this means it is at the “input layer” for the gene circuit. The pieces that are known are thus: sustained membrane depolarization in the neuron provokes calcium influx, which causes a number of enzymatic pathways to turn on; in particular, a well-studied pathway is via the cre/creb system (calcium response element/CRE binding protein), protein kinase C, and then ZENK induction. There are in excess of 100 putative binding sites for ZENK in the vertebrate genome, so the potential connectivity is huge. An extremely meaningful confirmed link is that ZENK directly promotes the induction of synapsins–the proteins promoting synaptic sprouting and proliferation.

It may seem strange that we know so little and in such vagueness about this gene–even though a MEDLINE search hits thousands of papers mentioning the names ZENK (or zif268 etc.) in their abstract. The problem is that the e ects are so many that they are extremely hard to unravel.

coined the ZENK acronym/name (thus contributing to general confusion and to the ever-growing mess that is the biological naming of genes), and then did the following experiment. He kept two canaries in sound-proof cages. At T = 0 he played several times a song to one of them. He sacrificed them both at T = 1 hour, cut the brains and reacted the tissue with a probe to look for the ZENK gene, comparing the slices to find it di erentially in the animal that had heard the song. He found a very robust induction in a hitherto little studied nucleus of the canary brain, the

caudiomedial neostriatum (NCM).

Some features that are worth noticing. The induction is extremely rapid: statistically significant levels of ZENK transcript are visible within 5 min. The induction is strong: the contrast between the animal kept in silence and that exposed to song is a factor of 12 (in number of visibly labelled cells). In fact, this induction has such high contrast that it can be observed in the wild: Mello, Ribeiro and friends took a tape-recorder to the field station, and played in it a new song, following which they captured a bird, which showed significant amounts of ZENK induction in its NCM. The induction does not require many repetitions: D. Clayton has shown that as little as one exposure to a 2 s song is enough to elicit measurable activation in zebra finches, and full strength is achieved at 3 repetitions [40]. I wish we had known this before our own study.

After induction, the timecourse of ZENK is a typical activationhabituation dynamics; i.e., the levels of gene expression as a function of time show a pulse of gene induction followed by a slower return to zero. The time course for the ZENK protein lags behind the ZENK mRNA, of course: the peak of mRNA induction appears to be 30 min after stimulation, while the peak of ZENK protein seems to lie at about 90 min [38].

After habituation, gene induction is not reinstated by playing the song all over again. No gene induction at all is elicited. But this habituation is song speciÞc: playing a di erent song induces the original timecourse all over again. This stimulus specificity in the habituation dynamics of ZENK was grounds to speculate that ZENK induction might be part of the circuit in charge of consolidating long-term memories and has now been partly validated by Davis’ study.

3.5 Histological analysis

At this stage, Sidarta Ribeiro (in Mello’s lab) and Guillermo Cecchi (in mine) decided that they wanted to use this induction as a means to probe the structuring of representations of complex objects. The idea was that the

robustness of the ZENK signaling made it a better marker than any other histological marker in use (cFOS, jun, etc.), and the specificity to memory consolidation would give us the “support” of a representation, in the sense that one would observe the set of cells involved in the representation of an object. The direct relevance of canary song to canary life and the evolutionary forces that shaped its brain during millennia made the study of canary song as a “complex object” not an idle exercise made for the sake of getting the word “complex” into the abstract, but one with direct implications for physiology.

But standing in the way of all this highly-charged ideology was a minor technical drawback–one that became the centerpiece of our work, of course, as is always the case with biology. The problem was analyzing the gene expression patterns. The way biologists had been doing it usually entailed a camera lucida–a contraption so medieval I shall skip its description. The way we did it entailed writing a lot (about 30 000 lines) of C code, which, while modern, is not that less medieval.

The analysis flow is as follows: after stimulation the bird is quickly (and painlessly) sacrificed, its brain extracted and rapidly frozen in liquid nitrogen. Some time later it is sliced with an ultrasound-driven knife within a cryostat, at −20 ◦C. The 20 µm slices are then dipped in a strong detergent to solubilize all membranes, and reacted with an antibody to the ZENK protein. This reaction is then amplified to generate a macroscopically observable stain by a method known as ICC; this amplification step is not unlike the development reactions used in photography, in that it is monotonic but nonlinear. The other issue is that the reactants are specific but not infinitely so, and then one is left with a tissue sample that is full of histological debris of all sorts.

Since ZENK protein is a transcriptional regulator, it is immediately shuttled to the nucleus of the cell after production, and so the histological stain is confined to the cellular nucleus. Please beware: nucleus is being

used here in two distinct senses, the nucleus of a cell is the organelle where the chromosomes are contained, while a neuronal nucleus like NCM is a collection of neurons which performs some distinct job in a geographically

distinct areaÐone is a part of a cell or neuron, while the other is made of millions of neurons. Because the neuronal nucleus has a distinctive shape, it is possible to write image-recognition algorithms which can pick it up from a high-quality photograph of the tissue–provided this photograph is high-resolution enough, which in our case was about 3 pixels per micron. At this resolution, a 2 mm nucleus like the NCM becomes a 36 megapixel photo–we’ve worked with photos as large as 50 thousand pixels on a side, for marmoset visual cortex. These photographs are constructed by connecting a camera to the microscope and the computer, and then joining to the

computer also an XYZ computer-controlled microscope stage; the photos are then stitched together in memory, a job that’s very easy to do to a low level of quality, but very hard to do at the quality levels we need. Please see in Figure 13 a screenshot of the program we wrote to deal with the analysis issue.

Fig. 13. Snapshot of our program’s main window. See [36].

After the photo is reconstructed, we apply image-recognition algorithms to recognize ZENK-labelled nuclei, extract the coordinates and staining levels, and then align the NCM boundary outlines to a prototypical NCM using a ne transforms. Maps of labelled cell density are constructed for distinct labeling intensities separately. In the search for discriminants that extracted the maximal amounts of information from our datasets, we found that the histogram of labeling intensities was distinctive enough to allow us to discriminate stimulus family on the basis of single slices alone. So an important component of our analysis was discriminating between a case where a few cells were expressing a lot of ZENK as opposed to many cells expressing a little bit of ZENK, and hence we created a representation where we displayed areal densities of cells in di erent ranges of gene expression levels.

3.6 Natural vs. artificial

Fig. 14. Activation for natural vs. artificial stimuli. From [34].

We found that for natural stimuli such as repetitions of recorded canary whistles, gene induction was confined to a somewhat stereotyped band of intensities, almost a boolean phenomenon: a few percent of neurons showed gene induction in this band, while most neurons did not show any measurable expression level. On the other hand, artificial stimuli elicited a broad band of activation: many cells with extremely high level of gene induction, and a whole lot of cells with very faint (but measurable) labeling. Thus, rather than a single well-defined peak, the histogram of gene expression levels becomes broader band. Please notice, in connection with the previous lecture, that this e ect would be completely invisible to a gene-chip analysis as currently performed on solubilized tissue: by averaging over all cells the ability to see the di erent ways in which di erent cells respond to the stimulus a great deal of violence has been done to the underlying biology. However, at 2000 $ a shot one can not expect to run a gene chip per neuron either!

There is a similar “compactness” feature occurring, not in the space of gene expression level, but in real geographical layout. Looking at the whistles it becomes clear that a relatively tight clump of cells is activated,

and that this clump moves up and down the dorsoventral axis of NCM according to the frequency of the whistle. One would normally label this as a tonotopic organization, meaning that there is a geographical organization to the neuronal system according to tone, or physical frequency. However, we then did the following experiment: given that the whistle is almost a pure tone (the second harmonic is more than 20 dB below the main harmonic) we reasoned that, if we were to eliminate all the little irregularities inherent in a natural emission, like the little fluctuations in amplitude envelope or the fluctuations in frequency, then we might get a tighter clump of cells being activated. We then constructed a “synthetic” whistle, made by digitally enveloping a pure sine wave with a parabolic envelope. Instead of getting less cells activated, we got more. We then proceeded to generate a sound that the bird could not conceivably imitate, yet was similarly narrow-band: we filtered a guitar sample narrowly around its main frequency to eliminate higher harmonics. It’s a percussive sound, one that canaries would find most di cult to imitate. The result was even more activation than before: both over a larger geographical range, with a larger amount of cells, as well as a larger spread over gene expression intensities. Composite grayscale panels are shown in Figure 14. Please re-

fer to http://asterion.rockefeller.edu/marcelo/Canario/ for a fullcolor, Java version with all stimuli and sound.

The conclusions are strange. First, clearly the cells in the NCM are not working, as is naively thought in visual neurophysiology, as “feature detectors”. If this were so, we would be hard at ease explaining how a stimulus with a lot less features could elicit so much more activation, in geographic range, gene expression range and sheer numbers. Second, if only in a poetic sense, it seems to be that canaries are not “expanding sound in a Fourier basis”. Whatever it is that they are doing, they are doing it in a “canary basis”. It seems to be the case that the representations are not tonotopically organized: if they were so, there would be an organization according to tone, which is narrowly defined in all three stimuli; the representations seem to be organized according to pitch only for the natural stimuli, with the artificial stimuli largely unorganized.

3.7 The Blush II: gAP

We tend to think of electrical activity as the primary function of neurons. Yet neurons have a complex physiology which goes well in excess of electrical activity alone. One of these physiological aspects is that some kinds of electrical activity, which by means currently ill-understood are judged as “novel”, excite into motion transcriptional programs which ultimately result in long-term changes underlying memory. Let me belabor the point that as we e ortlessly visually and auditorily parse scenes around us, we perceive

distinct objects which we immediately recognize–and there is no re-cognition without a prior cognition and the memory of it. Even in the shortest of timeframes, the fractions of seconds required for a cognitive event, the path travelled by electrical activity in the brain does not propagate over virgin ground, but rather over a landscape that was labored by means of complex transcriptional patterns.

David Clayton has nicknamed the transcriptional programs triggered by various kinds of electrical activity the genomic action potential (gAP), in analogy with the more familiar electric action potential [35]; he describes the latter as integrating over the dendritic arbor inputs the various forms of electrical activity conveyed by synapses, while the former integrates in time, over a much longer timeframe, the changes and adaptations necessary to become memory. Clearly it is no less a neuronal function to adapt than it is to integrate and transmit information–even the dumbest being with neurons has a memory.

3.8 Meditation

Neuroscience is a highly charged, ideologic field. Where else would a choice of preposition denounce an ideological stance? When we were preparing our first manuscript on this subject, my collaborators and I got into a heated argument over the phrase: “the sensory environment is processed by the brain”. The issue was whether to use “by the brain” as opposed to “in the brain”; in the first case, the brain is the active element which takes the initiative, goes out and finds the world and analyzes it; while in the second case, we have the brain as information processor, brain as computer, brain as Shannon communication channel idea.

The topoisomerase collaboration started when John Marko dropped down for a visit and related the problems in identifying a mechanism raised by the Rybenkov study; I have learned a lot from John in the 12 years we have done research together, and hope to continue. Most of the gene chip data analyis and algorithm development described here was carried out by Felix Naef, working on data of our excellent collaborators: Dan Lim and Arturo Alvarez-Buylla, whose work on neurogenesis in the adult mammalian brain started us trying to refine the analysis techniques available, and Nila Patil and Colleen Hacker at Perlegen (formerly the human genetics division of A ymetrix), first through the collaboration on neurogenesis and afterwards on the rheumathoid arthritis data; the Drosophila circadian rythm dataset by the Young lab, etc. The canary work was done in collaboration with Sidarta Ribeiro and Claudio Mello, then at the lab of Fernando Nottebohm, who has been extremely supportive of all of our nonsense; and on our side, most of the work was done by Guillermo Cecchi; I’d also like to acknowledge the work of Pabel Delgado, in charge of the “prosciutto machine”. That work received invaluable ideological support from Roy Crist and the ine able support of Jim Hudspeth. Finally, I would like to warmly thank the organizers of the Les Houches meeting, the colleagues who taught the other courses and lectures, and, particularly, the students at the Les Houches summer school, for an extremely stimulating time.