Monthly Archives: September 2013

A Bird’s Eye View of Cancer Research…

I often get asked what cancer is when people find out I do cancer research for a living and the whys and the wherefores thereof inevitably follow in conversation. The complexities of the disease often mirror the complexities of the bodies they plague and therefore I decided it might be good to get a few things written down that people could be pointed to in an effort to make things a little more lucid and also to serve as a compilation of resources people could delve into if they so desired. So here it is…

Omnis cellula e cellula

All known living organisms are made of cells, in most cases, one cell on its own is an organism, obtaining food from the environment, breathing, growing, multiplying, and carrying out a whole assortment of other life processes that are of interest to biologists, but I digress.

Cancer is fundamentally a disease of organisms that are made of communities of cells – these cells too do all of the above, but some of the more complex varieties of multicellular organisms show specialisation – brains and lungs and guts and genitals – all for the same purposes, to obtain energy, to stay alive and to reproduce; not that there is some grand predisposition to doing this with foresight, merely that those that stumble onto what is passable in the examinations posed by the brutal machinations of nature get to go on.

Starting from one cell,multicellular organisms expand to have several tissues, a whole paraphernalia of different types of cells. Some cells die out, some stay on but don’t divide unless required to make more cells, some double in number until they stumble across a battery of conditions – other cells, other molecules that tell them to stop growing, or those that induce them to multiply in a frenzy but then stop when balance has been restored.

This behaviour of cells, and the organs they form, and then the organ systems that they comprise, and the organisms themselves, emerge from interactions of the environment, both consisting of organisms and other things that appear prosaic but are nonetheless significant influences on the fates of organisms with the delightfully messy workings of the molecules within cells – from the DNA that contains all the genetic material of a cell that simply must be passed on from generation to generation to facilitate survival of the species.

Dawkins in his magnum opus; “The Selfish Gene”, popularised the notion of organisms serving as mere means for the continued survival of genes, but I shall go one step further and put it to you it isn’t just individual genes that are selfish, it is entire genomes (a collection of all the DNA in a cell/organism). Promiscuity is favoured by nature when rivals are less promiscuous and when it is possible to brutally stifle threats posed by the competition, and cancer essentially is this being taken to a horrifying extreme when genomes find ways to be malignantly selfish at the expenses of the other cells that are also integral to the survival of the organism, bringing with it much suffering and often, death…for alas! Cancer cells lack the foresight to know that their fate is tied inextricably with that of their hosts.

Of DNA, RNA and proteins…

Genomes are made of DNA, and this is the medium by which information gets passed on from generation to generation except in the case of a few viruses, but we don’t really consider viruses to be living things because they cannot reproduce on their own. But in order to help them do this, cells orchestrate a variety of biochemical functions through the medium of RNA that is transcribed from DNA, which by itself can affect other RNA or for some genes gets turned into proteins. RNA, proteins and DNA then interact with the outside environment, other DNA and proteins to give rise to the chemistry that leads to the formation of cells and organisms. This complexity is described elsewhere on the blog [1] [2] [3]  and I will add links and notes at the end so you’ll be able to explore further if it interests you.

On the road towards understanding cancer…we discover cancer causing genes have cellular origins. 

Towards the dusk of the first decade of the 1900s, Peyton Rous made a discovery that would shape perspectives towards cancer research for a long long time to come – he found that cancers in chicken could be transmitted akin to other known viral diseases – and the causative agent he isolated came to be known as Rous Sarcoma Virus (RSV). This immediately led people to believe that cancer was essentially a viral disease, until Michael Bishop and Harold Varmus made a revolutionary discovery – that the genes that caused cancer had a cellular origin – at some point, the virus had by sheer accident incorporated this gene while it was packaging itself up, and had no problems transmitting it because it could spread before the chickens died of cancer.

They got their hands on two strains of RSV, one with cancer causing properties, and one without, and consequently one could identify the viral gene that caused cancer as the one that was present in the former but not the latter. They made a probe of a molecule called RNA, which I shall describe later, to look for similar genes in cells, and then they found that it bound to the DNA of cells from different species, and in every species it happened to be found in the same place in the genomes of the cells (genomes are made of DNA); that gene that had been rampaging through chickens when transmitted through the virus was also found in normal cells, and when switched on in very high levels due to lots of replicating viruses, caused cells to lose control of how they grew and to take part in a frenetic orgy of cellular division, it was, in effect, an oncogene.

Then they looked elsewhere for genes with similar properties and began to identify more and more, which in cancer cells had defects in DNA that affected the function of the proteins they produced and consequently acted to launch the cells into rapidly increasing their cell numbers. On the other hand, people began to find proteins which, when altered, lost the ability to stop cells from dividing uncontrolled, these genes came to be known as tumour suppressors [4] and one of the breakthroughs in learning about the function of these genes came was the elucidation of Knudson’s “Two-hit” hypothesis…

Knudson’s two hit theory of cancer causation came about after the discovery of tumour suppressors, specifically a gene called Rb1.

Looking at genes implicated as either oncogenes or tumour suppressors people began to stumble across changes in DNA sequence compared to the sequences in normal cells that affected the proteins the genes made. These mutations, as we describe them, established the foundations of cancer genetics and genomics.

The hallmarks of cancer.

As people found more and more oncogenes and tumour suppressors they wondered what cancer was, for here was a set of diseases stemming from various tissue types that all appeared to consist of cells that grew rapidly and in many cases spread through the body, albeit at different rates. A seminal paper by Hanahan and Weinberg defined the hallmarks of cancer – traits that any disease that qualifies as a cancer *must* possess…

The Hallmarks of Cancer and examples of potential therapeutic methods to target them. From Hanahan and Weinberg’s seminal paper ‘The Hallmarks of Cancer: The Next Generation’, link in references.

These include the ability to multiply abnormally without requiring external signals, and if external signals that stop normal cells from dividing are present, not pay heed to them, the failure to undergo apoptosis (a form of cell death), immortalisation, which is the ability to divide indefinitely in permissive conditions unlike normal cells, angiogenesis, where tumours induce the formation of blood vessels so they can establish a bloody supply and finally, and most critically, metastasis; the ability to spread through the body and colonise other sites in the body, which is incidentally what is thought to kill patients. 
There was a recent update to the classical set of hallmarks described about and three new hallmarks entered the fray – altered cell metabolism; changes in how cancer cells generate energy, inflammation; a molecular response to wounds and injuries in normal cells that goes wrong and promotes cancer metastasis and genome instability – being prone to mutations and other structural aberrations that generate the complexities of cancer genomes, which I describe later [5]…

More than just mutations, and how we came to find out…

People who had been studying families of proteins called transcription factors noticed that they could fundamentally alter the way the RNA of different genes in the genome was produced – they could alter when they were produced, and how much was produced. This could then affect other proteins that controlled how cells divided and interacted with the environment, in some cases, transcription factors were found to be mutated, such as p53, which is also known as the guardian of the cell because of its critical role in stopping errant cells from progressing to cancer [6], which explains why so many tumours modify p53 function so they can get round it,and with this came the idea that cancers would exhibit differences in gene expression relative to normal tissue and this would then contribute to the achievement of the hallmarks of cancer. See [7] for a description of microarrays and case studies of how looking at expression profiles helped understand cancers.

People also realised that you could get changes in expression patterns independent of transcription factors… Cancer cells are host to a wide variety of large, structural, distortions of the genome, and compared to normal cells, which have 46 chromosomes, cancer cells accumulate a variety of aberrations, ranging from small deletions and duplications of bits of chromosomes to gains and losses of whole chromosomes, or in some cases whole sets of chromosomes (The ubiquitously used cancer cell line, HeLa, has 88 chromosomes).

Changes in copy number of genes through these aberrations could also have effects on gene expression profiles. Finally, people who’d been studying epigenetic processes, which involve cells inheriting expression patterns for instance and then modifying them through modifications of DNA without changes in sequence, such as DNA methylation and Hydroxymethylation or the histones around which DNA is wound [8], and began to develop techniques to characterise epigenetic changes in tumours, and we therefore ended up in a situation where we had a whole panel of analyses we could do on tumours.

The Cancer Genome Atlas

While several other tumour sequencing projects were underway at the likes of the Wellcome Trust Sanger Institute, The Cancer Genome Atlas really set things going ahead with their project on the deadly brain cancer; Glioblastoma Multiforme, with sequencing for mutations, microarray analysis for Copy Number Variation and gene expression and microarray analysis for DNA methylation. They essentially found that there are four groups of glioblastomas based on patterns in gene expression and were able to correlate these with different cellular origins and different ways in which those expression profiles were achieved [9].

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Heatmap from one of the first TCGA papers that profiled glioblastoma. Four subtypes of glioma were described based on expression profiles. They were able to classify samples in an independent dataset and also versions of glioblastomas grown in mice (technically called xenografts).

They also found a subset of tumours that had very high levels of methylation driven by mutations in a gene called IDH1, which leads to too much methylation as a direct consequence, as demonstrated in a landmark paper in the journal Nature, where they put in mutant IDH1 into astrocytes and showed it induced high methylation levels like those seen in gliomas of that type…

Since then, the TCGA has published multiple studies on breast cancer, ovarian cancer, renal clear cell carcinoma,lung adenocarcinoma and colorectal cancer to name a few [10] and is collecting data for more tumour types, and from 12 datasets so far a pan-cancer analysis was released recently [11]. This has inspired the formation of the even more ambitious International Cancer Genome Sequencing Consortium which aims to widely expand the scope and the size of the type of approaches taken by the TCGA to profile the most striking molecular features of tumours and to then relate them to clinical information.

Things are not quite so simple – the problem of heterogeneity.

One would think that by understanding the make-up of tumours and figuring out what drives them, it would be easy to target altered genes, proteins and pathways with specific drugs to achieve cures, however, tumours have such unstable genomes and often contain so many cells by the time they’re detected that they are capable of a great degree of evolution, which may become reflected in resistance to the drugs used to target them. Indeed, studies starting two years ago began to show two features of tumours, firstly; they evolved through time, and therapy often had an influence on which dominant properties were seen in a patient’s disease as they relapsed. They either saw that the most dominant clone before treatment acquired new mutations and then evolved to resist therapy or a previously minor clone expanded.

At around the same time, evidence was found to strongly support the notion that cancers not only evolved in a linear manner but could evolve in parallel. Both those studies were carried out on leukaemias and the same was shown to be true of solid tumours. An analysis of a kidney tumour looking at multiple regions of the primary tumour and metastases (new outgrowths of the tumour derived from cells that had spread from the primary tumour) highlighted branched evolution and also observed that different parts of the primary tumour showed different patterns of gene expression associated with  survival [12].

Intratumour heterogeneity is extensive in kidney cancer and sequencing multiple biopsies enabled reconstruction of evolutionary patterns.

A recent study looking at multiple regions from a series of glioblastomas also found the same striking pattern; there was evidence that all four of the glioma expression subtypes discovered by the TCGA were found in that tumour [13]. These studies have made one thing abundantly clear; that understanding and classifying tumours into subgroups may be of limited utility when the range of evolutionary invention achieved by tumours permits them to acquire different patterns of alterations for the most part in response to therapy. We will learn a lot, indubitably, from large scale analyses of the kinds already being carried out, and only recently we began to uncover what processes might contribute to the formation of mutations and how to find signatures for mutations from all that data being generated by sequencing tumour after tumour after tumour, so chances are we will have a comprehensive collection of molecular profiles to tuck into, soon, on an unprecedented scale. 


Finding chinks and causes for optimism…

Another way weaknesses might be found in tumours involves approaches based on what we call synthetic lethality and collateral lethality. Synthetic lethality is when, because a gene is mutated or altered otherwise, another gene becomes essential while it was not essential if the other gene was intact. A classic example of this is PARP inhibition. PARP is an enzyme that repairs breaks in DNA, but can be dispensed with if the BRCA genes are intact. A significant proportion of Ovarian and Breast cancers, especially those that run in families, show a characteristic loss of BRCA1 or BRCA2, and this makes them especially vulnerable to the blockade of PARP.

Explanation of synthetic lethality to PARP inhibitors. People with one copy of BRCA lost in normal cells can present with tumours that have lost both. Normal cells have BRCA to compensate for the loss of the PARP gene but cancer cells don’t, and blocking PARP can kill them while sparing normal cells as a consequence.

One way of finding synthetic lethal interactions is to combine knockdown experiments, where little RNA sequences are introduced into cells to block and degrade the RNA of target genes and not permit protein to be formed from that RNA and combining that information with mutation, expression and other “-omics” data as we call them. Even without -omics data in attendance, knockdown experiments themselves can reveal certain genes that come to be required specifically in cancer, and in that way we can identify targets for chemists to then develop specific drugs against. Of course, the other approach would be to target things that appear to transcend cancers, and examples include targeting a protein called CD44 that cancer cells appear to universally express to avoid being destroyed by the immune system or to use drugs that target fundamentally common features of tumours such as DNA methylation [14].

Finally, knowledge of tumour evolution itself may be employed to find weaknesses, as described elsewhere on the blog, the mechanism of resistance may by itself predispose tumours to weaknesses, and this could be as simple as withdrawing the drug (letting go of the brakes suddenly when the driver’s still got the foot on the pedal to compensate for jammed brakes till that point), as discussed here [15].

Dealing with heterogeneity may be rendered possible by bypassing resistance mechanisms where cancers find alternate pathways to get to where they need to be to survive and expand by hitting points that are altered in cancer, but have no known alternatives for tumours to route their functioning through. Indeed, this has been shown experimentally by targeting Myc, which when activated is a potent oncogene or by targeting BIM in glioblastoma where tumour cells evolve resistance by finding other ways to prevent BIM from being turned on when the pathway they usually use to do this is blocked with drugs.

For all the complexities of cancer, there might still be ways in which we will figure out how to target and attack them successfully, and one of the keys to that I think the sense of scale and community that cancer research projects these days are marked by. I think the enduring impact of the Human Genome Project [16] was not just the sequencing of the human genome, but ensuring that data was openly accessible to anybody who wanted to use it for their research or look at it for general interest; the TCGA and ICGC have put in place similar policies to govern how their data is accessed, and by allowing researchers to integrate the research they do locally with data that wouldn’t have been generated without big projects like them it is possible to achieve so much more. And maybe we’ll figure out what cancer is, and then determine what they can and can’t be, someday, soon… 
References!

Links to posts on the workings of DNA, RNA and proteins. [1] https://exploreable.wordpress.com/2011/02/19/the-central-dogma-of-molecular-biology/  (Central Dogma of Molecular Biology)

[2] https://exploreable.wordpress.com/2011/05/16/from-dna-to-rna-the-process-of-transcription/ (Transcription)

[3] https://exploreable.wordpress.com/2011/01/14/right-just-as-promised-here-comes-rna-interference/ (Includes descriptions of microRNAs, RNA species that can block other RNAs from being converted to protein as per the central dogma, thereby affecting gene expression).

Oncogenes, Tumour Suppressors and the Hallmarks of Cancer
[4] https://exploreable.wordpress.com/2011/10/05/oncological-complications-models-surrounding-tumour-suppression/ (Contains an exposition of the two-hit theory of cancer causation and links to further material on the topic, as well as nuance about how some tumour suppressors behave differently)

[5] http://www.cell.com/retrieve/pii/S0092867411001279 (Updated version of the classic paper; The Hallmarks of Cancer by Hanahan and Weinberg. May be paywalled).

[6] https://exploreable.wordpress.com/2011/11/03/the-p53-barcode-a-brief-introduction/ (Explains how p53 functions in different contexts, basically)

Understanding Cancers and large scale analyses
[7] http://www.nature.com/scitable/topicpage/genetic-diagnosis-dna-microarrays-and-cancer-1017 (Nature Scitable article on gene expression and cancer).

[8] https://exploreable.wordpress.com/2012/01/22/an-introduction-to-epigenetics/ (An introduction to epigenetic processes).

[9] http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2818769/ (The TCGA glioblastoma paper that documented four expression subtypes).

[10] https://tcga-data.nci.nih.gov/docs/publications/ (a list of papers from The Cancer Genome Atlas, most papers are openly accessible and readable should you want to, but they’re science heavy and really written for people with an in-depth understanding of cancer research. You may be able to search for materials and commentary related to them to get a more popular perspective, also look for TCGA press releases on the same site).

[11] http://blogs.nature.com/freeassociation/2013/09/focus-tcga-pan-cancer.html (Blogpost on the Nature blogging network containing links to further material, commentary and analysis papers from the TCGA pan-cancer project.)

Heterogeneity, synthetic lethality and the future

[12] https://exploreable.wordpress.com/2012/09/22/a-very-short-introduction-to-intratumour-heterogeneity/ (Blogpost on intratumour heterogeneity)

[13] http://www.pnas.org/content/110/10/4009.full (Paper documenting intratumour heterogeneity in glioblastoma multiforme)

[14] https://exploreable.wordpress.com/2013/02/24/more-is-not-always-merrier-methylation-version/ (Blogpost discussing broad spectrum effects of low doses of the DNA methylation blocker decitabine) .

[15] https://exploreable.wordpress.com/2013/06/21/a-window-into-acquired-resistance-to-targeted-therapies-through-the-eyes-of-a-mek-inhibitor/ (Blogpost exploring reports of how cancer cells evolved resistance to a drug and how this could be used to target the tumour).

[16] https://exploreable.wordpress.com/2011/05/03/the-story-of-the-human-genome-project-a-short-narration/ (Long blogpost on the Human Genome Project, written by yours truly and therefore recommended if you are a glutton for a few thousand words more having worked your way through to the end of the article).

That’s all from me now!

Cheers,
Exploreable

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Building Brains: Symmetry, Synapses, and Shakepeare

This column will be about the brain, the gooey three pound jelly-like substance inside our skulls. Appearances can be deceptive, for this is quite possibly one of the most complex structures you will ever come across. All our memories, our knowledge, our hopes, dreams, aspirations, our beliefs, our likings, dislikings, our passions, our love, our hatred, almost everything that make us who we are are but activities in this lump of jelly. The billions of cells and trillions of connections that make up this structure are buzzing with activity throughout our lives. In fact, their activities are manifested as what we call Life. As one of my favourite bloggers put it, “”All life is here” in those tangled little fibres.”[1]. To understand life, we must understand the brain. In this column, I will try to pick up one fascinating story from neuroscience research every month and I will try and elaborate on it. My primary aim will be to present the research in a broader context and explain in the process why this matters in the bigger scheme of things, how the research is going to be useful in expanding our knowledge about how the brain (and, in a way, the universe) works. I will try and convey the sense of wonder and beauty that drives most of scientific research and I hope they will be contagious enough to inspire the reader to pursue science, if not as a career then at least as more than a passing interest.

 

This article is about how brains are built. The science that studies it is called Developmental Neurobiology and it is one of the most popular and active disciplines right now. Thousands of peer-reviewed articles are published each year[2]. So you can guess that it is impossible to give a flavour of the entire discipline in this one article. But every single discovery, every single article is actually fascinating in its own way and as is always the case in science, intricately intertwined with the bigger tapestry of our understanding. In this article, we will focus on one single aspect of neuroembryology (that’s another name of this subject) called Neurogenesis. As I mentioned in the introduction, I’ll try to pass on the enthusiasm that makes Developmental Neurobiology my favourite subject!

All of life starts from a single cell. From the humblest of unicellular creatures to the gigantic blue whale, each one of us started our lives as a single cell. So how is it that the single cell could divide itself in such an orchestrated manner to give rise to something so wonderful and complex? Shouldn’t all the daughter cells arising from that be identical copies of each other? How is this information regarding the fate of individual daughter cells transmitted? Turns out that the answer lies in asymmetry. If all the cell divisions, distribution of intracellular products and the extracellular environmental parameters were symmetrical, there would be absolutely no way to differentiate between the daughter cells. Symmetry has a lower information content than asymmetry. Once you develop a gradient of asymmetry, you can work on it and amplify it to regulate the flow of information in a very specific way. The initial bootstrapping through asymmetry is thus key to all of life.

 

Most of the studies in this field have been done in fruit flies, transparent nematode worms and vertebrates like xenopus and zebra-fish. But remarkably, the basic developmental processes in these organisms seem to be highly conserved throughout evolution. We have homologues of most of fundamental processes found in these simple creatures in the higher vertebrates. So although I will chiefly be discussing Drosophila research today, the conclusions we draw can be useful in understanding how the human nervous system is formed.

 

The initial asymmetry in Drosophila embryogenesis is established even before the fertilisation of the oocyte (the fruit-fly equivalent of egg) occurs. The nurse cells that surround the egg secrete substances that are imbibed by the oocyte and asymmetrically distributed in it. Among these substances are genes called bicoid (bcd) and oskar (osk) that establish the antero-posterior axis through their concentration gradients. Other genes called dorsal, cactus and toll establish the dorso-ventral polarity. Now after fertilisation the first genes that are expressed in the zygote (members of a class called the gap genes) arrange themselves in a pattern along this pre-established antero-posterior axis. We have the start of asymmetric life with coded spatial patterns. This anteroposterior patterning is then further reinforced by the expression of pair-rule genes, Hox genes and segment-polarity genes.

 

The development of the nervous system proper starts much later in the life cycle of an organism. But the same basic principle of asymmetric cell division plays a pivotal role in there as well.

 

The majority of functions of the nervous system are controlled at the most basic level by highly specialized cells called Neurons. Now, in order to generate the enormous diversity of function and connectivity in the nervous system, it is imperative that each neuron must be specialized to carry out a specific task. As a result, the neurons show tremendous variety in cellular structure, physiological functions, chemical properties and connectivity. Even the cells from a single region in the brain vary from each other in different aspects. For example, the granule cells of the cerebellum (the part of the brain that plays an important role in motor control) vary significantly in morphology and chemistry from the Purkinje cells of the same region. Similarly, the motor neurons, despite their structural and chemical similarities, have different molecular attributes that make them connect with specific muscle fibers resulting in the precision of movements that we see. This process of developing highly specialized neurons from comparatively similar precursor cells is called Neurogenesis. Question is, how is it done? What determines the fates of neurons?

Sydney Brenner once jokingly said that neurons are either European or American. The fate of an European neuron is mostly determined by its family lineage whereas that of an American neuron is largely shaped by the surroundings in which it grows up! As a general rule it is often said invertebrate neurons mostly belong to the former class whereas the ones in vertebrates fall in the later (any inference drawn herefrom is purely coincidental!). However, a close inspection tells us that the situation is not as binary as it looks. A cell is projected into a definite developmental trajectory by the environmental conditions that it is subjected to. So by default its daughter cells are only allowed to maneuver within that trajectory but with an enormous amount of variability (both reversible and irreversible) provided by the environment that they are now subjected to. More technically, this is controlled by two main processes called spatial patterning and temporal regulation of birth-dates. That is, the spatially coded patterns that surround a cell and the time of the final division that gives rise to it are most pivotal in determining its fate. These result in the cells expressing different transcription factors (regulatory proteins that cause expression of different genes) which ultimately determine their fates.

Spatial control of cell fate determination is all about making the right neurons at the right place. As we saw earlier, the Drosophila embryo is finely subdivided in the anterior to posterior axis into stripes of expressions of gap genes, pair-rule genes, Hox genes and segment-polarity genes. Now the neuroblasts (cells that eventually give rise to the neurons and all other classes of cells in the nervous system) are provided with intrinsic positional information by these genes. These anterio-posterior positional identity genes play important role in determining the identity of the neuroblasts.

 

The first of these spatially coded domains are initially specified by a gradient of the signaling molecule Sonic hedgehog (Shh). Now there’s an interesting story behind the nomenclature of this molecule. For those of you who were avid video-game fans in the 1990’s, must remember Sonic, the hedgehog. The gene was indeed named after the same character. The hedgehog genes were initially called so because mutations in them caused bristled appearances of the Drosophila larvae. The custom was to name the genes after different species of hedgehogs but when this gene was discovered, one of the postdocs in Clifford Tabin’s lab requested to name it after the popular Sega video-game character. Some had reservations about it but the naming was carried out anyway. There are criticisms for naming it thus, as mutations in the gene (actually, its homologue in humans) causes a serious condition known as holoprosencephaly. It seems rather cruel to name a gene after a video game character when mutations of it can cause such devastating conditions in children. But we are digressing from the main story. For a lively discussion of this through models and live demonstrations, watch the Howard Hughes Medical Institute Lecture on the basics of neuroembryology.

 

The progenitors of the neural tube are highly sensitive to the concentration of Shh, and this results in the graded expression of a group of transcription factors. We have a cascade of transcription factors being expressed in a directional manner being started by the initial asymmetry in concentration gradient like we mentioned earlier. Similarly there are other set of genes that divide the embryo and the nervous system along the dorsoventral axis. So you can imagine a sort of grid system being established where a neuroblast in any position can be uniquely identified by expression of these spatial coordinate markers of latitude and longitude. Genes specifying positional information along these two axes confer a positional identity to each of the neuroblasts in the developing nervous system. Once thus being expressed in a neuroblast, the spatial coordinate genes are inherited by all its progenies and they bear the indelible stamp of their birthplaces.

Having thus acquired their positional identities, each neuroblast divides asymmetrically to produce a copy of itself and a cell called the Ganglion Mother Cell (GMC). The neurobalst then goes on to divide further to give rise to a set of GMCs. But the really cool part is that each GMC can be identified not only by the positional information that it inherits from the neuroblast, but also from the order of its generation (that is, whether it was the first, second, or third GMC to be arising from a neuroblast). This temporal coding of information is carried out through a program of transcription factor expression. When the first GMCs are generated, the neuroblasts express a transcription factor called hunchback (hb). Later, they turn off the expression of this gene and turn on another one called Krueppel (kr). GMCs formed at the respective stages thus acquire these transcription factors. The expression of these transcription factors are linked to the cell cycle which functions as a kind of clock. Thus, in addition to the information about the place of their origins, the GMCs also carry with them information about the time of their birth. But it gets more interesting from here.

 

Typically, the progeny of a neuroblast inherits the temporal and spatial coordinates expressed by the parent at its time of birth. However, often the parent divides asymmetrically to give the intrinsic determinants to one of the daughter and not the other. How does a cell accomplish this feat of partitioning information asymmetrically among the daughter cells? The answer, it turns out, has a touch of Shakespeare to it!

 

Two factors, Numb and Prospero (Pro) play a pivotal role in the asymmetric distribution of determinants of cell identity. During the time of division, these tow factors are concentrated in the smaller daughter cell, the GMC, where Prospero enters the nucleus and determine the fate of the GMC. Numb, on the other hand, blocks a signaling pathway that renders the GMC free to move down the determination pathway. But how do Numb and Prospero get asymmetrically distributed in the cell in the first place? Two proteins called Inscuteable (Insc) and Bazooka (Baz) form a complex known as the Insc complex which binds to the apical membrane of the neuroblast and this complex causes the mitotic spindle (that’s like the apparatus that pulls different substances into the daughter cells during cell division) to be arranged vertically. In conjunction with the actin-based cytoskeleton (that’s like the transporter system of cells!) mechanism, this complex drives the distribution of several proteins along the vertical axis so that they are asymmetrically distributed. In particular, a cytoplasmic protein called Miranda is enriched at the basal neuroblast pole and it binds with Numb and Prospero to result in their asymmetric distribution. Wait a second! Miranda binds Prospero? What’s going on in here?

 

When I first read about these proteins, I was pleasantly surprised to find the Shakespeare reference. What on earth is Tempest doing in the middle of a Developmental Genetics textbook? I mailed  Prof. Chris Doe, the guy behind this fascinating nomenclature and asked him about the inspiration behind it. He said something wonderful, “You are right, they are from the Tempest:  Prospero the magician = the controller of fates!”[3] In a befitting tribute to The Bard, we have named the determiner of the fate of a neuroblast, which in a way is key to determining the fate of most of life, after the magician in The Tempest.

 

We are nearly at the end of our story. We have seen how information is passed on to the developing nervous system in a wonderfully coordinated manner. We have seen how neurons that will eventually define who we are become who they are. We have seen how their fates are determined by their origins and the environment they grow up in. We have seen temporal and spatial patterns giving rise to diversity in the nervous system. We have glimpsed into the most magnificent process in the universe where the most complicated computational device in the whole universe is being formed through self-assembly.

 

It has often been asked, what is the utility of studying this? We have our defences ready and we say that an understanding of neural cell fate determination will be important in understanding and treatment of neural diseases and injuries. In the future, we might be able to develop molecular therapies to repair damages in the nervous system. We might even be able to develop specific neurons from stem cells to use them in transplantation therapy.  More importantly, an understanding of this will be able to help us understand neurodevelopmental disorders like the autistic spectrum disorders better.

 

But I think there is a better reason to do all these. Science is in her uninhibited best when she is curiosity-driven. What better reason to pursue a career in research than to be able to name an all-powerful gene after the name of your favourite literary (or video-game) character? What better thing to study than the making of the mind? What better way to understand life than to watch it being formed? We don’t always to need to justify our passions in an utilitarian framework. Science is our most reliable probe into the nature of reality and pursuing it is, in my opinion, one of the best ways to spend the brief amount of time we spend on this planet.

 

Hope you will like this column. More interesting stories about the three pound jelly next month. Till then, have a great time!

References:

 

1. From Neuroskeptic’s wonderful blogpost on the Morgellon’s disease: http://neuroskeptic.blogspot.com/2011/02/web-of-morgellons.html

 

2. About 25,000 articles were published on Developmental Neurobiology in the years between 2000 and 2004 (source: Development of the Nervous System by Sanes and Reh). We can only expect the number to be much higher than that in the last five years or so.

 

3. From the same email conversation with Prof. Doe, I also learnt that now there is a Caliban as well (PMID: 16103875). Shakespearem, it seems, is quite popular among developmental biologists!

 

Most of the other information in the article are from the following books:

 

Principles of Developmetal Genetics (edited by Sally A. Moody)

Development of the Nervous System (by Sanes and Reh)

Developmental Neurobiology (by Rao and Jacobson)

Principles of Neural Development (by Purves and Lichtman) (This book is currently out of print but you can download a copy for free from Purves’ website here)