When analogies go bad

Lots has been written about the scientific method (and even I have written about it in a minor way in the past).  The cycle of “make hypothesis, make predictions, test predictions, revise hypothesis, repeat” is the main thing people focus on when talking about how scientific progress happens.  What’s less talked about is where the hypothesis comes from in the first place, which starts with someone (maybe you, dear reader) noticing something that needs to be explained.  This is harder than it may sound, because in order to see something that needs to be explained, you need to be able to see past the existing explanations.  You need to notice that what the textbook says should happen isn’t quite correct, instead of falling prey to the temptation to edit what you’re seeing to match what you expected to see.  You need, in short, to creatively ignore dogma.  And so it’s always interesting to me to watch what happens as a dogma begins to shift.  The shift may be of earthquake proportions, as when a new way of looking at a problem causes you to doubt everything you ever knew — what people talk about (but far too often) as a paradigm shift.  Or it may be more of an evolution of your understanding — the new idea conflicts with dogma, but there’s no fundamental reason why it should.  Your world view can accommodate the new idea without major changes; it’s just that you didn’t think it had to.

Today’s dynamic dogma has to do with the ribosome, and the shift is of the latter type.  As you all know (and if you don’t, Wikipedia will tell you) the ribosome is responsible for reading the information carried in messenger RNAs and translating it to produce proteins.  It’s a big, complicated protein/RNA complex, and we think of it as both homogeneous and hidebound, a stereotyped machine with a single job: take RNA as input, produce protein as output.  We don’t think about the ribosome as exerting any control over which RNAs get translated — or, at least, we haven’t until quite recently.  But evidence is accumulating that it can. The most recent chapter in this story is some rather dramatic evidence that mutations in a specific ribosomal subunit can cause substantial changes in the vertebrate body plan (Kondrashov et al., 2011 Ribosome-mediated specificity in Hox mRNA translation and vertebrate tissue patterning.  Cell 145 383-397).  It looks to me as if we’re going to have to start thinking about the ribosome as an active participant in the regulation of gene expression.

A window on protein-carbohydrate interactions

It is a truth universally acknowledged that a single new method, especially one relevant to an area that has hitherto been resistant to study, often opens up enormous possibilities for increased understanding.  And yet, new methods often don’t get the publicity they deserve.  Possibly this is because a good new method, like a good new friend, is only really possible to identify in retrospect.  Will your new friend keep in touch when you move away, or is s/he only a good companion in a narrow geographical range?  Will the new method work in the system you’re interested in, or is it restricted to a narrow range of applications?  I don’t know how important the most recent method from Linda Hsieh-Wilson’s lab (Rogers et al. 2011.  Elucidating glycosaminoglycan—protein-protein interactions using carbohydrate microarray and computational approaches.  PNAS  PMID 21628576) is going to turn out to be; but I can tell that it’s a clever approach to a previously intractable problem.

The problem is the question of how to track interactions between proteins and various kinds of glycosaminoglycans (GAGs).  GAGs are polymers of disaccharides in which one of the sugar units carries an amine; they’re usually linked to proteins to make proteoglycans, which make up much of the extracellular matrix.  They’ve been hard to study because they’re structurally diverse — they may be made up of several different sugars, the chain can be over 100 sugar units long, and each sugar group may be modified with sulfate groups at different positions.  Hard to study does not equal uninteresting, however: GAGs regulate all kinds of biological processes, including cell growth, viral invasion, blood coagulation, and the process of recovery (or not) from that cruelest of injuries, spinal cord injury.  GAGs have been shown to help assemble multimeric protein complexes of growth factors such as fibroblast growth factor, helping the growth factor to induce signaling in target cells.  But little is known about how these interactions are controlled, because — until now, perhaps — the specificity of these interactions has been really hard to look at.

What the judge ate for elevenses

Andy Hilfinger pointed me to an interesting recent paper that looks at the effect of a judge’s daily routine on the decisions that he or she makes.  The judges in question are 8 Jewish-Israeli judges who preside over the parole board hearings for 4 major prisons in Israel.  Each judge hears, on average, 20 cases per day; most of the cases are requests for parole to be granted, while others request changes in the terms of their sentence.  Roughly 2/3 of the cases were rejected.  The authors wanted to know whether extraneous factors entered into the decision to allow or deny the request, and particularly whether there was any truth in the old saying that “justice is what the judge had for breakfast”.

They don’t actually look at the composition of the judges’ breakfasts, however.  What they look at instead is the rate of positive decision-making relative to the breaks the judges take to eat.  The regular routine in these courts, apparently, is that the judge takes both a mid-morning snack break and a lunch break.  The judge chooses when the break happens and how long it lasts.  The judge cannot decide when to take the break given the order of the upcoming cases: s/he doesn’t know the order of the cases in advance.

The mysteries of blood

Be honest — would you have guessed that red blood cells are mysterious?  No, I wouldn’t have either.  They’re the simplest cells in our bodies, for goodness sake — they don’t even have DNA.  All they do is carry hemoglobin around, picking up oxygen as they pass the lungs and gradually dumping it everywhere else.  How hard can that be to understand?  And we’ve studied them in various ways for over 450 years.

But indeed it turns out that there are significant holes in our knowledge of how the number, size and hemoglobin concentrations of red blood cells are controlled, and how these control systems go wrong in anemia.  We do know where new red blood cells come from — the bone marrow — and we know some of the factors that control the development and release of new red blood cells, such as erythropoietin.  The feedback control between “too few red cells” and “more erythropoietin needed” goes mainly through the kidneys; the mechanism the kidney uses to sense oxygen levels (protein hydroxylation) and induce erythropoietin synthesis has been an area of active research.  What we know less about is what happens to these new red blood cells once they get out in the circulation.

To clot or not: the trigger for coagulation

Following up on the papers from the Alber lab I wrote about a few weeks ago, John Higgins pointed out this paper (Panteleev et al. 2010.  Task-oriented modular decomposition of biological networks: trigger mechanism in blood coagulation.  Biophys. J. 98 1751-1761), which also aims to use modeling to probe the mechanisms of clot formation.  There’s an interesting contrast here between the different approaches used by the Alber lab and the authors of this paper.  The Alber group embeds their model of the biochemical events of the coagulation cascade in three layers of models of the physics of clot formation: the change in behavior of platelets as they become activated, the shear force of blood flow, and the interactions between the clot and the flowing blood; this allows them to trace the effects of alterations in biochemical events all the way to the predicted behavior of the overall clot.  In this paper, Panteleev et al. focus just on the cascade itself, and ask whether it can be broken down into different sub-parts with distinguishable tasks.  This is a test of what could be a general divide-and-conquer strategy: identify subtasks, identify the components involved in each subtask, and determine which components are changing rapidly and need to be modeled explicitly, and which are changing slowly and can be approximated as “constant” (a.k.a. “separation of timescales”).  If you can do all of this you will end up with a simple(r) model of the key events that drive the behavior you’re interested in, and it might even be simple enough to make you feel that you have an intuitive understanding of what’s going on.

(clotting cascade summary from http://en.wikipedia.org/wiki/File:Coagulation_full.svg )

In setting the stage for their approach, Panteleev et al. point out that the mapping between the biological reactions in the coagulation cascade and the task the cascade performs is not straightforward.  Only two reactions in the network have obvious functions: binding of factor VIIa to tissue factor (TF) is responsible for recognizing the site of damage, and cleavage of fibrinogen to fibrin causes blood to form a gel, blocking the hole resulting from damage and preventing excessive leakage of precious bodily fluids. Why do we need the dozen or so factors that are involved in the whole cascade? That’s a bit of a straw man, of course; the coagulation cascade is much more than a simple on/off switch.  The clotting community, if that’s what they call themselves, have recognized at least 4 subtasks this network needs to accomplish:

A surprising connection between the munchies and obesity

I’m not normally a fan of research that claims to prove that weight gain is anything other than a result of eating too many, and burning too few, calories; it seems too much like wishful thinking.  But I have to say that the story around gut microbiota being involved in obesity has been getting interesting.  The story just took another intriguing turn with a paper that identifies links between gut microbiota and the endocannabinoid system (Muccioli et al. 2010 The endocannabinoid system links gut microbiota to adipogenesis. Mol Syst Biol. 6 392. PMID: 20664638.)

The endocannabinoid system is, as you might guess, the system of receptors that respond to the active principle in cannabis, and the endogenous ligands that activate them.  Endocannabinoid signalling is involved in neurotransmission, memory retrieval, and the control of hunger.  [Yes, this is why smoking pot gives you the munchies, and affects your short-term memory; and no, the title isn’t meant to be sarcastic.]

Friday Feature: The Virtual Fish

Your Friday treat is a movie from Sean Megason’s lab of the development of a zebrafish ear.  Sean has a plan to provide a complete (“in toto”) image set describing the entire development of a vertebrate, using methods described here (Megason SG (2009). In toto imaging of embryogenesis with confocal time-lapse microscopy. Methods in molecular biology (Clifton, N.J.), 546, 317-32 PMID: 19378112).  When the project is complete — which will not be tomorrow — there will be a movie recording every cell division, and every morphological rearrangement, that happens as a zebrafish egg turns into a functioning fish.  And then you will be able to sit at your computer and analyze vertebrate development without needing to get so much as a single finger wet.

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This is from a zebrafish embryo in which both the nucleus (green) and the membrane (red) of every cell has been fluorescently labeled.  If you watch carefully, you can see individual cells divide (one green blob becomes two) and move into new positions, creating (for example) the circle of cells that then opens up into the tube of the inner ear.

Amazing.

Friday Feature: Calling for help

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This amazing movie, from Niethammer P, Grabher C, Look AT, Mitchison TJ. 2009 A tissue-scale gradient of hydrogen peroxide mediates rapid wound detection in zebrafish. Nature 459 996-9 PMCID: PMC2803098, shows leukocytes (the white blobs) rushing to the site of a wound in response to a hydrogen peroxide signal (fluorescence in upper panel).

We’ve known for a while that leukocytes rapidly (within minutes) home to the sites of wounds.  What hasn’t been clear is what signal attracts them.  We’ve also known for a while that hydrogen peroxide is generated in wound sites: but until now, the general belief has been that it comes from the leukocytes that are attracted into the wound.  Hydrogen peroxide has a role in killing bacteria, at least under some conditions, and so this all seemed to make sense.  Until this movie.  You can’t really tell by eye, but the quantitative analysis clearly shows that the hydrogen peroxide production starts before the first leukocyte arrives.  In fact, the timing is such that it seems very plausible that it is the hydrogen peroxide that calls in the leukocytes: soon after the hydrogen peroxide reaches the nearest blood vessel, you start seeing purposeful leukocytes making tracks towards the wound.

Is the hydrogen peroxide gradient we see causal, or is it a byproduct of something else? There are five enzymes in the zebrafish genome that can produce hydrogen peroxide, directly or indirectly (four NADPH oxidases (Nox-1, -2, -4 and -5), and Dual Oxidase, abbreviated Duox).  Niethammer et al. showed that small molecule inhibitors that inhibit all 5 enzymes prevented the hydrogen peroxide gradient from forming in response to a wound, and also strongly reduced leukocyte recruitment to the wound.  Using antisense morpholinos and quantitative PCR, they then narrowed down which of the five possible enzymes could be responsible: none of the Nox enzymes seems to be involved, but knocking out Duox blocked both gradient formation and leukocyte recruitment.  Problem solved: the leukocyte recruitment signal has been discovered!

Well — there may still be more to the story.

Load tolerance and disease

Continuing on the theme of ideas that were ahead of their time, John Higgins pointed out this article from 1962 (Frenster JH, 1962.  Load tolerance as a quantitative estimate of health.  Ann Intern Med. 57 788-94. PMID: 13959579)  Frenster was struggling with the fact that as new technology makes it easier to detect the symptoms of disease earlier and earlier, it becomes less easy to define the point at which disease starts. On the other hand, he was also aware that the same developments offered the tantalizing possibility of viewing disease as a quantitative phenomenon.

His proposal for how to think about this is to define a “unit of physiology” that he calls a body process (today we might say system), which transforms mass, energy or information.  He then asks us to look at a body process as taking an input load and converting it to an output; each process also has “resistances”, both internal and external.  A healthy process can manage its input load easily: in other words, it has additional capacity.  A partly diseased process might be able to keep up with a normal load, but any unusual demand would send it over the edge; and as the disease becomes more serious, you lose the ability even to cope with a normal load.