Harvard Medical School, Boston, Massachusetts

A signalling scientist marvels at perfect patterns

The formation of patterns during animal development depends to a great extent on cells, or groups of cells, sending a specific signal that activates a cascade of reactions in the cells that receive and respond to it. Studies of this process in the fruitfly Drosophila have provided many insights into the nature of the molecules involved and the mechanisms underlying cell–cell signalling.

The cell surfaces of almost all animals are decorated extensively with large molecules known as heparan sulphate proteoglycans (HSPGs). These modulate most developmental signalling pathways and comprise protein cores modified by the addition of long carbohydrate chains called glycosaminoglycans (GAGs). GAGs are key to mediating interactions between HSPGs and the molecules that they bind.

Recently, Rahul Warrior at the University of California, Irvine, and his colleagues (Development 135, 1039–1047; 2008) explained the puzzling observation that although HSPGs are required for signalling by the protein BMP in certain tissues, they are not required for BMP signalling during very early fly development.

The authors demonstrate that GAG synthesis does not occur in early embryos because the messenger RNAs that encode two enzymes involved in its construction are not translated.Preventing GAG synthesis at this stage allows an 'activity gradient' of BMP to be generated across the embryo that patterns the dorso–ventral axis of the fly. A few hours later, the GAG enzymes are produced, allowing the modified HSPGs to participate in other signalling pathways.

This study illustrates how temporal control of the synthesis of a ubiquitous set of enzymes is used to modulate the activity of signalling pathways in different tissues.

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Howard Hughes Medical Institute, Stanford University School of Medicine, California

A developmental biologist muses on the magic of the egg.

Many biologists, myself included, grew up watching frogs’ eggs hatch into tadpoles at the warm surfaces of summer ponds. The yearly cycle provided a leisurely period of thought about basic biology. But few of us guessed how central to current biological and financial interests the egg would become. These days, an enucleated egg’s ability to reprogram the nucleus of a somatic cell — first demonstrated in frogs’ eggs in 1958 — promises an era in which organs could be picked up like junkyard parts.

What magic does the egg possess that allows it to reset the nucleus to a basal, or ‘pluripotent’, state from which all cells can be generated? The three famous transcription factors — Oct4, Sox2 and Klf4 — that are required to transform a skin cell into a pluripotent cell provide some insight. But do these recapitulate a pattern used by the egg during development, or induce reprogramming by an alternative pathway?

John Gurdon and his colleagues at the Gurdon Institute in Cambridge, UK, have purified the proteins that bind to the regulatory sequences of the Oct4 gene in frogs’ eggs (M. J. Koziol et al. Curr. Biol. 17, 801–807; 2007). The group chose Oct4 because its regulatory regions have been clearly defined. They found that the initiation of Oct4 expression involved, in addition to likely candidates, some unexpected proteins.

If, as many scientists think is the case, the re-establishment of pluripotency involves shortcircuiting egg development, this suggests to me that the magic that allows the egg to reset a nucleus into a pluripotent state may lie in these unexpected proteins — as well as Oct4, Sox2 and Klf4. There is so much more to learn from watching frogs’ eggs grow up.

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Max-Planck Institute for Dynamics and Self-Organization, Göttingen, Germany

A physicist enthuses about criticality in biological development.

Physicists often overestimate the impact of their work on biological research. A biologist recently joked to me that physicists are rather like consultants: they appear without being asked and don't tell you anything new. As a physicist studying the spread of infectious diseases, I reckon there is some truth in this.

But biologists can underestimate our insights, too. The joke turned my mind to a paper by three physicists who applied the theory around spontaneous symmetry breaking to the development of body axes (J. Soriano et al. Phys. Rev. Lett. 97, 258102; 2006).

Spontaneous symmetry breaking occurs in, for example, a cooling magnetic material. At high temperatures, magnetic spins are randomly arranged, but as the material cools patches form in which the spins are aligned. At a critical temperature, the spins align throughout the material. A small, external magnetic field can then determine the system's fate, setting all the spins in a particular direction.

Soriano and his team studied symmetry breaking in developing hydra — multicellular organisms with clearly defined head and foot ends. Hydra can establish their body axis from a jumbled ball of cells, reminiscent of the way a magnetic material orders its spins as it cools. Patches of cells develop similar gene-expression profiles. This creates a system that is critically sensitive to tiny temperature gradients, which determine the direction of the body axis.

Impressively, Soriano and his team worked out the exponent in the size distribution of cell patches expressing a particular gene as a function of the age of the developing hydra. Through this, they related axis development to other self-organized critical systems physicists study, such as forest fires.

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Stanford University School of Medicine, California, USA

A systems biologist encourages modelling by the millions.

In a typical modelling study, we write down equations, solve them, and see whether they account for known data. If they do, we claim to understand some bit of biology. One huge caveat is that many other models might have matched the data just as well.

Researchers from Peking University in Beijing and the University of California, San Francisco, have devised a satisfying way of dealing with this problem (W. Ma et al. Mol. Syst. Biol. 2, 70; 2006).

Their starting point was epithelial patterning in the fruitfly Drosophila. During embryogenesis, a system known as the 'segment polarity network' generates repeating stripes of gene expression. The stripes are initially fuzzy and later become sharp. Ma et al. set out to see what simple gene circuits were best suited to this sharpening process.

They formulated differential-equation models for about 14 million ways of connecting two or three segmentation genes, then randomly chose 100 sets of parameters that defined the strength of the interactions for each gene. They then carried out computations for each combination to determine which of them converted fuzzy stripes into sharp ones.

Many topologies worked for at least one parameter set. But only a fraction worked for more than one or two. Interestingly, the most robust topologies were all variations on the same design — each had three sub-circuits, one 'stripe generator' motif and two bistable 'response sharpeners'. These findings give hope that complex networks may be decomposed into modular sub-circuits with understandable functions.

Comprehensively examining millions of models is a lot of work, but is not impossible. And, as Ma et al. show, it can yield important insight that could not have been derived from studies of one or two.

Posted by Brendan Maher at 07:54 PM | Permalink | Comments (1) | TrackBacks (0)

Stanford University School of Medicine, California, USA

A systems biologist encourages modelling by the millions.

In a typical modelling study, we write down equations, solve them, and see whether they account for known data. If they do, we claim to understand some bit of biology. One huge caveat is that many other models might have matched the data just as well.

Researchers from Peking University in Beijing and the University of California, San Francisco, have devised a satisfying way of dealing with this problem (W. Ma et al. Mol. Syst. Biol. 2, 70; 2006).

Their starting point was epithelial patterning in the fruitfly Drosophila. During embryogenesis, a system known as the 'segment polarity network' generates repeating stripes of gene expression. The stripes are initially fuzzy and later become sharp. Ma et al. set out to see what simple gene circuits were best suited to this sharpening process.

They formulated differential-equation models for about 14 million ways of connecting two or three segmentation genes, then randomly chose 100 sets of parameters that defined the strength of the interactions for each gene. They then carried out computations for each combination to determine which of them converted fuzzy stripes into sharp ones.

Many topologies worked for at least one parameter set. But only a fraction worked for more than one or two. Interestingly, the most robust topologies were all variations on the same design — each had three sub-circuits, one 'stripe generator' motif and two bistable 'response sharpeners'. These findings give hope that complex networks may be decomposed into modular sub-circuits with understandable functions.

Comprehensively examining millions of models is a lot of work, but is not impossible. And, as Ma et al. show, it can yield important insight that could not have been derived from studies of one or two.

Posted by Brendan Maher at 07:54 PM | Permalink | Comments (1) | TrackBacks (0)