ScienceWeek

PHYSIOLOGY: GENETIC FACTORS IN METABOLIC RATES

The following points are made by Robert W. Furness (Nature 2003 425:779):

1) Reptiles tend to live in warmer parts of the world because their low metabolic rate hinders life in cold climates. Birds and mammals, on the other hand, have high metabolic rates, which allow them to exploit colder climates by maintaining their body temperatures far above ambient conditions. But many species of bird and mammal are found in both cold and warm climates and metabolism can vary between the different populations. Are these variations driven by physiological adaptations to the local climate, or are they hard-wired into the animals' genetic make-up?

2) The gross differences in metabolic rate between cold-blooded reptiles and warm-blooded birds and mammals are well known, but the patterns of variation between closely related species, or indeed within individual species, are less well understood. Wikelski et al(1) have examined the variation in the metabolic rates of different populations of a single species of bird. The authors made an outstanding choice of study species. They needed a species that could be reared in captivity easily, and that existed as several genetically distinct populations, inhabiting a wide range of climates. They chose to work with stonechats (Saxicola torquata), a species of bird with several widely distributed "races". They show that stonechats from a tropical climate with little seasonal variation (Kenya) have a consistently low metabolic rate, whereas birds from a cooler but still fairly mild climate (Ireland) have a somewhat higher metabolic rate. And birds from colder climates (Austria and Kazakstan) have by far the highest metabolic rates.

3) It was demonstrated several years ago that metabolic rate increases with latitude(2,3) (a rough proxy for cold climate), but the crucial aspect of the Wikelski et al(1) study is that from a very early age all the birds were reared in captivity under the same conditions. So the variations between birds from the different populations could not be due to physiological adaptations to their respective local climates. Instead, the authors suggest that the differences in metabolic rate are specified by their genes.

4) What selective pressures shape the genetic make-up of different stonechat populations? Stonechats living in mild climates are year-round residents, whereas in locations with harsh winter climates, the birds are long-distance migrants. So the higher metabolic rate of stonechats from the Austrian and Kazakstan populations could be a consequence of selective pressures associated with migration. Wikelski et al(1). suggest that this is not the case because, energetically, migration is a cheap option compared with thermoregulation. The high metabolic rates of the Austrian and Kazakstan populations are more likely to be selected for by occasional periods of freezing temperatures during the breeding season.

References (abridged):

1. Wikelski, M. et al. Proc. R. Soc. Lond. B doi:10.1098/rspb.2003.2500 (2003)

2. Klaassen, M. Oecologia 104, 424-432 (1995)

3. Bryant, D. M. & Furness, R. W. Ibis 137, 219-226 (1995)

4. Kirkwood, T. B. Nature 270, 301-304 (1977)

5. Bech, C., Langseth, I. & Gabrielsen, G. W. Proc. R. Soc. Lond. B 266, 2161-2167 (1999)

Nature http://www.nature.com/nature

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ON ORGANIZATION AND MODULARITY IN METABOLIC NETWORKS

The following points are made by E. Ravasz et al (Science 2002 297:1551):

1) The identification and characterization of system-level features of biological organization is a key issue of post-genomic biology (1-3). The concept of modularity assumes that cellular functionality can be seamlessly partitioned into a collection of modules. Each module is a discrete entity of several elementary components and performs an identifiable task, separable from the functions of other modules (1,4,5). Spatially and chemically isolated molecular machines or protein complexes (such as ribosomes and flagella) are prominent examples of such functional units, but more extended modules, such as those achieving their isolation through the initial binding of a signaling molecule, are also apparent.

2) Simultaneously, it is recognized that the thousands of components of a living cell are dynamically interconnected, so that the cell's functional properties are ultimately encoded into a complex intracellular web of molecular interactions (2). This is perhaps most evident with cellular metabolism, a fully connected biochemical network in which hundreds of metabolic substrates are densely integrated through biochemical reactions. Within this network, however, modular organization (i.e., clear boundaries between subnetworks) is not immediately apparent. Indeed, recent studies have demonstrated that the probability that a substrate can react with (k) other substrates [the degree distribution P(k) of a metabolic network] decays as a power law in all organisms, suggesting that metabolic networks have a scale-free topology. A distinguishing feature of such scale-free networks is the existence of a few highly connected nodes (e.g., pyruvate or coenzyme A), which participate in a very large number of metabolic reactions. With a large number of links, these hubs integrate all substrates into a single, integrated web in which the existence of fully separated modules is prohibited by definition.

3) In summary: Spatially or chemically isolated functional modules composed of several cellular components and carrying discrete functions are considered fundamental building blocks of cellular organization, but their presence in highly integrated biochemical networks lacks quantitative support. The authors demonstrate that the metabolic networks of 43 distinct organisms are organized into many small, highly connected topologic modules that combine in a hierarchical manner into larger, less cohesive units, with their number and degree of clustering following a power law. Within Escherichia coli, the uncovered hierarchical modularity closely overlaps with known metabolic functions. The identified network architecture may be generic to system-level cellular organization.

References (abridged):

1. L. H. Hartwell, J. J. Hopfield, S. Leibler, A. W. Murray, Nature 402, C47 (1999)

2. H. Kitano, Science 295, 1662 (2002)

3. Y. I. Wolf, G. Karev, E. V. Koonin, Bioessays 24, 105 (2002)

4. D. A. Lauffenburger, Proc. Natl. Acad. Sci. U.S.A. 97, 5031 (2000)

5. C. V. Rao and A. P. Arkin, Annu. Rev. Biomed. Eng. 3, 391 (2001)

Science http://www.sciencemag.org

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