We can now add orangutans to the list of species whose genomes have been sequenced. ScienceDaily:
"The average orangutan is more diverse -- genetically speaking -- than the average human," says lead author Devin Locke, PhD, an evolutionary geneticist at Washington University's Genome Center. "We found deep diversity in both Bornean and Sumatran orangutans, but it's unclear whether this level of diversity can be maintained in light of continued widespread deforestation."
It appears that the orangutan genome is more stable than its cousins’.
"In terms of evolution, the orangutan genome is quite special among great apes in that it has been extraordinarily stable over the past 15 million years," says senior author Richard K. Wilson, PhD, director of Washington University's Genome Center, which led the project. "This compares with chimpanzees and humans, both of which have experienced large-scale structural rearrangements of their genome that may have accelerated their evolution."
This means that orangutans are probably closer to our common great ape ancestor than other species. The Nature article “Comparative and demographic analysis of orang-utan genomes” can be found here.
A press release from University of Veterinary Medicine – Vienna:
Comparing the new sequence with that of the human genome and other mammalian genomes thus provides unique insights into the evolution of man. [Dr Carolin] Kosiol has examined a total of nearly 14,000 human genes that are also found in the orang-utan, chimpanzee, macaque and dog genomes. She was able to show that genes involved in two processes have been particularly subject to natural selection in primate evolution: visual perception and the metabolism of glycolipids. Intriguingly, defects in glycolipid metabolism are known to be associated with a number of neurodegenerative diseases in humans. "Changes in lipid metabolism may have played a big part in neurological evolution in primates, as well as being involved in the diversity of diets and life history strategies," Kosiol believes. "Apes, especially orang-utans, have slower rates of reproduction and much lower energy usage than other mammals. It would be very valuable to sequence more primate genomes to enable more comparative analysis of this kind and thus help us understand the evolution of primates and our own species."
I want to put the above in context with an article from Nature Physics, “Impact of single links in competitive percolation.” As I have said many times, the fact that genes work in networks and that animals are examples of complex systems means that small changes can have major consequences. For example, predicting drug and disease response across species lines is going to problematic. The Nature Physics article discusses the impact of adding just one more connection to a network. ScienceDaily:
A single new connection can dramatically enhance the size of a network -- no matter whether this connection represents an additional link in the Internet, a new acquaintance within a circle of friends or a connection between two nerve cells in the brain.
Or if the connection is between genes or proteins.
The scientists found that after a certain number of new links, a sudden growth spurt occurs: The size of the largest network within the system is enhanced dramatically. "With respect to the size of the system, this jump is more dramatic in small systems than in large ones," says Nagler. However even in systems that consist of a huge number of elements -- comparable for example to the number of neurons in the brain -- the size of the largest network can double.
Ritsert C. Jansen wrote in Nature Reviews Genetics:
The recent progress in genomics and genetics, particularly in the high-throughput measurement of DNA transcript and protein levels, has led to a surge of interest in the fields of Systems Biology and Complex Traits Biology to study complex human diseases and disorders. Although such optimism is justified by the success that has been achieved in identifying the genetic basis of monogenic diseases and disorders, such as cystic fibrosis and Huntington disease, unravelling how genes “talk to each other” is likely to be an intimidating effort for more complex traits and processes. This is also true when analysing the genetic actions/interactions that occur in experimental systems: even organisms that have 10 [raised to the power of] 3 – 10 [raised to the power of] 4 genes, such as the budding yeast Saccharomyces cerevisiae, can produce more than 10 [raised to the power of] 4 – 10 [raised to the power of] 5 gene products, which can easily account for more than 10 [raised to the power of] 5 – 10 [raised to the power of] 7 interactions.1
(Sorry about the funny formatting of exponents. I could not make the exponents look like exponents so had to write it out that way.)
Humans have around 24,000 genes. With 24,000 genes, a pairwise matrix would yield 576 million combinations. If we assume a 100-gene network with each gene switched off or on, then the number of states the network can be is 2 [raised to the power of] 100. As Brian Goodwin describes in How the Leopard Changed Its Spots 2, if the network proceeded through every state in some sequence and stayed in that state for one microsecond, it would take billions of billions of years to get through all the states. With 24,000 genes that can be expressed between 0 and 1.0, the number of states is at least 2 [raised to the power of] 24,000. This should give some idea of the complexity of the system and why small changes in it can have enormous implications.
Small changes on the genetic level (involving these networks and or the regulation thereof) can lead to very large differences between species. Indeed, that is what evolution is all about. The claim that humans and rodents (or humans and any animal) are the same animal at the biochemical level, just dressed up differently, simply isn’t true. (Even rodents are not the same animal dressed up differently. There are profound differences between strains.) Moreover, it is irrelevant to point to observed similarities in genetic makeup between species (e.g., 94% similarity in sequences), since the details of the differences are in the interactions between conserved genes, not in the genes themselves. (See Animal Models in Light of Evolution for more.)
Yes, animals have similarities with humans. All are composed of essentially the same elements; all are made up of cells; and all share conserved processes. This is interesting for its own sake (and was important in the early days of biology research) but it is not sufficient for using animals to predict human response to drugs and disease—the reason animals are used in biomedical research and testing (or at least the reason given to society).
Vivisection activists cannot refute the argument from evolution, complex systems, and gene networks and it is this argument that allows us to place the empirical evidence (for examples see 3-31) in context. Just as many failures to make a perpetual motion machine, when put into the context of the 2nd law of thermodynamics, allow us to conclude a perpetual motion machine is impossible, so too should a thorough understanding of the above stop the practice of using animals as predictive models for humans.
Considering all the money involved, I doubt it will.
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