A comparison of the genomes of 50 Tibetans and 40 Han Chinese shows that ethnic Tibetans split off from the Han less than 3,000 years ago and since then rapidly evolved a unique ability to thrive at high altitudes and low oxygen levels.
A common misperception is that evolutionary changes require tens or hundreds of thousands of years. Not so. Given strong enough selective pressures mutations can spread very rapidly. Humans have developed many adaptations to adjust them to local conditions. Altitude is just one of the environmental challenges which humans have evolved to handle in local regions. Among the many local adaptations: lactase enzyme upregulation among pastoral populations that derived a substantial fraction of their calories from animal milk. Differences in alcohol tolerance in northern and southern European peoples also probably comes as a result of selective pressures in areas where more alcohol was produced.
Many genes were involved in the evolution of Tibetan adaptation to high altitude living.
The genome-wide comparison, performed by evolutionary biologists at the University of California, Berkeley, uncovered more than 30 genes with DNA mutations that have become more prevalent in Tibetans than Han Chinese, nearly half of which are related to how the body uses oxygen. One mutation in particular spread from fewer than 10 percent of the Han Chinese to nearly 90 percent of all Tibetans.
Evolution happens due to different death rates between carriers of different genetic variations. Lots of people died early while others died later (after reproducing) to adapt Tibetans to their ecological niche.
"This is the fastest genetic change ever observed in humans," said Rasmus Nielsen, UC Berkeley professor of integrative biology, who led the statistical analysis. "For such a very strong change, a lot of people would have had to die simply due to the fact that they had the wrong version of a gene."
The fastest change observed in human evolution took place in 3000 miles? See this book for an example of a human evolutionary adaptation that took place in several hundred years.
The continued decline in DNA sequencing costs is set to unleash a huge flood of discoveries about human genetic adaptations which developed in ecological niches around the globe. I expect that every organ in the body has genetic variations that have adapted humans to different diets, weather, diseases, elevations, temperature ranges, local prey, and other local conditions.
Also see my previous post Tibetans Genetically Adapted To High Altitude.
Some scientists at Arizona State University in Tempe are experimenting around with faster ways to find better gene and protein designs.
Nature, through the trial and error of evolution, has discovered a vast diversity of life from what can only presumed to have been a primordial pool of building blocks. Inspired by this success, a new Biodesign Institute research team, led by John Chaput, is now trying to mimic the process of Darwinian evolution in the laboratory by evolving new proteins from scratch. Using new tricks of molecular biology, Chaput and co-workers have evolved several new proteins in a fraction of the 3 billion years it took nature.
Their most recent results, published in the May 23rd edition of the journal PLoS ONE, have led to some surprisingly new lessons on how to optimize proteins which have never existed in nature before, in a process they call ‘synthetic evolution.’
I expect technological methods for creating new variations will some day allow scientists and bioengineers to search large solution spaces to come up with protein design improvements that will allow us to introduce huge improvements in human body designs. We'll not just take out the harmful mutations that we carry. We'll also replace beneficial mutations with even more beneficial mutations.
Natural selection is slow at selecting better designs because species have to go through many generations of reproduction in order to select for small improvements. Faster ways to find better designs will come in the future with testing of large numbers of gene and protein changes in microfluidic devices.
The scientists generated random mutations and tested them for the desired qualities.
Chaput’s group decided to speed up protein evolution once again by randomly mutating the parental sequence with a selection specially designed to improve protein stability. The team upped the ante and added increasing amounts of a salt, guanidine hydrochloride, making it harder for the protein fragment to bind its target (only the top 10 percent of strongest ATP binders remained). After subjecting the protein fragments to several rounds of this selective environmental pressure, only the ‘survival of the fittest’ ATP binding protein fragments remained.
The remaining fragments were identified and amino acid sequences compared with one another. Surprisingly, Chaput had bested nature’s designs, as the test tube derived protein was not only stable, but could bind ATP twice as tight as anything nature had come up with before.
To understand how this information is encoded in a protein sequence, Chaput and colleagues solved the 3-D crystal structures for their evolutionary optimized protein, termed DX, and the parent sequence.
In a surprising result, just two amino acids changes in the protein sequence were found to enhance the binding, solubility and heat stability. "We were shocked, because when we compared the crystal structures of the parent sequence to the DX sequence, we didn’t see any significant changes," said Chaput. "Yet no one could have predicted that these two amino acids changes would improve the function of the DX protein compared to the parent.
My guess is that the search for useful variations could be accelerated by introducing mutations that are not random. Rules could be developed based on a growing body of knowledge about amino acid sequences and protein functionality in order to come up with genetic variations that are more likely to improve a protein for some purpose. Also, as computers become more powerful we'll reach a point where testing of variations will become possible in computer models rather than by creating real proteins.
We are going to optimize our genomes just as computer programmers optimize and improve computer code. New types of flaws will get introduced in the process. But many more existing flaws will get fixed.
You can read the full paper for free: Structural Insights into the Evolution of a Non-Biological Protein: Importance of Surface Residues in Protein Fold Optimization.