Arrival of the Fittest: Solving Evolution's Greatest Puzzle (12 page)

Bacteria can acquire new genes in other ways too. Some absorb DNA from other cells after they die, rupture, and spill their molecular innards. As boneheaded as a person who would rather eat books—surely a great source of fiber—than read them, they use some of this DNA as food. Occasionally, though, the eaten DNA becomes hitched to their genome and helps make new proteins.
¹⁷

Gene transfer can also take advantage of viruses, those tiny lifeless particles whose DNA can enslave cells many times their size.
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While viruses reprogram a cell into a helpless factory that clones its viral masters, small pieces of the cell’s DNA can fuse with the viral genome. These pieces piggyback on newly minted viruses that leave the cell, and get injected along with the viral genome into the next hapless victim. In this scenario, one of our basketball-playing friends might simply have to sneeze on the other, whereupon his talent could be transferred to his teammate’s newly improved genome.

If all this horizontal gene transfer went on unchecked, the size of a genome would constantly increase over time and become grotesquely bloated. But excessively long DNA strings break more easily, and copying them wastes energy and materials—a mortal sin that nature won’t tolerate.
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Fortunately, such bloating does not occur, because gene transfer is balanced by gene deletions. These are the by-products of errors that happen when cells cut or splice DNA molecules as they repair and copy their DNA. Unlike DNA mutations that alter one letter at a time, deletions can strike out thousands of letters and many genes. As long as a deletion affects no essential genes, the cell can live with it. Such survivable deletions occur all the time. They ensure that only useful genes stay around in the long run, and they keep genomes lean.

In another difference from sex as we know it, gene transfer occurs not just between similar organisms but also between baker’s yeast and fruit flies, between microbes and plants, and especially among bacteria, which can be as different from one another as humans are from oak trees.
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This is why gene transfer is so powerful, and the most important reason why bacteria are masters of metabolic innovation. Very different organisms harbor very different metabolic texts, and gene transfer can edit one text with borrowed passages that are very different yet meaningful within another text, the microbial equivalent of a musical mash-up that combines a Baroque instrumental track with a pop vocal. Only some edits will improve a text, because the recipient cannot pick and choose which new genes it gets—they are a random subset of the donor’s genome. But because gene transfer is incredibly frequent, the odds for innovation aren’t bad: Even though many edits lack luster, the shelves of life’s universal library contain a virtually infinite number of masterpieces waiting to be found.

An example of nature’s editorial prowess is our friend
E. coli
and its multiple varieties,
E. coli
strains that were long thought to be like closely related ethnic groups.
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At the beginning of the twenty-first century, biologists first deciphered the genomes of many such strains, expecting them to be very similar. False. Two
E. coli
strains can differ in more than one million letters, or one-quarter of their DNA, such that one strain can harbor a thousand genes that the other strain lacks.
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Every million years—a blip of evolutionary time, 20 percent of the time since humans diverged from chimpanzees—an
E. coli
genome acquires some sixty new genes, all of them through horizontal gene transfer.
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And these are only the successful edits—many others have gone out of print and left no descendants.

We already know the DNA sequences of more than a thousand bacterial species, and they testify that
E. coli
is not an exception, but the rule.
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Most bacterial genomes are just as packed with genes trafficked from other sources, many with an unknown origin, though this is scarcely surprising. Trying to discover the provenance of a particular gene is a bit like trying to trace the literary influences revealed in a single paragraph of a novel by reading a small and random selection of the Library of Congress. A thousand species—or even a hundred thousand—would still be a drop in the ocean of bacterial diversity with countless millions of bacterial species, most of them unknown, all of them potential gene donors.

Not all of this genomic change causes metabolic change, because only a third of a genome is devoted to metabolism—the proteins it encodes also have a lot of other business, helping a cell move around, transporting building materials, and so on.
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So what if gene transfer mostly shuffles the nonmetabolic parts of the genome? Then evolution’s journey through the metabolic library might not take it far, and most metabolisms would therefore be very similar.

Are they? A few years ago, I asked this question of hundreds of bacterial species with known genomic DNA sequences, in a study that relied on decades of research done before my time. This research had discovered thousands of genes that code for particular enzymes, and allowed us to draw maps that connect genes with enzymes, and enzymes with chemical reactions.
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In other words, we could translate a genome sequence into a metabolic genotype, and compare these genotypes among organisms.
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And that is what I did.

Figure 5 shows how easy it is to compare two metabolic texts, using the simple example of two short snippets corresponding to ten enzymes in two organisms. Four of the ten enzymes cannot be made by either organism (gray zeroes), six are encoded by the first organism—its genotype string has six ones—and five are encoded by the second organism. We take the number of enzymes (six) made by at least one of the two organisms, and the number of enzymes made by only one but not the other organism (one enzyme), and calculate the ratio (1/6) of these numbers. If this ratio were zero, then two organisms would encode exactly the same enzymes. If it were 1/2, then half of the enzymes that one organism can produce would also be produced by the other organism. If the ratio were equal to 1, then the first organism wouldn’t be able to produce a single enzyme that the second organism can produce—their metabolisms would be maximally different. Those ratios, ranging from 0 to 1, reflect the difference between the enzymatic portfolios of two organisms, but that’s a little unwieldy to write over and over again—better to replace it with the symbol
D
for
difference
or
distance
.
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Unspeakably tedious as comparing genotypes for hundreds of bacteria—each encoding more than a thousand reactions—would be on pencil and paper, my trusted computer can finish it in the blink of an eye. When I asked it to calculate
D
for hundreds of pairs of bacteria, I was surprised to see—although their highly diverse genomes should have warned me—that even closely related organisms had highly diverse metabolic texts. Thirteen different strains of
E. coli
differed in more than 20 percent of their enzymes.
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An average pair of microbes differed in more than half of them.
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I had also suspected that bacteria living in the same environment—the soil, for example, or the ocean—might encounter similar nutrients and thus have similar metabolic texts. Wrong. Their metabolic texts were just as diverse, with a
D
just as different as that from bacteria living in different environments.

This exercise underscores the staggering scale at which nature experiments through gene shuffling. Everywhere on this planet, a relentless shuffling and mixing and recombining of genes takes place. Wherever microbial life occurs, in the depth of the oceans and on arid mountaintops, in scalding hot springs and on frigid glaciers, in fertile soils and desiccated deserts, inside and around our bodies, life is experimenting with every conceivable combination of new genes, rereading, editing, and rejuggling its metabolic texts without pause, yielding an enormous and still growing diversity of metabolisms.

Without readers, a book is a bundle of cellulose sheets with meaningless ink stains. Likewise, a text in the metabolic library needs to be read to reveal its meaning: the metabolic phenotype that determines which fuels an organism can use, and which molecules it can manufacture. We think of a phenotype as something we can see, and many metabolic phenotypes are plain as daylight. They include the melanins that protect our skin against radiation, that camouflage a lion’s fur, and that color the ink of an octopus. All of them are molecules synthesized by metabolism. And so are the various pigments that dye tree leaves, lobsters, flowers, and chameleons, whether for defense, courtship, or sometimes for no good reason at all.
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But metabolic phenotypes do not end at this visible surface. They extend to depths that are hidden from our eyes yet visible to chemical instruments—and to natural selection. Their most important role is to ensure viability itself, which boils down to the ability to synthesize sixty-odd molecules very different from those pretty pigments—they are the essential biomass molecules I mentioned in chapter 2. Viability, viewed as the phenotypic meaning of a genotypic text, is like the simple moral of a complex story, or like a brutally straightforward court judgment: If you can’t make all essential biomass molecules, your sentence is death, and it is carried out immediately. Organisms with a mutation that has compromised the ability to synthesize essential molecules don’t just fail to live long enough to reproduce. They don’t live at all.

To grasp this phenotypic meaning—viability or death—we need to read an organism’s metabolic genotype. This is a tall order, not only because the meaning of a text is so much more complex than the text itself—to understand the moral, we have to understand the whole story—but also because our brains are not well practiced in reading chemical language. Fortunately, we can program the artificial intelligence of computers to assist us.

A genotype tells us which reactions a metabolism can catalyze, the molecules these reactions consume, and the molecules they produce. To decipher its meaning, we would first need to know which nutrients are available—without the right ingredients, you cannot bake a cake—and whether the metabolism can use them to build an essential biomass molecule such as tryptophan. This is easiest for the austere minimal environments where survivalists like
E. coli
can thrive, because they contain so few nutrients, sometimes only a single sugar that provides all the carbon and energy the organism needs.

Starting from the available nutrients, we would then write a list of all the molecules the metabolism’s reactions
produce
from the available nutrients, find the reactions in the genotype that
consume
these product molecules, and list
their
products, iterating in this way until we find one or more reactions whose products include tryptophan. If no such reaction exists, then the metabolism cannot produce tryptophan.